Special Issue "Fault Detection and Diagnosis in Mechatronics Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (31 December 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Nicola Bosso

Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
Website | E-Mail
Interests: railway vehicles (design and simulation); wheel/rail contact; railway dynamics; vehicle monitoring and diagnostics; heavy vehicles; multibody codes; machine design; experimental mechanics

Special Issue Information

Dear Colleagues,

The mechatronic approach is, nowadays, applied to many industrial sectors, and allows the integration in multi-disciplinary, environment, different technologies with the purpose of enhancing the performance of a system. A mechatronic system typically includes sensors, data acquisition, actuators (that operate in synergy) driven by specific control algorithms to perform a desired function on a controlled device.

One of the most recent and promising application of mechatronics concerns its application to improve the safety of complex systems. With this aim, it can be applied to different sectors: Transportation systems, vehicles, wind turbines, industrial processes, manufacturing, food industry, automation, and many others.

This Special Issue aims to collect papers concerning recent advances and challenges in application of “Mechatronics on Fault Detection and Diagnosis”, articulated over a wide range of sectors.

Papers submitted to this Special Issue are expected to provide an original contribution, proposing new solutions, improvements to existing solutions, and new applications in emerging sectors. The paper can address the solution of specific problems in the sector of interest using algorithms, experimental tests, and numerical analysis. 

Prof. Dr. Nicola Bosso
Guest Editor

Manuscript Submission Information

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Keywords

  • Fault diagnosis
  • Fault detection
  • Fault tolerant control
  • Diagnostic algorithms
  • Intelligent fault diagnosis
  • System active monitoring
  • Real-time monitoring
  • Monitoring systems
  • Mechatronic systems

Published Papers (38 papers)

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Research

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Open AccessArticle Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors
Appl. Sci. 2019, 9(4), 616; https://doi.org/10.3390/app9040616
Received: 31 December 2018 / Revised: 27 January 2019 / Accepted: 8 February 2019 / Published: 13 February 2019
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Abstract
Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most [...] Read more.
Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation
Appl. Sci. 2019, 9(3), 613; https://doi.org/10.3390/app9030613
Received: 25 December 2018 / Revised: 29 January 2019 / Accepted: 8 February 2019 / Published: 12 February 2019
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Abstract
Real-time prediction of remaining useful life (RUL) is one of the most essential works in prognostics and health management (PHM) of the micro-switches. In this paper, a linear degradation model based on an inverse Kalman filter to imitate the stochastic deterioration process is [...] Read more.
Real-time prediction of remaining useful life (RUL) is one of the most essential works in prognostics and health management (PHM) of the micro-switches. In this paper, a linear degradation model based on an inverse Kalman filter to imitate the stochastic deterioration process is proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm are used to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solve the errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data, the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, the effectiveness of the proposed approach is validated on an experimental data relating to micro-switches for the rail vehicle. Additionally, it proposes another two methods for comparison to illustrate the effectiveness of the method with an inverse Kalman filter in this paper. In conclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors in RUL estimation of the micro-switches excellently. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Fault Prediction Model of High-Power Switching Device in Urban Railway Traction Converter with Bi-Directional Fatigue Data and Weighted LSM
Appl. Sci. 2019, 9(3), 444; https://doi.org/10.3390/app9030444
Received: 23 December 2018 / Revised: 23 January 2019 / Accepted: 23 January 2019 / Published: 28 January 2019
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Abstract
The switching device is relatively weakest in the traction converter, and this paper aims at the fault prediction of it. Firstly, the mathematical distribution is analyzed based on the results that were obtained in electro thermal simulation and a single-directional accelerated fatigue test. [...] Read more.
The switching device is relatively weakest in the traction converter, and this paper aims at the fault prediction of it. Firstly, the mathematical distribution is analyzed based on the results that were obtained in electro thermal simulation and a single-directional accelerated fatigue test. Then, the accelerated fatigue test with bi-directional fatigue current is proposed, the data from which reflects the accelerating effect from FWD on the device aging process. The analytical model of fatigue process is fitted with the data that were obtained in the test. In order to shorten the test time consumption, we propose a weighted least squares method (LSM) to fit the failure data. Finally, the prediction model is presented with the consideration of fatigue signature and Arrhenius temperature factor. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Latent Leakage Fault Identification and Diagnosis Based on Multi-Source Information Fusion Method for Key Pneumatic Units in Chinese Standard Electric Multiple Units (EMU) Braking System
Appl. Sci. 2019, 9(2), 300; https://doi.org/10.3390/app9020300
Received: 23 December 2018 / Revised: 8 January 2019 / Accepted: 9 January 2019 / Published: 15 January 2019
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Abstract
To identify and diagnose the latent leakage faults of key pneumatic units in the Chinese standard Electric Multiple Units (EMU) braking system, a multi-source information fusion method based on Kalman filtering, sequential probability ratio test (SPRT), and support vector machine (SVM) is proposed. [...] Read more.
To identify and diagnose the latent leakage faults of key pneumatic units in the Chinese standard Electric Multiple Units (EMU) braking system, a multi-source information fusion method based on Kalman filtering, sequential probability ratio test (SPRT), and support vector machine (SVM) is proposed. The relay valve is taken as an example for research. Firstly, Kalman’s state estimation function is used to obtain the innovation sequence, and the innovation sequence is input into the SPRT model to help recognize latent leakage faults of the relay valve. Using this method, the problem of the incomplete training set of the traditional SPRT method due to the change of the braking level and the vehicle load is solved. Secondly, the eight time-domain parameters of the relay valve input and the output pressure signal are extracted as fault characteristics, and then input to the support vector machine to realize the internal and external leakage fault diagnosis of the relay valve, which provides a reference for maintenance. Finally, this method is verified by the fault simulation data by quickly identifying latent leakage faults and diagnosing the internal and external leakage at a fault recognition rate of 100% by SVM under small sample conditions. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM
Appl. Sci. 2019, 9(2), 224; https://doi.org/10.3390/app9020224
Received: 15 December 2018 / Revised: 3 January 2019 / Accepted: 3 January 2019 / Published: 9 January 2019
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Abstract
Permanent magnet synchronous motors (PMSM) has the advantages of simple structure, small size, high efficiency, and high power factor, and a key dynamic source and is widely used in industry, equipment and electric vehicle. Aiming at its inter-turn short-circuit fault, this paper proposes [...] Read more.
Permanent magnet synchronous motors (PMSM) has the advantages of simple structure, small size, high efficiency, and high power factor, and a key dynamic source and is widely used in industry, equipment and electric vehicle. Aiming at its inter-turn short-circuit fault, this paper proposes a fault diagnosis method based on sparse representation and support vector machine (SVM). Firstly, the sparse representation is used to extract the first and second largest sparse coefficients of both current signal and vibration signals, and then they are composed into four-dimensional feature vectors. Secondly, the feature vectors are input into the support vector machine for fault diagnosis, which is suitable for small sample. Experiments on a permanent magnet synchronous motor with artificially set inter-turn short-circuit fault and a normal one showed that the method is feasible and accurate. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Leakage Detection of a Spherical Water Storage Tank in a Chemical Industry Using Acoustic Emissions
Appl. Sci. 2019, 9(1), 196; https://doi.org/10.3390/app9010196
Received: 22 November 2018 / Revised: 25 December 2018 / Accepted: 3 January 2019 / Published: 8 January 2019
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Abstract
Spherical storage tanks are used in various industries to store substances like gasoline, oxygen, waste water, and liquefied petroleum gas (LPG). Cracks in the storage tanks are unaccepted defects, as storage tanks can leak or spill the contained substance through these cracks. Leakage [...] Read more.
Spherical storage tanks are used in various industries to store substances like gasoline, oxygen, waste water, and liquefied petroleum gas (LPG). Cracks in the storage tanks are unaccepted defects, as storage tanks can leak or spill the contained substance through these cracks. Leakage from contained hazardous substances storage tanks can contaminate the environment and may lead to fatal accidents. Therefore, the ability to detect cracks from spherical storage tanks is necessary to avoid damage to the environment and to ensure public safety. In this paper, we present a crack detection case study of a spherical tank. The detection was performed using time-domain statistical features and a machine learning algorithm. The proposed method consists of (1) extraction of statistical features from the acoustic emissions (AE) acquired from the spherical tank, and (2) classification of the nonlinear data using a support vector machine (SVM). We evaluate the proposed algorithm with AE data obtained from the spherical tank, demonstrating that the algorithm effectively discriminates between normal and crack conditions. These results show that the proposed algorithm is effective for detecting cracks in spherical storage tanks. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle An Improved Real-Time Contrasts Control Chart Using Novelty Detection and Variable Importance
Appl. Sci. 2019, 9(1), 173; https://doi.org/10.3390/app9010173
Received: 22 November 2018 / Revised: 26 December 2018 / Accepted: 29 December 2018 / Published: 5 January 2019
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Abstract
Fault detection and isolation are important tasks in statistical process control. A real-time contrasts (RTC) control chart converts the statistical process-monitoring problem to the real-time classification problem, thus outperforming traditional monitoring techniques. An RTC assigns a class to reference data and the other [...] Read more.
Fault detection and isolation are important tasks in statistical process control. A real-time contrasts (RTC) control chart converts the statistical process-monitoring problem to the real-time classification problem, thus outperforming traditional monitoring techniques. An RTC assigns a class to reference data and the other class to a window of real-time contrasts. However, RTC control charts often fail to detect abnormal states when both normal and abnormal data exist together in the window. To enable more rapid detection of an improved RTC control chart, this paper proposes a multivariate process monitoring system with an improved RTC control chart. Although previous RTC control charts proposed by other studies outperform the original RTC chart, it is still difficult to detect an abnormal state when normal and abnormal data exist together. To overcome this problem, this paper proposes an RTC control chart using novelty detection and variable importance with random forests. Novelty detection and variable importance were used so that fault can be detected when the control limit could not be exceeded despite the abnormal state. The proposed method extracts representative data in the sliding window and adds the extracted data to the window to quickly detect the abnormal state. Experiments demonstrate the proposed method to outperform the original RTC chart. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle A Feasibility Study of the Drive-By Method for Damage Detection in Railway Bridges
Appl. Sci. 2019, 9(1), 160; https://doi.org/10.3390/app9010160
Received: 1 November 2018 / Revised: 11 December 2018 / Accepted: 20 December 2018 / Published: 4 January 2019
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Abstract
Damage identification and localization in railway bridges is a widely studied topic. Strain, displacement, or acceleration sensors installed on the bridge structure are normally used to detect changes in the global behavior of the structure, whereas approaches like ultra-sonic testing, acoustic emission, and [...] Read more.
Damage identification and localization in railway bridges is a widely studied topic. Strain, displacement, or acceleration sensors installed on the bridge structure are normally used to detect changes in the global behavior of the structure, whereas approaches like ultra-sonic testing, acoustic emission, and magnetic inspection are used to check a small portion of structure near localized damage. The aim of this paper is to explore another perspective for monitoring the structural status of railway bridges, i.e., to detect structural damage from the dynamic response of the train transiting the bridge. This approach can successfully be implemented in the case of resonant bridges, thanks to the high level of acceleration generated, but its application becomes more challenging when the excitation frequencies due to train passage do not excite the first mode of vibration of the bridge. The paper investigates the feasibility of the method in the latter case, through numerical simulations of the complete train-track-bridge system. Accelerations on axleboxes and bogies are processed through suitable algorithms to detect differences arising when the train crosses a defective bridge or a healthy one. The results outline the main operational parameters affecting the method, the best placement for sensors, and the best frequency range to be considered in the signal processing, also addressing the issues that are related to track irregularity. Good performance can be achieved in the case of short bridges, but a few practical issues must be tackled before the method could be tested in practice. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Geometrical Defect Detection in the Wire Electrical Discharge Machining of Fir-Tree Slots Using Deep Learning Techniques
Appl. Sci. 2019, 9(1), 90; https://doi.org/10.3390/app9010090
Received: 4 December 2018 / Revised: 21 December 2018 / Accepted: 23 December 2018 / Published: 27 December 2018
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Abstract
Traceability is a critical issue in the manufacturing of aerospace components. However, extracting understandable information from huge amounts of data from manufacturing processes may become a very difficult task. In this paper, a novel proposal for geometrical defect detection in the manufacturing of [...] Read more.
Traceability is a critical issue in the manufacturing of aerospace components. However, extracting understandable information from huge amounts of data from manufacturing processes may become a very difficult task. In this paper, a novel proposal for geometrical defect detection in the manufacturing of fir-tree slots for disk turbines using wire electrical discharge machining is presented. Useful data about the wire Electrical Discharge Machining (WEDM) process are collected every 5 ms and each single discharge is classified as a function of ignition delay time. Information from this large amount of data is extracted by using a deep neural network, which includes two hidden dense layers, each with 64 units and Relu activation, and it ends with a single unit with no activation. The average of the per-epoch absolute error (MAE) scores has been used to decide the optimum training situation for the deep learning network. Validation of the method has been carried out by machining a high-precision fir-tree slot for a disk turbine under industrial conditions. Results show that even though a strict tolerance band of ±5 µm has been applied, as many as 80% of the predictions from the network match the results of the conventional measuring method (coordinate measuring machine). Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring
Appl. Sci. 2018, 8(12), 2611; https://doi.org/10.3390/app8122611
Received: 11 November 2018 / Revised: 6 December 2018 / Accepted: 11 December 2018 / Published: 13 December 2018
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Abstract
This work reports a novel method by fusing Laplacian Eigenmaps feature conversion and deep neural network (DNN) for machine condition assessment. Laplacian Eigenmaps is adopted to transform data features from original high dimension space to projected lower dimensional space, the DNN is optimized [...] Read more.
This work reports a novel method by fusing Laplacian Eigenmaps feature conversion and deep neural network (DNN) for machine condition assessment. Laplacian Eigenmaps is adopted to transform data features from original high dimension space to projected lower dimensional space, the DNN is optimized by the particle swarm optimization algorithm, and the machine run-to-failure experiment were investigated for validation studies. Through a series of comparative experiments with the original features, two other effective space transformation techniques, Principal Component Analysis (PCA) and Isometric map (Isomap), and two other artificial intelligence methods, hidden Markov model (HMM) as well as back-propagation neural network (BPNN), the present method in this paper proved to be more effective for machine operation condition assessment. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
Appl. Sci. 2018, 8(12), 2416; https://doi.org/10.3390/app8122416
Received: 14 September 2018 / Revised: 7 November 2018 / Accepted: 10 November 2018 / Published: 28 November 2018
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Abstract
Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural [...] Read more.
Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Bearing Fault Diagnosis under Variable Rotational Speeds Using Stockwell Transform-Based Vibration Imaging and Transfer Learning
Appl. Sci. 2018, 8(12), 2357; https://doi.org/10.3390/app8122357
Received: 19 October 2018 / Revised: 8 November 2018 / Accepted: 19 November 2018 / Published: 22 November 2018
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Abstract
In this paper, discrete orthonormal Stockwell transform (DOST)-based vibration imaging is proposed as a preprocessing step for supporting load and rotational speed invariant scenarios for signals of various health conditions. For any health condition, features can easily be extracted from its generated health [...] Read more.
In this paper, discrete orthonormal Stockwell transform (DOST)-based vibration imaging is proposed as a preprocessing step for supporting load and rotational speed invariant scenarios for signals of various health conditions. For any health condition, features can easily be extracted from its generated health pattern. To automate the feature selection process, a convolutional neural network (CNN)-based transfer learning (TL) approach for diagnosis has also been introduced. Transfer learning allows an established model to use feature knowledge obtained under one set of working conditions through hidden layers to diagnose faults that occur under other working conditions. The network learns from the massive source dataset, and that knowledge is applied to the target data to identify faults. Using the bearing dataset of Case Western Reserve University, the proposed approach yields an average 99.8% classification accuracy and, specifically, 99.99% for healthy condition (HC), 99.95% for inner race fault (IRF), 99.96% for ball fault (BF), 99.68% for outer race fault for 12 o’clock sensor position ([email protected]), 99.93% for outer race fault for 3 o’clock sensor position ([email protected]), and 99.89% for outer race fault for 6 o’clock sensor position ([email protected]). In this paper, the proposed approach is compared with conventional artificial neural networks (ANNs), support vector machines (SVMs), hierarchical CNNs, and deep autoencoders. The proposed approach outperforms these conventional methods in the accuracy under all working conditions. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Effective Prediction of Bearing Fault Degradation under Different Crack Sizes Using a Deep Neural Network
Appl. Sci. 2018, 8(11), 2332; https://doi.org/10.3390/app8112332
Received: 24 October 2018 / Revised: 12 November 2018 / Accepted: 19 November 2018 / Published: 21 November 2018
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Abstract
Exact evaluation of the degradation levels in bearing defects is one of the most essential works in bearing condition monitoring. This paper proposed an efficient evaluation method using a deep neural network (DNN) for correct prediction of degradation levels of bearings under different [...] Read more.
Exact evaluation of the degradation levels in bearing defects is one of the most essential works in bearing condition monitoring. This paper proposed an efficient evaluation method using a deep neural network (DNN) for correct prediction of degradation levels of bearings under different crack size conditions. An envelope technique was first used to capture the characteristic fault frequencies from acoustic emission (AE) signals of bearing defects. Accordingly, a health-related indicator (HI) calculation was performed on the collected envelope power spectrum (EPS) signals using a Gaussian window method to estimate the fault severities of bearings that served as an appropriate dataset for DNN training. The proposed DNN was then trained for effective prediction of bearing degradation using the Adam optimization-based backpropagation algorithm, in which the synaptic weights were optimally initialized by the Xavier initialization method. The effectiveness of the proposed degradation prediction approach was evaluated through different crack size experiments (3, 6, and 12 mm) of bearing faults. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Fault Localization by Comparing Memory Updates between Unit and Integration Testing of Automotive Software in an Hardware-in-the-Loop Environment
Appl. Sci. 2018, 8(11), 2260; https://doi.org/10.3390/app8112260
Received: 21 September 2018 / Revised: 10 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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During the inspection stage, an integration test is performed on electronic automobile parts that have passed a unit test. The faults found during this test are reported to the developer, who subsequently modifies the source code. If the tester provides the developer with [...] Read more.
During the inspection stage, an integration test is performed on electronic automobile parts that have passed a unit test. The faults found during this test are reported to the developer, who subsequently modifies the source code. If the tester provides the developer with memory usage information (such as functional symbol or interface signal), which works differently from normal operation in failed Hardware-in-the-Loop (HiL) testing (even when the tester has no source code), that information will be useful for debugging. In this paper, we propose a fault localization method for automotive software in an HiL environment by comparing the analysis results of updated memory between units and integration tests. Analyzing the memory usage of a normally operates unit test, makes it possible to obtain memory-updated information necessary for the operation of that particular function. By comparing this information to the memory usage when a fault occurs during an integration test, erroneously operated symbols and stored values are presented as potential root causes of the fault. We applied the proposed method to HiL testing for an OSEK/VDX-based electronic control unit (ECU). As a result of testing using fault injection, we confirmed that the fault causes can be found by checking the localized memory symbols with an average of 5.77%. In addition, when applying this methodology to a failure that occurred during a body control module (BCM) (which provides seat belt warnings) test, we could identify a suspicious symbol and find the cause of the test failure with only 8.54% of localized memory symbols. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Aircraft Air Compressor Bearing Diagnosis Using Discriminant Analysis and Cooperative Genetic Algorithm and Neural Network Approaches
Appl. Sci. 2018, 8(11), 2243; https://doi.org/10.3390/app8112243
Received: 28 September 2018 / Revised: 24 October 2018 / Accepted: 7 November 2018 / Published: 14 November 2018
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Abstract
Monitoring and diagnosis of rotating machines has become an effective and indispensable tool for the efficient and timely detection of defects, avoiding then incidents that can have serious economic and human consequences. Bearings are the most sensitive parts of these machines; this is [...] Read more.
Monitoring and diagnosis of rotating machines has become an effective and indispensable tool for the efficient and timely detection of defects, avoiding then incidents that can have serious economic and human consequences. Bearings are the most sensitive parts of these machines; this is why special attention must be paid to its monitoring. This paper presents a methodology for diagnosing an aircraft air compressor bearing using neural networks that have been optimized by genetic algorithms. We used in our study a database of vibratory signals that were recorded on a test bench from bearings with different defects. The faults features are extracted from these noisy signals using the estimate of the spectral density. The diagnostic capacity of obtained model has been demonstrated by a comparative study with two other automatic classifiers, which are discriminant analysis and neural networks whose training has been done with the Back-Propagation algorithm. This approach has the advantage of simultaneously ensuring the optimal structure of the neural network and accomplishing its learning. The importance of this study is the construction of a diagnostic tool that is characterized by efficiency, speed of decision making and ease of implementation not only on the computers on the ground, but also on the mounted calculators on aircraft. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Adaptive Threshold Generation for Fault Detection with High Dependability for Cyber-Physical Systems
Appl. Sci. 2018, 8(11), 2235; https://doi.org/10.3390/app8112235
Received: 26 October 2018 / Revised: 9 November 2018 / Accepted: 10 November 2018 / Published: 13 November 2018
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Abstract
Cyber-physical systems (CPS) applied to safety-critical or mission-critical domains require high dependability including safety, security, and reliability. However, the safety of CPS can be significantly threatened by increased security vulnerabilities and the lack of flexibility in accepting various normal environments or conditions. To [...] Read more.
Cyber-physical systems (CPS) applied to safety-critical or mission-critical domains require high dependability including safety, security, and reliability. However, the safety of CPS can be significantly threatened by increased security vulnerabilities and the lack of flexibility in accepting various normal environments or conditions. To enhance safety and security in CPS, a common and cost-effective strategy is to employ the model-based detection technique; however, detecting faults in practice is challenging due to model and environment uncertainties. In this paper, we present a novel generation method of the adaptive threshold required for providing dependability for the model-based fault detection system. In particular, we focus on statistical and information theoretic analysis to consider the model and environment uncertainties, and non-linear programming to determine an adaptive threshold as an equilibrium point in terms of adaptability and sensitivity. To do this, we assess the normality of the data obtained from real sensors, define performance measures representing the system requirements, and formulate the optimal threshold problem. In addition, in order to efficiently exploit the adaptive thresholds, we design the storage so that it is added to the basic structure of the model-based detection system. By executing the performance evaluation with various fault scenarios by varying intensities, duration and types of faults injected, we prove that the proposed method is well designed to cope with uncertainties. In particular, against noise faults, the proposed method shows nearly 100% accuracy, recall, and precision at each of the operation, regardless of the intensity and duration of faults. Under the constant faults, it achieves the accuracy from 85.4% to 100%, the recall of 100% from the lowest 54.2%, and the precision of 100%. It also gives the accuracy of 100% from the lowest 83.2%, the recall of 100% from the lowest 43.8%, and the precision of 100% against random faults. These results indicate that the proposed method achieves a significantly better performance than existing dynamic threshold methods. Consequently, an extensive performance evaluation demonstrates that the proposed method is able to accurately and reliably detect the faults and achieve high levels of adaptability and sensitivity, compared with other dynamic thresholds. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle An AWS Machine Learning-Based Indirect Monitoring Method for Deburring in Aerospace Industries Towards Industry 4.0
Appl. Sci. 2018, 8(11), 2165; https://doi.org/10.3390/app8112165
Received: 12 October 2018 / Revised: 27 October 2018 / Accepted: 27 October 2018 / Published: 5 November 2018
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Abstract
The number of studies on the Internet of Things (IoT) has grown significantly in the past decade and has been applied in various fields. The IoT term sounds like it is specifically for computer science but it has actually been widely applied in [...] Read more.
The number of studies on the Internet of Things (IoT) has grown significantly in the past decade and has been applied in various fields. The IoT term sounds like it is specifically for computer science but it has actually been widely applied in the engineering field, especially in industrial applications, e.g., manufacturing processes. The number of published papers in the IoT has also increased significantly, addressing various applications. A particular application of the IoT in these industries has brought in a new term, the so-called Industrial IoT (IIoT). This paper concisely reviews the IoT from the perspective of industrial applications, in particular, the major pillars in order to build an IoT application, i.e., architectural and cloud computing. This enabled readers to understand the concept of the IIoT and to identify the starting point. A case study of the Amazon Web Services Machine Learning (AML) platform for the chamfer length prediction of deburring processes is presented. An experimental setup of the deburring process and steps that must be taken to apply AML practically are also presented. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
Appl. Sci. 2018, 8(11), 2143; https://doi.org/10.3390/app8112143
Received: 8 October 2018 / Revised: 26 October 2018 / Accepted: 30 October 2018 / Published: 2 November 2018
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Abstract
Effective feature selection can help improve the classification performance in bearing fault diagnosis. This paper proposes a novel feature selection method based on bearing fault diagnosis called Feature-to-Feature and Feature-to-Category- Maximum Information Coefficient (FF-FC-MIC), which considers the relevance among features and relevance between [...] Read more.
Effective feature selection can help improve the classification performance in bearing fault diagnosis. This paper proposes a novel feature selection method based on bearing fault diagnosis called Feature-to-Feature and Feature-to-Category- Maximum Information Coefficient (FF-FC-MIC), which considers the relevance among features and relevance between features and fault categories by exploiting the nonlinearity capturing capability of maximum information coefficient. In this method, a weak correlation feature subset obtained by a Feature-to-Feature-Maximum Information Coefficient (FF-MIC) matrix and a strong correlation feature subset obtained by a Feature-to-Category-Maximum Information Coefficient (FC-MIC) matrix are merged into a final diagnostic feature set by an intersection operation. To evaluate the proposed FF-FC-MIC method, vibration data collected from two bearing fault experiment platforms (CWRU dataset and CUT-2 dataset) were employed. Experimental results showed that accuracy of FF-FC-MIC can achieve 97.50%, and 98.75% on the CWRU dataset at the motor speeds of 1750 rpm, and 1772 rpm, respectively, and reach 91.75%, 94.69%, and 99.07% on CUT-2 dataset at the motor speeds of 2000 rpm, 2500 rpm, 3000 rpm, respectively. A significant improvement of FF-FC-MIC has been confirmed, since the p-values between FF-FC-MIC and the other methods are 1.166 × 10 3 , 2.509 × 10 5 , and 3.576 × 10 2 , respectively. Through comparison with other methods, FF-FC-MIC not only exceeds each of the baseline feature selection method in diagnosis accuracy, but also reduces the number of features. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Sparse Haar-Like Feature and Image Similarity-Based Detection Algorithm for Circular Hole of Engine Cylinder Head
Appl. Sci. 2018, 8(10), 2006; https://doi.org/10.3390/app8102006
Received: 18 September 2018 / Revised: 10 October 2018 / Accepted: 18 October 2018 / Published: 22 October 2018
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Abstract
If the circular holes of an engine cylinder head are distorted, cracked, defective, etc., the normal running of the equipment will be affected. For detecting these faults with high accuracy, this paper proposes a detection method based on feature point matching, which can [...] Read more.
If the circular holes of an engine cylinder head are distorted, cracked, defective, etc., the normal running of the equipment will be affected. For detecting these faults with high accuracy, this paper proposes a detection method based on feature point matching, which can reduce the detection error caused by distortion and light interference. First, the effective and robust feature vectors of pixels are extracted based on improved sparse Haar-like features. Then we calculate the similarity and find the most similar matching point from the image. In order to improve the robustness to the illumination, this paper uses the method based on image similarity to map the original image, so that the same region under different illumination conditions has similar spatial distribution. The experiments show that the algorithm not only has high matching accuracy, but also has good robustness to the illumination. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Dynamic Pressure Analysis of Hemispherical Shell Vibrating in Unbounded Compressible Fluid
Appl. Sci. 2018, 8(10), 1938; https://doi.org/10.3390/app8101938
Received: 17 September 2018 / Revised: 12 October 2018 / Accepted: 14 October 2018 / Published: 16 October 2018
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Abstract
This paper is the first to highlight the vibrations of a hemispherical shell structure interacting with both compressible and incompressible fluids. To precisely calculate the pressure of the shell vibrating in the air, a novel analytical approach has been established that has existed [...] Read more.
This paper is the first to highlight the vibrations of a hemispherical shell structure interacting with both compressible and incompressible fluids. To precisely calculate the pressure of the shell vibrating in the air, a novel analytical approach has been established that has existed in very few publications to date. An analytical formulation that calculates pressure was developed by integrating both the ‘small-density method’ and the ‘Bessel function method’. It was considered that the hemispherical shell vibrates as a simple harmonic function, and the fluid is non-viscous. For comparison, the incompressible fluid model has been analyzed. Surprisingly, it is the first to report that the pressure of the shell surface is proportional to the vibration acceleration, and the velocity amplitude decreased at the rate of 1 r 2 when the fluid was incompressible. Otherwise, the surface pressure of the hemispherical shell was proportional to the vibration velocity, and the velocity amplitude decreased with the rate of 1 r when the fluid was compressible. The compressibility of fluid played an important role in the dynamic pressure of the shell structure. Furthermore, the scale factor derived by the theoretical approach was the product of the density and the sound velocity of the fluid ( ρ o c ) exactly. In this study, the analytical solutions were verified by the calibrated numerical simulations, and the analytical formulation were rigorously tested by extensive parametric studies. These new findings can be used to guide the optimal design of the spherical shell structure subjected to wind load, seismic load, etc. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle A Fast Three-Dimensional Display Method for Time-Frequency Spectrogram Used in Embedded Fault Diagnosis Devices
Appl. Sci. 2018, 8(10), 1930; https://doi.org/10.3390/app8101930
Received: 29 September 2018 / Revised: 10 October 2018 / Accepted: 14 October 2018 / Published: 15 October 2018
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Abstract
Time-frequency analysis is usually used to reveal the appearance of different frequency components varying with time, in signals, of which time-frequency spectrogram is an important visual tool to display the information. The Mesh Surface Generation (MSG) algorithm is widely used in three-dimensional (3D) [...] Read more.
Time-frequency analysis is usually used to reveal the appearance of different frequency components varying with time, in signals, of which time-frequency spectrogram is an important visual tool to display the information. The Mesh Surface Generation (MSG) algorithm is widely used in three-dimensional (3D) modeling. Removing hidden lines from the mesh plot is an essential process that produces explicit depth information. In this paper, a fast and effective method has been proposed for a time-frequency Spectrogram Mesh Surface Generation (SMSG) display, especially, based on the painter’s algorithm. In addition, most portable fault diagnosis devices have little function to generate a 3D spectrogram, which generally needs a general computer to realize the complex time-frequency analysis algorithms and a 3D display. However, general computer is not portable and then not suitable for field test. Hence, the proposed SMSG algorithm is applied to an embedded fault diagnosis device, which is light, low-cost, and real-time. The experimental results show that this approach can realize a high degree of accuracy and save considerable time. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Prediction of Damage to the Vehicle Underbody due to Stone Chipping
Appl. Sci. 2018, 8(10), 1805; https://doi.org/10.3390/app8101805
Received: 31 August 2018 / Revised: 22 September 2018 / Accepted: 28 September 2018 / Published: 2 October 2018
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Abstract
In these days, there is a paradigm shift from internal-combustion-engine vehicles to electric vehicles. Most electric vehicles developed include batteries mounted at the bottom, near the rear wheels. Hence, the robust design of underbody parts against the impact of external bodies or random [...] Read more.
In these days, there is a paradigm shift from internal-combustion-engine vehicles to electric vehicles. Most electric vehicles developed include batteries mounted at the bottom, near the rear wheels. Hence, the robust design of underbody parts against the impact of external bodies or random stone chipping needs to be made. In this study, the mathematical modeling and statistical probability analysis of stone chipping and tire slip are performed for identifying and confirming the critical zones of the vehicle underbody that may be damaged by stone chipping. Thereby, stone chipping can be predicted by simulations using the employed mathematical model, before conducting experimental verification using the existing methods. Furthermore, the development cost and time can be reduced because the elements of the designed underbody can be analyzed for robustness, and the safety factor can be established during the design stage. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Failure Identification of Dump Truck Suspension Based on an Average Correlation Stochastic Subspace Identification Algorithm
Appl. Sci. 2018, 8(10), 1795; https://doi.org/10.3390/app8101795
Received: 15 August 2018 / Revised: 19 September 2018 / Accepted: 24 September 2018 / Published: 1 October 2018
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Abstract
This paper proposes a fault identification method based on an improved stochastic subspace modal identification algorithm to achieve high-performance fault identification of dump truck suspension. The sensitivity of modal parameters to suspension faults is evaluated, and a fault diagnosis method based on modal [...] Read more.
This paper proposes a fault identification method based on an improved stochastic subspace modal identification algorithm to achieve high-performance fault identification of dump truck suspension. The sensitivity of modal parameters to suspension faults is evaluated, and a fault diagnosis method based on modal energy difference is established. The feasibility of the proposed method is validated by numerical simulation and full-scale vehicle tests. The result shows that the proposed average correlation signal based stochastic subspace identification (ACS-SSI) method can identify the fluctuation of vehicle modal parameters effectively with respect to different spring stiffness and damping ratio conditions, and then fault identification of the suspension system can be realized by the variation of the modal energy difference (MED). Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Induction Motors Dynamic Eccentricity Fault Diagnosis Based on the Combined Use of WPD and EMD-Simulation Study
Appl. Sci. 2018, 8(10), 1709; https://doi.org/10.3390/app8101709
Received: 20 August 2018 / Revised: 13 September 2018 / Accepted: 14 September 2018 / Published: 20 September 2018
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Abstract
The frequency-domain analysis using the fast Fourier transform (FFT) for diagnosis of eccentricity fault has been widely used in squirrel-cage induction motor (IM). However, with the restriction of sampling frequency and time acquisition, FFT analysis could not provide ideal results under low levels [...] Read more.
The frequency-domain analysis using the fast Fourier transform (FFT) for diagnosis of eccentricity fault has been widely used in squirrel-cage induction motor (IM). However, with the restriction of sampling frequency and time acquisition, FFT analysis could not provide ideal results under low levels of dynamic eccentricity (DE). In this paper, a combined use of the wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) method is presented to diagnose the IM fault under low degrees of purely DE. The proposed method is based on the decomposition of apparent power signal and extracts the characteristic component. The fault severity factor (FSF) has been defined to evaluate the eccentricity severity. Simulation results using the finite element method (FEM) are tested to verify the effectiveness of the presented method under different load conditions. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal
Appl. Sci. 2018, 8(9), 1677; https://doi.org/10.3390/app8091677
Received: 7 September 2018 / Revised: 12 September 2018 / Accepted: 14 September 2018 / Published: 16 September 2018
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Abstract
The stator inter-turn short circuit fault is one of the most common and key faults in permanent magnet synchronous motor (PMSM). This paper introduces a time–frequency method for inter-turn fault detection in stator winding of PMSM using improved wavelet packet transform. Both stator [...] Read more.
The stator inter-turn short circuit fault is one of the most common and key faults in permanent magnet synchronous motor (PMSM). This paper introduces a time–frequency method for inter-turn fault detection in stator winding of PMSM using improved wavelet packet transform. Both stator current signal and vibration signal are used for the detection of short circuit faults. Two different experimental data from a three-phase PMSM were processed and analyzed by this time–frequency method in LabVIEW. The feasibility of this approach is shown by the experimental test. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis
Appl. Sci. 2018, 8(9), 1621; https://doi.org/10.3390/app8091621
Received: 14 August 2018 / Revised: 4 September 2018 / Accepted: 10 September 2018 / Published: 12 September 2018
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Abstract
Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation [...] Read more.
Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle
Appl. Sci. 2018, 8(9), 1594; https://doi.org/10.3390/app8091594
Received: 8 August 2018 / Revised: 22 August 2018 / Accepted: 6 September 2018 / Published: 9 September 2018
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Abstract
This paper proposes a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the self-diagnosis of an autonomous vehicle, which supports a rapid and accurate communication between in-vehicle sensors and a self-diagnosis module and between in-vehicle protocols. A paper on the self-diagnosis module has been published [...] Read more.
This paper proposes a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the self-diagnosis of an autonomous vehicle, which supports a rapid and accurate communication between in-vehicle sensors and a self-diagnosis module and between in-vehicle protocols. A paper on the self-diagnosis module has been published previously, thus this paper only covers the LI-VEG, not the self-diagnosis. The LI-VEG consists of an In-Vehicle Sending and Receiving Layer (InV-SRL), an InV-Management Layer (InV-ML) and an InV-Data Translator Layer (InV-DTL). First, the InV-SRL receives the messages from FlexRay, Control Area Network (CAN), Media Oriented Systems Transport (MOST), and Ethernet and transfers the received messages to the InV-ML. Second, the InV-ML manages the message transmission and reception of FlexRay, CAN, MOST, and Ethernet and an Address Mapping Table. Third, the InV-DTL decomposes the message of FlexRay, CAN, MOST, and Ethernet and recomposes the decomposed messages to the frame suitable for a destination protocol. The performance analysis of the LI-VEG shows that the transmission delay time about message translation and transmission is reduced by an average of 10.83% and the transmission delay time caused by traffic overhead is improved by an average of 0.95%. Therefore, the LI-VEG has higher compatibility and is more cost effective because it applies a software gateway to the OBD, compared to a hardware gateway. In addition, it can reduce the transmission error and overhead caused by message decomposition because of a lightweight message header. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images
Appl. Sci. 2018, 8(9), 1538; https://doi.org/10.3390/app8091538
Received: 21 July 2018 / Revised: 16 August 2018 / Accepted: 21 August 2018 / Published: 3 September 2018
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Abstract
Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault [...] Read more.
Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault diagnosis method based on time–frequency image (TFI) recognition is attracting increasing attention. This paper contributes to the ongoing investigation by proposing a new approach for the fault severity monitoring of rolling bearings based on the texture feature extraction of sparse TFIs. The first and main step is to obtain accurate TFIs from the vibration signals of rolling bearings. Traditional time–frequency analysis methods have disadvantages such as low resolution and cross-term interference. Therefore, the TFIs obtained cannot satisfactorily express the time–frequency characteristics of bearing vibration signals. To solve this problem, a sparse time–frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) was developed in this paper. Unlike traditional time–frequency analysis methods, the time–frequency analysis model of the STFA-PD method is based on the theory of sparse representation, and is solved using the first-order primal-dual algorithm. For employing the sparse constraint in the frequency domain, the STFA-PD obtains a higher time–frequency resolution and is free from cross-term interference, as the model is based on a linear time–frequency analysis method. The gray level co-occurrence matrix is then employed to extract texture features from the sparse TFIs as input features for classifiers. Vibration signals of rolling bearings with different fault severity degrees are used to validate the proposed approach. The experimental results show that the developed STFA-PD outperforms traditional time–frequency analysis methods in terms of the accuracy and effectiveness for the fault severity monitoring of rolling bearings. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
Appl. Sci. 2018, 8(9), 1441; https://doi.org/10.3390/app8091441
Received: 25 July 2018 / Revised: 15 August 2018 / Accepted: 20 August 2018 / Published: 23 August 2018
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Abstract
A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the [...] Read more.
A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions
Appl. Sci. 2018, 8(8), 1392; https://doi.org/10.3390/app8081392
Received: 16 July 2018 / Revised: 11 August 2018 / Accepted: 13 August 2018 / Published: 17 August 2018
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Abstract
This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra [...] Read more.
This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design
Appl. Sci. 2018, 8(8), 1290; https://doi.org/10.3390/app8081290
Received: 1 June 2018 / Revised: 16 July 2018 / Accepted: 25 July 2018 / Published: 2 August 2018
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Abstract
Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of [...] Read more.
Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of the modified quasi-outer-bounding algorithm towards non-linear neural network models. Subsequently, its convergence is proven using quadratic boundedness paradigm. The obtained algorithm is then equipped with the sequential D-optimum experimental design mechanism allowing gradual reduction of the neural model uncertainty. Finally, an emerging robust fault detection framework on the basis of the neural network uncertainty description as the adaptive thresholds is proposed. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning
Appl. Sci. 2018, 8(7), 1164; https://doi.org/10.3390/app8071164
Received: 14 June 2018 / Revised: 9 July 2018 / Accepted: 13 July 2018 / Published: 18 July 2018
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Abstract
This paper proposes “An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning” that collects information from the sensors of an autonomous vehicle, diagnoses itself, and the influence between its parts by using [...] Read more.
This paper proposes “An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning” that collects information from the sensors of an autonomous vehicle, diagnoses itself, and the influence between its parts by using Deep Learning and informs the driver of the result. The ISS consists of three modules. The first In-Vehicle Gateway Module (In-VGM) collects the data from the in-vehicle sensors, consisting of media data like a black box, driving radar, and the control messages of the vehicle, and transfers each of the data collected through each Controller Area Network (CAN), FlexRay, and Media Oriented Systems Transport (MOST) protocols to the on-board diagnostics (OBD) or the actuators. The data collected from the in-vehicle sensors is transferred to the CAN or FlexRay protocol and the media data collected while driving is transferred to the MOST protocol. Various types of messages transferred are transformed into a destination protocol message type. The second Optimized Deep Learning Module (ODLM) creates the Training Dataset on the basis of the data collected from the in-vehicle sensors and reasons the risk of the vehicle parts and consumables and the risk of the other parts influenced by a defective part. It diagnoses the vehicle’s total condition risk. The third Data Processing Module (DPM) is based on Edge Computing and has an Edge Computing based Self-diagnosis Service (ECSS) to improve the self-diagnosis speed and reduce the system overhead, while a V2X based Accident Notification Service (VANS) informs the adjacent vehicles and infrastructures of the self-diagnosis result analyzed by the OBD. This paper improves upon the simultaneous message transmission efficiency through the In-VGM by 15.25% and diminishes the learning error rate of a Neural Network algorithm through the ODLM by about 5.5%. Therefore, in addition, by transferring the self-diagnosis information and by managing the time to replace the car parts of an autonomous driving vehicle safely, this reduces loss of life and overall cost. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network
Appl. Sci. 2018, 8(7), 1102; https://doi.org/10.3390/app8071102
Received: 1 June 2018 / Revised: 2 July 2018 / Accepted: 4 July 2018 / Published: 8 July 2018
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Abstract
In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted [...] Read more.
In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted from signals using time domain, frequency domain, and time–frequency domain analyses, and which are then fused. However, the process of selecting and fusing features for the HI is very complex and labor-intensive. We propose a novel time–frequency image feature to construct HI and predict the RUL. To convert the one-dimensional vibration signals to a two-dimensional (2-D) image, the continuous wavelet transform (CWT) extracts the time–frequency image features, i.e., the wavelet power spectrum. Then, the obtained image features are fed into a 2-D convolutional neural network (CNN) to construct the HI. The estimated HI from the proposed model is used for the RUL prediction. The accuracy of the RUL prediction is improved by using the image features. The proposed method compresses the complex process including feature extraction, selection, and fusion into a single algorithm by adopting a deep learning approach. The proposed method is validated using a bearing dataset provided by PRONOSTIA. The results demonstrate that the proposed method is superior to related studies using the same dataset. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation
Appl. Sci. 2018, 8(6), 906; https://doi.org/10.3390/app8060906
Received: 18 May 2018 / Revised: 23 May 2018 / Accepted: 25 May 2018 / Published: 1 June 2018
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Abstract
Faults in bearings and gearboxes, which are widely used in rotating machines, can lead to heavy investment and productivity loss. Accordingly, a fault diagnosis system is necessary to ensure a high-performance transmission. However, as mechanical fault diagnosis enters the era of big data, [...] Read more.
Faults in bearings and gearboxes, which are widely used in rotating machines, can lead to heavy investment and productivity loss. Accordingly, a fault diagnosis system is necessary to ensure a high-performance transmission. However, as mechanical fault diagnosis enters the era of big data, it can be difficult to apply traditional fault diagnosis methods because of the massive computation cost and excessive reliance on human labor. Meanwhile, unsupervised learning has been shown to have excellent performance in processing machanical big data. In this paper, an unsupervised learning method known as sparse filtering is applied, the multi-correlation of a weight matrix is investigated, and a method that is more suitable for the feature extraction of signals is proposed. The main contribution of our work is the modification of original method. First, to understand the non-monotonicity testing accuracies of the original method, the physical interpretation of input dimensions is studied. Second, using the physical interpretation, an overfitting phenomenon is discovered and examined. Third, to reduce the overfitting phenomenon, a method which eliminates the multi-correlation of the weight matrix is proposed. Finally, bearing and gear datasets are employed to verify the effectiveness of the proposed method; experimental results show that the proposed method can achieve a superior performance in comparison to the original sparse filtering model. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Research of Feature Extraction Method Based on Sparse Reconstruction and Multiscale Dispersion Entropy
Appl. Sci. 2018, 8(6), 888; https://doi.org/10.3390/app8060888
Received: 16 April 2018 / Revised: 15 May 2018 / Accepted: 24 May 2018 / Published: 29 May 2018
Cited by 1 | PDF Full-text (13794 KB) | HTML Full-text | XML Full-text
Abstract
As one of the most important components in rotating machinery, it’s necessary and essential to monitor the rolling bearing operating condition to prevent equipment failure or accidents. However, in vibration signal processing, the bearing initial fault detection under background noise is quite difficult. [...] Read more.
As one of the most important components in rotating machinery, it’s necessary and essential to monitor the rolling bearing operating condition to prevent equipment failure or accidents. However, in vibration signal processing, the bearing initial fault detection under background noise is quite difficult. Therefore, in this paper a new feature extraction method combining sparse reconstruction and Multiscale Dispersion Entropy (MDErms) is proposed. Firstly, the Sliding Matrix Sequences (SMS) truncation and sparse reconstruction by Hankel-matrix are applied to the vibration signal. Then MDErms is utilized as a characteristic index of vibration signal, which is suitable for a short time series. Additionally, the MDErms is employed in the sparse reconstructed matrix sequences to achieve the Multiscale Fusion Entropy Value Sequence (MFEVS). The MFEVS keeps the fault potential feature information in different scales and is superior in distinguishing fault periodic impulses from heavy background noise. Finally, the designed FIR bandpass filter based on the MFEVS, shows prominent features in denoising and detecting weak bearing faults, which is separately verified by simulation studies and artificial fault experiments in different cases. By comparison with traditional methods like EEMD, Wavelet Packet (WP), and fast kurtogram, it can be concluded that the proposed method has a remarkable ability in removing noise and detecting rolling bearing faint fault. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
Appl. Sci. 2018, 8(6), 865; https://doi.org/10.3390/app8060865
Received: 5 May 2018 / Revised: 21 May 2018 / Accepted: 23 May 2018 / Published: 25 May 2018
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Abstract
Fault detection and isolation with redundant strapdown inertial measurement unit is critical for ensuring the reliability of the guidance or navigation system in the fields of both aeronautics and astronautics. Although the parity space approach is used widely, it cannot detect the soft [...] Read more.
Fault detection and isolation with redundant strapdown inertial measurement unit is critical for ensuring the reliability of the guidance or navigation system in the fields of both aeronautics and astronautics. Although the parity space approach is used widely, it cannot detect the soft fault which affects navigation performance under pulse quantization. This paper develops the three-channel filters to detect the soft fault and conducts theoretical implementation. The constraint conditions of their parameters are explored and the influence of the weight of different ratios is analyzed. The Monte Carlo simulation is carried out in order to verify the validity of the fault detection and isolation method. The simulation results and their analysis provide a theoretical reference for fault detection and isolation with redundant strapdown inertial measurement unit. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Open AccessArticle Fault Detection in a Multistage Gearbox Based on a Hybrid Demodulation Method Using Modulation Intensity Distribution and Variational Mode Decomposition
Appl. Sci. 2018, 8(5), 696; https://doi.org/10.3390/app8050696
Received: 16 March 2018 / Revised: 22 April 2018 / Accepted: 27 April 2018 / Published: 1 May 2018
Cited by 1 | PDF Full-text (21948 KB) | HTML Full-text | XML Full-text
Abstract
It is critical to detect hidden, periodically impulsive signatures caused by tooth defects in a gearbox. A hybrid demodulation method for detecting tooth defects has been developed in this work based on the variational mode decomposition algorithm combined with modulation intensity distribution. An [...] Read more.
It is critical to detect hidden, periodically impulsive signatures caused by tooth defects in a gearbox. A hybrid demodulation method for detecting tooth defects has been developed in this work based on the variational mode decomposition algorithm combined with modulation intensity distribution. An original multi-component signal is first non-recursively decomposed into a number of band-limited mono-components with specific sparsity properties in the spectral domain using variational mode decomposition. The hidden meaningful cyclostationary features can be clearly identified in the bi-frequency domain via the modulation intensity distribution (MID) technique. Moreover, the reduced frequency aliasing effect of variational mode decomposition is evaluated as well, which is very useful for separating noise and harmonic components in the original signal. The influences of the spectral coherence density and the spectral correlation density of the modulation intensity distribution on the demodulation were also investigated. The effectiveness and noise robustness of the proposed method have been well-verified using a simulated signal compared with the empirical mode decomposition algorithm associated with modulation intensity distribution. The proposed technique is then applied to detect four different defects in a multi-stage gearbox. The results demonstrated that the demodulated numerical information and pigmentation directly illustrated in the bi-frequency plot of the modulation intensity distribution can be successfully used to quantitatively differentiate the four gear defects. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Review

Jump to: Research

Open AccessReview A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives
Appl. Sci. 2018, 8(12), 2475; https://doi.org/10.3390/app8122475
Received: 16 November 2018 / Revised: 29 November 2018 / Accepted: 29 November 2018 / Published: 3 December 2018
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Abstract
During the last decade, due to the increasing importance of reliability and availability, railway industry is making greater use of fault diagnosis approaches for early fault detection, as well as Condition-based maintenance frameworks. Due to the influence of traction drive in the railway [...] Read more.
During the last decade, due to the increasing importance of reliability and availability, railway industry is making greater use of fault diagnosis approaches for early fault detection, as well as Condition-based maintenance frameworks. Due to the influence of traction drive in the railway system availability, several research works have been focused on Fault Diagnosis for Railway traction drives. Fault diagnosis approaches have been applied to electric machines, sensors and power electronics. Furthermore, Condition-based maintenance framework seems to reduce corrective and Time-based maintenance works in Railway Systems. However, there is not any publication that summarizes all the research works carried out in Fault diagnosis and Condition-based Maintenance frameworks for Railway Traction Drives. Thus, this review presents the development of Health Assessment and Fault Diagnosis in Railway Traction Drives during the last decade. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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