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Special Issue "Sensors for Fault Diagnosis"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (10 January 2020).

Special Issue Editor

Dr. Steven Chatterton
E-Mail Website
Guest Editor
Politecnico di Milano, Department of Mechanical Engineering, Via G. La Masa 1, 20156 Milano, Italy
Interests: rotordynamics; fault diagnostics; rolling element bearings; oil-film-bearings

Special Issue Information

Dear Colleagues,

Until the last century, civil and industrial fault diagnostics were often implemented using standard inspection tools, techniques, and sensors that were installed and used only after the failure, for example, in non-destructive tests using ultrasound transducers or accelerometers.

In recent years, there has been a trend reversal, with an increase in the number of on-board sensors used to monitor the status of machines and to diagnose their faults or malfunctions.

The increase in sensors took place in parallel with the miniaturization of the sensors and with the availability of electronic systems with an ever-increasing computing capacity capable of processing the amount of information coming from the sensors, as occurs in modern appliances, where the status or the diagnosis of any malfunction can be performed using a mobile phone.

The increase in the number of sensors has led to decentralized monitoring systems, where the sensors are able to process the signal and transmit an indicator of the malfunction to the plant's central control unit via a wireless mode.

We are also witnessing a transformation of the sensor concept, no longer as a separate object added to a machine or to an already existing product, but as an element integrated into the product itself as, for example, fibre optic sensors embedded in composite materials.

The Special Issue “Sensors for Fault Diagnosis” aims to summarize the state of the art of the research technology and application on any kind of sensor for diagnostics purposes. The Special Issue includes, but is not limited to, the following applications:

  • Acoustic emission and vibration sensors
  • Strain gauges
  • Pressure and temperature sensors
  • Fibre optic sensors
  • Embedded sensors and applications
  • Strategies and algorithms for fault detection

The purpose of the Special Issue is to collect original research papers or review articles. Although the emphasis is on practical applications, we also welcome fundamental studies.

Dr. Steven Chatterton
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Strain gauge
  • Pressure sensor
  • Accelerometer
  • Fibre optic sensor
  • Image sensor
  • Optical sensor
  • Acoustic emission
  • Radiation detector
  • Embedded sensor
  • Smart sensor

Published Papers (15 papers)

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Research

Open AccessArticle
State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System
Sensors 2020, 20(4), 1017; https://doi.org/10.3390/s20041017 - 13 Feb 2020
Abstract
As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process [...] Read more.
As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives
Sensors 2020, 20(4), 962; https://doi.org/10.3390/s20040962 (registering DOI) - 11 Feb 2020
Abstract
Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order to move from preventive maintenance to condition-based maintenance. Most research works are limited to sensor fault detection and isolation, but only a [...] Read more.
Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order to move from preventive maintenance to condition-based maintenance. Most research works are limited to sensor fault detection and isolation, but only a few of them analyze the types of sensor faults, such as offset or gain, with the aim of reconfiguring the sensor in order to implement a fault tolerant system. This article is based on a fusion of model-based and data-driven techniques. First, an observer-based approach, using a Sliding Mode observer, is utilized for sensor fault reconstruction in real time. Then, once the fault is detected, a time window of sensor measurements and sensor fault reconstruction is sent to the remote maintenance center for fault evaluation. Finally, an offline processing is carried out to discriminate between gain and offset sensor faults, in order to get a maintenance decision-making to reconfigure the sensor during the next train stop. Fault classification is done by means of histograms and statistics. The technique here proposed is applied to the DC-link voltage sensor in a railway traction drive and is validated in a hardware-in-the-loop platform. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
An in-Process Inspection System to Detect Noise Originating from within the Interior Trim Panels of Car Doors
Sensors 2020, 20(3), 630; https://doi.org/10.3390/s20030630 - 23 Jan 2020
Abstract
Car body parts are sometimes responsible for irritating noise caused by assembly defects. Typically, various types of noise are known to originate from within the interior trim panels of car doors. This noise is considered to be an important factor that degrades the [...] Read more.
Car body parts are sometimes responsible for irritating noise caused by assembly defects. Typically, various types of noise are known to originate from within the interior trim panels of car doors. This noise is considered to be an important factor that degrades the emotional satisfaction of the driver of the car. This research suggests an in-process inspection system consisting of an inspection workstation and a noise detection method. The inspection workstation presses down the car door trim panel by using a pneumatic pusher while microphones record the acoustic signals directly above the door trim panel and on the four sides of the workstation. The collected signals are analyzed by the proposed noise detection method after applying noise reduction. The noise detection method determines the presence of irritating noise by using noise source localization in combination with the time difference of arrival method and the relative signal strengths. The performance of the in-process noise detection system was evaluated by conducting experiments on faulty and healthy car door trim panels. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis
Sensors 2020, 20(1), 320; https://doi.org/10.3390/s20010320 - 06 Jan 2020
Abstract
Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault [...] Read more.
Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
Bearing State Recognition Method Based on Transfer Learning Under Different Working Conditions
Sensors 2020, 20(1), 234; https://doi.org/10.3390/s20010234 - 31 Dec 2019
Abstract
Bearing state recognition, especially under variable working conditions, has the problems of low reusability of monitoring data, low state recognition accuracy and low generalization ability of the model. The feature-based transfer learning method can solve the above problems, but it needs to rely [...] Read more.
Bearing state recognition, especially under variable working conditions, has the problems of low reusability of monitoring data, low state recognition accuracy and low generalization ability of the model. The feature-based transfer learning method can solve the above problems, but it needs to rely on signal processing knowledge and expert diagnosis experience to obtain the cross-characteristics of different working conditions data in advance. Therefore, this paper proposes an improved balanced distribution adaptation (BDA), named multi-core balanced distribution adaptation (MBDA). This method constructs a weighted mixed kernel function to map different working conditions data to a unified feature space. It does not need to obtain the cross-characteristics of different working conditions data in advance, which simplifies the data processing and meet end-to-end state recognition in practical applications. At the same time, MBDA adopts the ADistance algorithm to estimate the balance factor of the distribution and the balance factor of the kernel function, which not only effectively reduces the distribution difference between different working conditions data, but also improves efficiency. Further, feature self-learning and rolling bearing state recognition are realized by the stacked autoencoder (SAE) neural network with classification function. The experimental results show that compared with other algorithms, the proposed method effectively improves the transfer learning performance and can accurately identify the bearing state under different working conditions. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions
Sensors 2019, 19(22), 4827; https://doi.org/10.3390/s19224827 - 06 Nov 2019
Abstract
The rolling bearing is an important part of the train’s running gear, and its operating state determines the safety during the running of the train. Therefore, it is important to monitor and diagnose the health status of rolling bearings. A convolutional neural network [...] Read more.
The rolling bearing is an important part of the train’s running gear, and its operating state determines the safety during the running of the train. Therefore, it is important to monitor and diagnose the health status of rolling bearings. A convolutional neural network is widely used in the field of fault diagnosis because it does not require feature extraction. Considering that the size of the network model is large and the requirements for monitoring equipment are high. This study proposes a novel bearing fault diagnosis method based on lightweight network ShuffleNet V2 with batch normalization and L2 regularization. In the experiment, the one-dimensional time-domain signal is converted into a two-dimensional Time-Frequency Graph (TFG) using a short-time Fourier transform, though the principle of graphics to enhance the TFG dataset. The model mainly consists of two units, one for extracting features and one for spatial down-sampling. The building units are repeatedly stacked to construct the whole model. By comparing the proposed method with the origin ShuffleNet V2, machine learning model and state-of-the-art fault diagnosis model, the generalization of the proposed method for bearing fault diagnosis is verified. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine
Sensors 2019, 19(20), 4542; https://doi.org/10.3390/s19204542 - 18 Oct 2019
Abstract
The health state of rotating machinery directly affects the overall performance of the mechanical system. The monitoring of the operation condition is very important to reduce the downtime and improve the production efficiency. This paper presents a novel rotating machinery fault diagnosis method [...] Read more.
The health state of rotating machinery directly affects the overall performance of the mechanical system. The monitoring of the operation condition is very important to reduce the downtime and improve the production efficiency. This paper presents a novel rotating machinery fault diagnosis method based on the improved multiscale amplitude-aware permutation entropy (IMAAPE) and the multiclass relevance vector machine (mRVM) to provide the necessary information for maintenance decisions. Once the fault occurs, the vibration amplitude and frequency of rotating machinery obviously changes and therefore, the vibration signal contains a considerable amount of fault information. In order to effectively extract the fault features from the vibration signals, the intrinsic time-scale decomposition (ITD) was used to highlight the fault characteristics of the vibration signal by extracting the optimum proper rotation (PR) component. Subsequently, the IMAAPE was utilized to realize the fault feature extraction from the PR component. In the IMAAPE algorithm, the coarse-graining procedures in the multi-scale analysis were improved and the stability of fault feature extraction was promoted. The coarse-grained time series of vibration signals at different time scales were firstly obtained, and the sensitivity of the amplitude-aware permutation entropy (AAPE) to signal amplitude and frequency was adopted to realize the fault feature extraction of coarse-grained time series. The multi-classifier based on the mRVM was established by the fault feature set to identify the fault type and analyze the fault severity of rotating machinery. In order to demonstrate the effectiveness and feasibility of the proposed method, the experimental datasets of the rolling bearing and gearbox were used to verify the proposed fault diagnosis method respectively. The experimental results show that the proposed method can be applied to the fault type identification and the fault severity analysis of rotating machinery with high accuracy. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
Fault Feature Extraction and Diagnosis of Rolling Bearings Based on Enhanced Complementary Empirical Mode Decomposition with Adaptive Noise and Statistical Time-Domain Features
Sensors 2019, 19(18), 4047; https://doi.org/10.3390/s19184047 - 19 Sep 2019
Cited by 1
Abstract
In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white [...] Read more.
In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white noise (AAWN) and the ensemble trails (ET). By introducing the concept of decomposition level, the optimal AAWN can be determined by judging the mutation of mutual information (MI) between adjacent intrinsic mode functions (IMFs). Furthermore, the ET is fixed at two to reduce the computational cost. This method can avoid disturbance of the spurious mode in the signal decomposition and increase computational speed. Enhanced CEEMDAN demonstrates a more significant improvement than that of the traditional CEEMDAN. Vibration signals can be decomposed into a set of IMFs using enhanced CEEMDAN. Some IMFs, which are named intrinsic information modes (IIMs), effectively reflect the vibration characteristic. The evaluated comprehensive factor (CF), which combines the shape, crest and impulse factors, as well as the kurtosis, skewness, and latitude factor, is developed to identify the IIM. CF can retain the advantage of a single factor and make up corresponding drawbacks. Experiment results, especially for the extraction of bearing fault under variable speed, illustrate the superiority of the proposed method for the fault diagnosis of rolling bearings over other methods. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis
Sensors 2019, 19(18), 3994; https://doi.org/10.3390/s19183994 - 16 Sep 2019
Cited by 4
Abstract
To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order [...] Read more.
To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
A Novel Demodulation Analysis Technique for Bearing Fault Diagnosis via Energy Separation and Local Low-Rank Matrix Approximation
Sensors 2019, 19(17), 3755; https://doi.org/10.3390/s19173755 - 30 Aug 2019
Cited by 1
Abstract
Bearing fault diagnosis is of utmost importance in the maintenance of mechanical equipment. The collected fault vibration signal generally presents a modulated nature due to the special structure and dynamic characteristics of the bearings. This paper introduces a novel demodulation analysis technique via [...] Read more.
Bearing fault diagnosis is of utmost importance in the maintenance of mechanical equipment. The collected fault vibration signal generally presents a modulated nature due to the special structure and dynamic characteristics of the bearings. This paper introduces a novel demodulation analysis technique via energy separation and local low-rank matrix approximation (LLORMA) to address this type of signal. The amplitude envelope and instantaneous frequency of the signal can be calculated via an energy separation algorithm based on the Teager energy operator. We can confirm the bearing faults by comparing the peak frequencies of the Fourier spectrum of the amplitude envelope and instantaneous frequency with the theoretical bearing fault-related frequencies. However, this algorithm is only suitable for handling single-component signals. In addition, the powerful background noise has a serious effect on the demodulation results. To tackle these problems, a new signal decomposition method based on LLORMA is proposed to decompose the signal into several single-components and eliminate the noise simultaneously. After that, the single-component signal representing the fault characteristics can be identified via the high frequency feature of the modulated signal. The analysis of the simulated signal and the bearing outer race fault signal collected from a bearing-gear fault test rig indicate that the proposed technique has an excellent diagnostic performance for bearing fault signals. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
Automatic Fault Detection and Isolation Method for Roller Bearing Using Hybrid-GA and Sequential Fuzzy Inference
Sensors 2019, 19(16), 3553; https://doi.org/10.3390/s19163553 - 15 Aug 2019
Cited by 3
Abstract
Though accelerometers for condition diagnosis of a bearing is preferably placed at the nearest position of the bearing as possible, in some plant equipment, the accelerometer is difficult to set near the diagnosed bearing, and in many cases, sensors have to be placed [...] Read more.
Though accelerometers for condition diagnosis of a bearing is preferably placed at the nearest position of the bearing as possible, in some plant equipment, the accelerometer is difficult to set near the diagnosed bearing, and in many cases, sensors have to be placed at a location far from the diagnosed bearing to measure signals for diagnosing bearing faults. Since, in these cases, the measured signals contain stronger noise than the signal measured near the diagnosed bearing, bearing faults are more difficultly to be detected. In order to overcome the above difficulty, this paper proposes a new fault auto-detection method by which the signals measured by an accelerometer located at a far point from the diagnosed bearing can be used to simply and accurately detect the bearing faults automatically. Firstly, the hybrid GA (the combination of genetic algorithm and tabu search) is used to automatically search and determine the optimum cutoff frequency of the high-pass filter to extract the fault signal of the abnormal bearing. Secondly, the bearing faults are precisely diagnosed by possibility theory and fuzzy inference. Finally, in order to demonstrate the effectiveness of these proposed methods, these methods were applied to bearing diagnostics using vibration signals measured at the far point of the diagnostic bearing, and the efficiency of these methods was verified by the results of automatic bearing fault diagnosis. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
A Novel Fault Feature Recognition Method for Time-Varying Signals and Its Application to Planetary Gearbox Fault Diagnosis under Variable Speed Conditions
Sensors 2019, 19(14), 3154; https://doi.org/10.3390/s19143154 - 17 Jul 2019
Cited by 1
Abstract
The existing time-frequency analysis (TFA) methods mainly highlight the time-frequency ridges of the interested components by optimizing the time-frequency plane to facilitate the extraction of the relevant components. Generalized demodulation (GD), order tracking (OT), and other methods are generally used in conjunction with [...] Read more.
The existing time-frequency analysis (TFA) methods mainly highlight the time-frequency ridges of the interested components by optimizing the time-frequency plane to facilitate the extraction of the relevant components. Generalized demodulation (GD), order tracking (OT), and other methods are generally used in conjunction with the TFA methods to realize the transition from a time-varying signal to a stationary signal, and finally identify the fault feature through a time-frequency plane. Generally, it is necessary to clarify the accuracy of the estimated components such as the rotational frequency or the fault characteristic frequency (FCF) during the operation of the GD or OT methods. Unfortunately, it is not only difficult to extract and locate rotational frequency or FCF, but also complicated in the whole estimation process. In this paper, a simple yet readable method is proposed to reveal the fault feature of time-varying signals. First, the method only needs to extract an arbitrary instantaneous frequency (IF). This is different from the GD method which needs to estimate and locate all phase functions. Then, it converts all variable frequency curves into corresponding lines parallel to the frequency axis based on the extracted IF to determine the proportional relationship between the components. Finally, to further improve the readability of the final results, we reduce the dimension of the transformed time-frequency representation to generate a two-dimensional (2D) energy-frequency map with high resolution and the same proportion. Subsequently, the performance is validated by simulated and experimental data. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis
Sensors 2019, 19(14), 3109; https://doi.org/10.3390/s19143109 - 14 Jul 2019
Cited by 1
Abstract
Fault diagnosis is critical to ensuring the safety and reliable operation of rotating machinery systems. Long short-term memory networks (LSTM) have received a great deal of attention in this field. Most of the LSTM-based fault diagnosis methods have too many parameters and calculation, [...] Read more.
Fault diagnosis is critical to ensuring the safety and reliable operation of rotating machinery systems. Long short-term memory networks (LSTM) have received a great deal of attention in this field. Most of the LSTM-based fault diagnosis methods have too many parameters and calculation, resulting in large memory occupancy and high calculation delay. Thus, this paper proposes a low-delay lightweight recurrent neural network (LLRNN) model for mechanical fault diagnosis, based on a special LSTM cell structure with a forget gate. The input vibration signal is segmented into several shorter sub-signals in order to shorten the length of the time sequence. Then, these sub-signals are sent into the network directly and converted into the final diagnostic results without any manual participation. Compared with some existing methods, our experiments illustrate that the proposed method has less memory space occupancy and lower computational delay while maintaining the same level of accuracy. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform
Sensors 2019, 19(12), 2750; https://doi.org/10.3390/s19122750 - 19 Jun 2019
Cited by 3
Abstract
Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN [...] Read more.
Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN combined with signal processing algorithm is often adopted for fault diagnosis. This increases manual conversion work and expertise dependence while reducing the feasibility and robustness of the corresponding fault diagnosis method. In this paper, a novel sensor data-driven fault diagnosis method is proposed by fusing S-transform (ST) algorithm and CNN, namely ST-CNN. First of all, a ST layer is designed based on S-transform algorithm. In the ST layer, sensor data is automatically converted into 2D time-frequency matrix without manual conversion work. Then, a new ST-CNN model is constructed, and the time-frequency coefficient matrixes are inputted into the constructed ST-CNN model. After the training process of the ST-CNN model is completed, the classification layer such as softmax performs the fault diagnosis. Finally, the diagnosis performance of the proposed method is evaluated by using two public available datasets of bearings. The experimental results show that the proposed method performs the higher and more robust diagnosis performance than other existing methods. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
A New Fault Diagnosis Method for a Diesel Engine Based on an Optimized Vibration Mel Frequency under Multiple Operation Conditions
Sensors 2019, 19(11), 2590; https://doi.org/10.3390/s19112590 - 06 Jun 2019
Cited by 3
Abstract
The diesel engine has been a significant component of large-scale mechanical systems for the intelligent manufacturing industry. Because of its complex structure and poor working environment, it has trouble effectively acquiring the representative fault features. Further, fault diagnosis of the diesel engine faces [...] Read more.
The diesel engine has been a significant component of large-scale mechanical systems for the intelligent manufacturing industry. Because of its complex structure and poor working environment, it has trouble effectively acquiring the representative fault features. Further, fault diagnosis of the diesel engine faces great challenges. This paper presents a new fault diagnosis method for the detection of diesel engine faults under multiple operation conditions instead of conventional methods confined to a single condition. First, an adaptive correlation threshold process is designed as a preprocessing unit to enhance data quality by weakening non-impact region characteristics. Next, a feature extraction method for sound signals based on the Mel frequency cepstrum (MFC) is improved and introduced into the machinery fault diagnosis. Then, the combination of the improved feature and vibrational mode decomposition (VMD) is proposed to incorporate VMD into an effective adaptive decomposition of non-stationary signals to combine it with an excellent feature representation of the vibration signal. Finally, the vector quantization algorithm is adopted to reduce the feature dimensions and generate codebook model bases, which trains the K-Nearest Neighbor classifiers. Five comparative methods were carried out, and the experimental results show that the proposed method offers a good effect of the common valve clearance fault of diesel engines under different conditions. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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