Special Issue "Fault Detection and Diagnosis of Intelligent Mechatronic Systems"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (15 February 2020).

Special Issue Editors

Dr. Hui Zhang
E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Special Issues and Collections in MDPI journals
Dr. Dan Zhang
E-Mail Website
Guest Editor
Department of Automation, Zhejiang University of Technology, Zhejiang, China
Dr. Guoguang Zhang
E-Mail Website
Guest Editor
Aptiv PLC, Kokomo, IN 46902, USA
Prof. Dr. Hamid Reza Karimi
E-Mail Website
Guest Editor
Department of Mechanical Engineering, Politecnico di Milano, via La Masa 1, 20156 Milan, Italy
Tel. 00491738179956
Interests: control theory; mechatronics; sensors and actuators; motion control; vibration control; vehicle dynamics; fault detection; health monitoring; wind energy
Special Issues and Collections in MDPI journals
Dr. Anh-Tu Nguyen
E-Mail Website
Guest Editor
Laboratory LAMIH UMR CNRS 8201, University of Valenciennes, France

Special Issue Information

Dear Colleagues,

Intelligent mechatronic systems (IMSs), such as intelligent vehicles, robots, airplanes, engines, and marine systems, have received considerable attention due to their practical applications in real lives. However, IMSs are generally complex due to the integrations of artificial intelligence and multidisciplinary features taken from mechanical engineering, computer engineering, electrical engineering, and control engineering. This integrated complexity leads to great challenges in system modeling and reliability testing due to different and complex failure modes. To achieve reliability requirements, fault detection and diagnosis are critical for the development of IMSs. With the advances in sensing, network transmission, and information processing techniques, it is our opportunity to exploit them for the benefit of fault detection and diagnosis of IMSs.

Potential topics include but are not limited to:

  • System modeling and analysis;
  • Advanced sensing technologies for IMSs;
  • Sensor fusion for IMSs;
  • Fault detection of IMSs;
  • Fault diagnosis of IMSs;
  • Fault prediction of IMSs;
  • Health monitoring of IMSs;
  • Signal processing and optimization of IMSs;
  • Intelligent detection algorithms and applications on IMSs;
  • Testbed and real system development.

Dr. Hui Zhang
Prof. Dr. Hamid Reza Karimi
Dr. Dan Zhang
Dr. Anh-Tu Nguyen
Dr. Guoguang Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Electronics is an international peer-reviewed open access monthly 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 1400 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.

Published Papers (6 papers)

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Research

Open AccessArticle
Power Quality Assessment of Grid-Connected PV System in Compliance with the Recent Integration Requirements
Electronics 2020, 9(2), 366; https://doi.org/10.3390/electronics9020366 (registering DOI) - 21 Feb 2020
Abstract
The generation and integration of photovoltaic power plants (PVPPs) into the utility grid have increased dramatically over the past two decades. In this sense, and to ensure a high quality of the PVPPs generated power as well as a contribution on the power [...] Read more.
The generation and integration of photovoltaic power plants (PVPPs) into the utility grid have increased dramatically over the past two decades. In this sense, and to ensure a high quality of the PVPPs generated power as well as a contribution on the power system security and stability, some of the new power quality requirements imposed by different grid codes and standards in order to regulate the installation of PVPPs and ensure the grid stability. This study aims to investigate the recent integration requirements including voltage sag, voltage flicker, harmonics, voltage unbalance, and frequency variation. Additionally, compliance controls and methods to fulfill these requirements are developed. In line with this, a large-scale three-phase grid-connected PVPP is designed. A modified inverter controller without the use of any extra device is designed to mitigate the sage incidence and achieve the low-voltage ride-through requirement. It can efficiently operate at normal conditions and once sag or faults are detected, it can change the mode of operation and inject a reactive current based on the sag depth. A dynamic voltage regulator and its controller are also designed to control the voltage flicker, fluctuation, and unbalance at the point of common coupling between the PVPP and the grid. The voltage and current harmonics are reduced below the specified limits using proper design and a RLC filter. The obtained results show that the proposed controller fulfilled the recent standard requirements in mitigating power quality (PQ) events. Thus, this study can increase the effort towards the development of smooth PVPP integration by optimizing the design, operation and control strategies towards high PQ and green electricity. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)
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Open AccessArticle
Robust Detection of Bearing Early Fault Based on Deep Transfer Learning
Electronics 2020, 9(2), 323; https://doi.org/10.3390/electronics9020323 - 13 Feb 2020
Abstract
In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training [...] Read more.
In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)
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Open AccessArticle
A New Fault Diagnosis Method of Bearings Based on Structural Feature Selection
Electronics 2019, 8(12), 1406; https://doi.org/10.3390/electronics8121406 - 25 Nov 2019
Abstract
By using signal processing and statistical analysis methods simultaneously, many heterogeneous features can be produced to describe the bearings fault with more comprehensive and discriminant information. At same time, there may exist redundant or irrelevant information which will instead reduce the diagnosis performance. [...] Read more.
By using signal processing and statistical analysis methods simultaneously, many heterogeneous features can be produced to describe the bearings fault with more comprehensive and discriminant information. At same time, there may exist redundant or irrelevant information which will instead reduce the diagnosis performance. To solve this problem, it is necessary to conduct feature selection which tries to choose the most typical and discriminant features by evaluating their effect on fault status. However, if the structural relationship between features has not been considered well, some similar or redundant features are still probably chosen, which would introduce bias into the final diagnosis model. In this paper, a new fault diagnosis method of bearings based on structural feature selection is proposed to solve the aforementioned problem. Obeying the hypothesis that the features with strong relatedness have close coefficient distance, the proposed method aims to improve diagnosis performance via determining group structure in fault features. First, a new feature selection strategy is proposed by introducing a group identification matrix. Using this matrix, two evaluation criteria about intra-group feature correlation and inter-group feature difference are constructed by means of coefficient’s distance. Consequently, we get a multi-objective 0–1 integer programming problem by minimizing intra-group distance and maximizing inter-group distance simultaneously. Second, we use the multi-objective particle swarm optimization algorithm to solve this problem, and then determine the optimal group structure of features adaptively. Finally, a diagnosis model can be trained by support vector machine on the typical features extracted from these groups. Experimental results on four UCI datasets show the effectiveness of the proposed group feature selection strategy. Moreover, the experimental results on two bearing datasets (i.e., CWRU and IMS datasets) demonstrate that the proposed method can identify the inherent group structure in fault features, and then has better diagnosis performance compared with several state-of-the-art methods. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)
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Open AccessArticle
A Life Prediction Model of Flywheel Systems Using Stochastic Hybrid Automaton
Electronics 2019, 8(11), 1236; https://doi.org/10.3390/electronics8111236 - 29 Oct 2019
Abstract
This paper proposes a practical life prediction model for Flywheel Systems (FSs) using the Stochastic Hybrid Automaton (SHA) method. The reliability of motors and the performance degradation of bearings are considered key causes of the failure of FSs. The unit flywheel SHA model [...] Read more.
This paper proposes a practical life prediction model for Flywheel Systems (FSs) using the Stochastic Hybrid Automaton (SHA) method. The reliability of motors and the performance degradation of bearings are considered key causes of the failure of FSs. The unit flywheel SHA model is established for the failure mechanism, considering burst failure of motors and the accumulated performance degradation of bearings. This prediction model also describes the dynamic relation of lifetime with the configurations of FSs, work modes, and running environments. Monte Carlo simulation results demonstrate that the life distributions of FSs are quite different if the spacecrafts run in various orbits or with different configurations, or under changed work modes. The proposed method provides an engineering reference and guidance for the scheme design and in-orbit mission planning of FSs. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)
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Open AccessArticle
An Effective Multiclass Twin Hypersphere Support Vector Machine and Its Practical Engineering Applications
Electronics 2019, 8(10), 1195; https://doi.org/10.3390/electronics8101195 - 20 Oct 2019
Abstract
Twin-KSVC (Twin Support Vector Classification for K class) is a novel and efficient multiclass twin support vector machine. However, Twin-KSVC has the following disadvantages. (1) Each pair of binary sub-classifiers has to calculate inverse matrices. (2) For nonlinear problems, a pair of additional [...] Read more.
Twin-KSVC (Twin Support Vector Classification for K class) is a novel and efficient multiclass twin support vector machine. However, Twin-KSVC has the following disadvantages. (1) Each pair of binary sub-classifiers has to calculate inverse matrices. (2) For nonlinear problems, a pair of additional primal problems needs to be constructed in each pair of binary sub-classifiers. For these disadvantages, a new multi-class twin hypersphere support vector machine, named Twin Hypersphere-KSVC, is proposed in this paper. Twin Hypersphere-KSVC also evaluates each sample into 1-vs-1-vs-rest structure, as in Twin-KSVC. However, our Twin Hypersphere-KSVC does not seek two nonparallel hyperplanes in each pair of binary sub-classifiers as in Twin-KSVC, but a pair of hyperspheres. Compared with Twin-KSVC, Twin Hypersphere-KSVC avoids computing inverse matrices, and for nonlinear problems, can apply the kernel trick to linear case directly. A large number of comparisons of Twin Hypersphere-KSVC with Twin-KSVC on a set of benchmark datasets from the UCI repository and several real engineering applications, show that the proposed algorithm has higher training speed and better generalization performance. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)
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Open AccessArticle
Fault Tolerant Control of Electronic Throttles with Friction Changes
Electronics 2019, 8(9), 918; https://doi.org/10.3390/electronics8090918 - 22 Aug 2019
Abstract
To enhance the reliability of the electronic throttle and consequently the vehicles driven by the internal combustion engines, a fault tolerant control strategy is developed in this paper. The proposed method employs a full-order terminal sliding mode control in conjunction with an adaptive [...] Read more.
To enhance the reliability of the electronic throttle and consequently the vehicles driven by the internal combustion engines, a fault tolerant control strategy is developed in this paper. The proposed method employs a full-order terminal sliding mode control in conjunction with an adaptive radial basis function network to estimate change rate of the fault. Fault tolerant control to abrupt and incipient changes in the throttle viscous friction torque coefficient and the throttle coulomb friction torque coefficient is achieved. Whilst the throttle position is driven to track the reference signal, the post-fault dynamics are guaranteed to converge to the equilibrium point in finite time, and the control is smooth without chattering. A nonlinear Simulink model of an electronic throttle is developed with real physical parameters and is used for evaluation of the developed method. A significant change of the throttle friction torque is simulated, and the fault tolerant control system keeps system stability and tracking the reference signal in the presence of the fault. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)
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