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Selected Papers from Railway Vehicle Operation and Maintenance Academic Forum...
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Selected Papers from Railway Vehicle Operation and Maintenance Academic Forum 2022

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6498

Special Issue Editors

Prof. Dr. Qian Xiao

E-Mail Website
Guest Editor
School of Mecharonics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: PHM of rail vehicles
Dr. Long Zhang

E-Mail Website
Guest Editor
School of Mecharonics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: condition monitoring and fault diagnosis
Prof. Dr. Changqing Shen

E-Mail Website
Guest Editor
School of Rail Transportation, Soochow University, Suzhou 215131, China
Interests: signal processing; data mining; fault diagnosis; mechanical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The second RVOA will be held in Nanchang, China in 2022. Railway Vehicle O&M Academic Forum(RVOA) is a large-scale academic forum hosted by East China Jiaotong University. The forum focuses on the latest developments in the field of rail vehicle operation and maintenance, focusing on the vibration and noise of rail vehicles, monitoring and evaluation of service performance of rail vehicles, wheel-rail friction and wear, rolling contact fatigue and contact vibration, fatigue reliability of key components of rail vehicles, life extension technology of rail vehicles, PHM technology of rail vehicles, key issues such as the application of key technologies of intelligent operation and maintenance of rail vehicles, operation and maintenance equipment of rail vehicles, new technologies, new methods and new theories in the operation and maintenance of rail vehicles. The forum is held once every two years. In this issue, we aim to report some key issues such as the application of key technologies of intelligent operation and maintenance of rail vehicles, operation and maintenance equipment of rail vehicles, new technologies, new methods and new theories in the operation and maintenance of rail vehicles.

Prof. Dr. Qian Xiao
Dr. Long Zhang
Prof. Dr. Changqing Shen
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

  • railway vehicle
  • operation and maintenance
  • fault diagnosis
  • prognosis
  • vibration sensing

Published Papers (4 papers)

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Research

18 pages, 6828 KiB  
Article
Performance Degradation Assessment of Railway Axle Box Bearing Based on Combination of Denoising Features and Time Series Information
by Zhigang Liu, Long Zhang, Qian Xiao, Hao Huang and Guoliang Xiong
Sensors 2023, 23(13), 5910; https://doi.org/10.3390/s23135910 - 26 Jun 2023
Viewed by 900
Abstract
In the existing rolling bearing performance degradation assessment methods, the input signal is usually mixed with a large amount of noise and is easily disturbed by the transfer path. The time information is usually ignored when the model processes the input signal, which [...] Read more.
In the existing rolling bearing performance degradation assessment methods, the input signal is usually mixed with a large amount of noise and is easily disturbed by the transfer path. The time information is usually ignored when the model processes the input signal, which affects the effect of bearing performance degradation assessment. To solve the above problems, an end-to-end performance degradation assessment model of railway axle box bearing based on a deep residual shrinkage network and a deep long short-term memory network (DRSN-LSTM) is proposed. The proposed model uses DRSN to extract local abstract features from the signal and denoises the signal to obtain the denoised feature vector, then uses deep LSTM to extract the time-series information of the signal. The healthy time-series signal of the rolling bearing is input into the DRSN-LSTM reconstruction model for training. Time-domain, frequency-domain, and time–frequency-domain features are extracted from the signal both before and after reconstruction to form a multi-domain features vector. The mean square error of the two feature vectors is used as the degradation indicator to implement the performance degradation assessment. Artificially induced defects and rolling bearings life accelerated fatigue test data verify that the proposed model is more sensitive to early failures than mathematical models, shallow networks or other deep learning models. The result is similar to the development trend of bearing failures. Full article
(This article belongs to the Special Issue Selected Papers from Railway Vehicle Operation and Maintenance Academic Forum 2022)
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20 pages, 6892 KiB  
Article
Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis
by Long Zhang, Hao Zhang, Qian Xiao, Lijuan Zhao, Yanqing Hu, Haoyang Liu and Yu Qiao
Sensors 2022, 22(24), 9759; https://doi.org/10.3390/s22249759 - 13 Dec 2022
Viewed by 1219
Abstract
Given the complexity of the application scenarios of rolling bearing and the severe scarcity of fault samples, a solution to the issue of fault diagnosis under varying working conditions along with the absence of fault samples is required. A numerical model-driven cross-domain fault [...] Read more.
Given the complexity of the application scenarios of rolling bearing and the severe scarcity of fault samples, a solution to the issue of fault diagnosis under varying working conditions along with the absence of fault samples is required. A numerical model-driven cross-domain fault diagnosis method targeting variable working conditions is proposed based on the cross-Domain Nuisance Attribute Projection (cDNAP). Firstly, the simulation datasets consisting of multiple fault types under variable working conditions are constructed to solve the problem of incomplete fault samples. Secondly, the simulation datasets are expanded by means of generating adversarial network to ensure sufficient samples for subsequent model training. Finally, cDNAP is used to obtain the cross-domain simulation projection matrix, which eliminates the variance in the distribution of measured and simulated sample features under varying working conditions. The experimental results of cross-domain for variable working conditions show that the diagnostic accuracy reaches up to 99%. Compared with DANN, DSAN, and DAAN domain adversarial neural networks, the proposed method performs better in bearing fault diagnosis. Full article
(This article belongs to the Special Issue Selected Papers from Railway Vehicle Operation and Maintenance Academic Forum 2022)
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22 pages, 11462 KiB  
Article
Safety Study of High-Speed Collisions between Trains and Live Intruder
by Hai Zhang, Gengzhe Fu, Yongzhang Su, Yixin Yue, Wei Zhu, Chenyu Zhang and Yuxiang Lu
Sensors 2022, 22(22), 8824; https://doi.org/10.3390/s22228824 - 15 Nov 2022
Cited by 3 | Viewed by 1717
Abstract
To investigate the safety of train collisions with live intruders under high-speed operation, a new 3D finite element laminated model of live intruder filling was constructed based on reconstruction using physical 3D scanning, with three outer layers of the model simulating the skin, [...] Read more.
To investigate the safety of train collisions with live intruders under high-speed operation, a new 3D finite element laminated model of live intruder filling was constructed based on reconstruction using physical 3D scanning, with three outer layers of the model simulating the skin, three inner layers simulating bone, and internal filling simulating internal organs. The model was simulated in LS-DYNA with pendulum side collision, and the force–time and force–displacement curves of the collision between the pendulum and the living intruder were obtained, which were consistent with the curve trend of the results of the cadaver pendulum collision test by Viano in 1989, and the accuracy of the finite element model of the intruder was verified. Through the simulation calculation of high-speed collision between the train and two kinds of living intrusions, the maximum acceleration of the train body, the maximum lifting of the wheel pair, the deformation of the cowcatcher, and the maximum central load on the cowcatcher during the collision can be obtained. The results of the study show that at a collision speed of 110 km/h and different collision positions, the collision risk factor between the train and heavier organisms is relatively high, and the risk arising from frontal collisions is generally greater than that of offset collisions; despite this, all the indicators such as the maximum acceleration of the train, the maximum lift of the wheel pairs, the reduction in the length of the cowcatcher discharge per 5 m of space, and the maximum central load borne by the cowcatcher discharge are lower than the EN15227 standard. Additionally, the safety of the train is not affected and the components can work reliably. Full article
(This article belongs to the Special Issue Selected Papers from Railway Vehicle Operation and Maintenance Academic Forum 2022)
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23 pages, 16776 KiB  
Article
Multiple Enhanced Sparse Representation via IACMDSR Model for Bearing Compound Fault Diagnosis
by Long Zhang, Lijuan Zhao, Chaobing Wang, Qian Xiao, Haoyang Liu, Hao Zhang and Yanqing Hu
Sensors 2022, 22(17), 6330; https://doi.org/10.3390/s22176330 - 23 Aug 2022
Cited by 1 | Viewed by 1194
Abstract
For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation, namely IACMDSR, is developed in this paper. [...] Read more.
For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation, namely IACMDSR, is developed in this paper. Firstly, the IACMD is employed to simultaneously separate the distinct fault types and extract multiple resonance frequencies induced by them. Next, an adaptive bilateral wavelet hyper-dictionary that digs deeper into the periodicity and waveform characteristics exhibited by the real fault impulse response is constructed to identify and reconstruct each type of fault-induced feature with the help of the orthogonal matching pursuit (OMP) algorithm. Finally, the fault characteristic frequency can be detected via an envelope demodulation analysis of the reconstructed signal. A simulation and two sets of experimental results confirm that the developed IACMDSR model is a powerful and versatile tool and consistently outperforms the leading MCKDSR and MCKDMWF models. Furthermore, the developed model has satisfactory capability in practical applications because the IACMD has no requirement for the input number of the signal components and the adaptive bilateral wavelet is powerfully matched to the real fault-induced impulse response. Full article
(This article belongs to the Special Issue Selected Papers from Railway Vehicle Operation and Maintenance Academic Forum 2022)
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