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Sensors for Machinery Condition Monitoring and Diagnosis
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Sensors for Machinery Condition Monitoring and Diagnosis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 4947

Special Issue Editors

Prof. Dr. Peng Chen

E-Mail Website
Guest Editor
Graduate School of Bioresources, Mie University, Tsu, Mie 514-8507, Japan
Interests: condition monitoring and diagnosis of machinery; diagnostic instrument and system for machinery; decision making of maintenance policy; diagnosis and inspection robot for plant machinery
Dr. Hongtao Xue

E-Mail Website
Guest Editor
School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212000, China
Interests: signal processing; online diagnosis; safety assessment
Prof. Dr. Huaqing Wang

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Interests: deep learning; intelligent diagnosis; feature extraction
Prof. Dr. Ke Li

E-Mail Website
Guest Editor
Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, Wuxi 214122, China
Interests: visual inspection; intelligent detection; measure technology
Dr. Liuyang Song

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Interests: data fusion; DL-based fault detection; RUL prediction

Special Issue Information

Dear Colleagues,

In recent years, the number of technological innovations in Industry 4.0 and IoT has been increasing due to the utilization of big data and artificial intelligence (AI), and the era named the Fourth Industrial Revolution is about to come. In order to realize IoT in production/transportation equipment and social infrastructure facilities, effective condition diagnosis technology of equipment, to prevent serious equipment issues and accidents and to ensure safety and security, is also indispensable.

This Special Issue focuses on methods and results obtained in fundamental and applied research pertaining to techniques and systems of condition monitoring and diagnosis for machinery. We welcome everyone from related fields to actively contribute original papers.

The topics of interest for submission in the field of sensors for machinery condition monitoring and diagnosis include, but are not limited to, the following:

(1) Condition diagnosis engineering and technology;

(2) Sensing technology;

(3) Measurement system;

(4) Diagnostic instrument and system;

(5) Fault self-recovery engineering;

(6) Signal processing;

(7) Data and information fusion;

(8) Maintenance robot;

(9) Life prediction;

(10) Others on condition monitoring and diagnosis for machinery

Prof. Dr. Peng Chen
Dr. Hongtao Xue
Prof. Dr. Huaqing Wang
Prof. Dr. Ke Li
Dr. Liuyang Song
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 submissions that pass pre-check are 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. 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 2600 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

  • sensors
  • condition monitoring
  • condition diagnosis
  • rotating machinery
  • production plant equipment
  • transportation equipment
  • social infrastructure facilities

Published Papers (3 papers)

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Research

17 pages, 3364 KiB  
Article
In-Wheel Motor Fault Diagnosis Using Affinity Propagation Minimum-Distance Discriminant Projection and Weibull-Kernel-Function-Based SVDD
by Bingchen Liu, Hongtao Xue, Dianyong Ding, Ning Sun and Peng Chen
Sensors 2023, 23(8), 4021; https://doi.org/10.3390/s23084021 - 15 Apr 2023
Cited by 1 | Viewed by 1228
Abstract
To effectively ensure the operational safety of an electric vehicle with in-wheel motor drive, a novel diagnosis method is proposed to monitor each in-wheel motor fault, the creativity of which lies in two aspects. One aspect is that affinity propagation (AP) is introduced [...] Read more.
To effectively ensure the operational safety of an electric vehicle with in-wheel motor drive, a novel diagnosis method is proposed to monitor each in-wheel motor fault, the creativity of which lies in two aspects. One aspect is that affinity propagation (AP) is introduced into a minimum-distance discriminant projection (MDP) algorithm to propose a new dimension reduction algorithm, which is defined as APMDP. APMDP not only gathers the intra-class and inter-class information of high-dimensional data but also obtains information on the spatial structure. Another aspect is that multi-class support vector data description (SVDD) is improved using the Weibull kernel function, and its classification judgment rule is modified into a minimum distance from the intra-class cluster center. Finally, in-wheel motors with typical bearing faults are customized to collect vibration signals under four operating conditions, respectively, to verify the effectiveness of the proposed method. The results show that the APMDP’s performance is better than traditional dimension reduction methods, and the divisibility is improved by at least 8.35% over the LDA, MDP, and LPP. A multi-class SVDD classifier based on the Weibull kernel function has high classification accuracy and strong robustness, and the classification accuracies of the in-wheel motor faults in each condition are over 95%, which is higher than the polynomial and Gaussian kernel function. Full article
(This article belongs to the Special Issue Sensors for Machinery Condition Monitoring and Diagnosis)
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19 pages, 5937 KiB  
Article
Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers
by Huaqing Wang, Zhitao Xu, Xingwei Tong and Liuyang Song
Sensors 2023, 23(4), 2137; https://doi.org/10.3390/s23042137 - 14 Feb 2023
Cited by 2 | Viewed by 1709
Abstract
The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working [...] Read more.
The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working environment in industrial production, the status of the equipment is changeable, and the collected signals can have new fault classes. Therefore, the open set recognition ability of the transfer learning method is an urgent research direction. The existing transfer learning model can have a severe negative transfer problem when solving the open set problem, resulting in the aliasing of samples in the feature space and the inability to separate the unknown classes. To solve this problem, we propose a Weighted Domain Adaptation with Double Classifiers (WDADC) method. Specifically, WDADC designs the weighting module based on Jensen–Shannon divergence, which can evaluate the similarity between each sample in the target domain and each class in the source domain. Based on this similarity, a weighted loss is constructed to promote the positive transfer between shared classes in the two domains to realize the recognition of shared classes and the separation of unknown classes. In addition, the structure of double classifiers in WDADC can mitigate the overfitting of the model by maximizing the discrepancy, which helps extract the domain-invariant and class-separable features of the samples when the discrepancy between the two domains is large. The model’s performance is verified in several fault datasets of rotating machinery. The results show that the method is effective in open set fault diagnosis and superior to the common domain adaptation methods. Full article
(This article belongs to the Special Issue Sensors for Machinery Condition Monitoring and Diagnosis)
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15 pages, 5205 KiB  
Article
Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor
by Hongtao Xue, Ziwei Song, Meng Wu, Ning Sun and Huaqing Wang
Sensors 2022, 22(16), 6316; https://doi.org/10.3390/s22166316 - 22 Aug 2022
Cited by 4 | Viewed by 1529
Abstract
To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a [...] Read more.
To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and AdaBoost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the AdaBoost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM). Full article
(This article belongs to the Special Issue Sensors for Machinery Condition Monitoring and Diagnosis)
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