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: 30 September 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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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. Applied Sciences is an international peer-reviewed open access monthly journal published by MDPI.

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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 (7 papers)

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Research

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
Cited by 1 | PDF Full-text (7005 KB) | HTML Full-text | XML Full-text
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
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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
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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
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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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