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A Data-Driven Dynamics Simulation Model for Railway Vehicles Based on Lightweight 3DCNN With Physics-Informed Constraints
The dynamics simulation of complex railway vehicles requires a dedicated vehicle model, such as multi-body dynamics model. However, the multi-body model is time-consuming in long-distance simulation due to its computational complexity. This issue can be alleviated by using a data-driven vehicle dynamics model due to its effective generalization and computational speed. Firstly, the construction of...Show More
Integrating dynamic simulation knowledge and domain adversarial neural network for state recognition of high-speed train axlebox bearings
Yanlong Pan;Cai Yi;Xiaokang Liao;Yunzhi Lin;Lei Yan;Jianhui Lin
IEEE Sensors Journal
Year: 2025 | Early Access Article |
Transfer learning helps address the issue of limited fault samples in real high-speed trains by transferring domain knowledge. However, most existing transfer learning approaches rely heavily on well-labeled source domain samples and do not account for the varying quality of these samples, treating all samples with equal importance. To tackle this problem, this paper proposes a high-speed train ax...Show More
Normalized Feature Energy-Guided Adaptive Frequency Band Division Method for Wheelset Bearing Compound Fault Detection
Yanlong Pan;Cai Yi;Yunxiao Fu;Yunzhi Lin;Fan Zhang;Jianhui Lin
Resonance demodulation technology effectively extracts bearing fault features and identifies fault types. Its key step is the accurate detection of the informative frequency band (IFB) caused by faults. For compound faults, multiple IFBs from different faults must be identified. Given the complexity of the train operating environment, IFBs cannot be predetermined, and fixed band division often fai...Show More
Dictionary Learning Method for Cyclostationarity Maximization and Its Application to Bearing Fault Feature Extraction
Weihao Zhang;Cai Yi;Lei Yan;Qi Liu;Qiuyang Zhou;Pengfei He;Le Ran;Yunzhi Lin
It has been demonstrated that fast convolutional sparse dictionary learning (FCSDL) is a useful instrument for diagnosing rolling bearing faults and can recover rolling bearing fault shocks unaffected by random slippage. However, although FCSDL is not impacted by random fluctuations and can rapidly reconstruct fault shock without truncating the signal, its performance for repetitive fault shock re...Show More
Direct Denoising of Fault Signal for Train Bogie Bearing Under Speed Change Condition
Zexian Wei;Deqiang He;Zhenzhen Jin;Haimeng Sun;Sheng Shan;Chang Liu;Cai Yi
Due to wheel-rail coupling and other factors, the fault feature of train bogie bearings can be easily masked by noise. The superposition of speed changes under fault shock further causes more difficulty for the direct denoising of signals. Therefore, a denoising framework that can directly process the original vibration signal under speed change has been proposed in this study. The framework inclu...Show More
Multi-Node Feature Learning Network Based on Maximum Spectral Harmonics-to-Noise Ratio Deconvolution for Machine Condition Monitoring
Qiuyang Zhou;Cai Yi;Lei Yan;Chenguang Huang;Xinwu Song;Du Xu;Lu Zhou;Jianhui Lin
Since the cyclostationarity in vibration signals is the key to judge the rotating machine health state, spectral harmonics-to-interference ratio (SHIR) has been used to construct single node feature learning network (SHIR-based blind deconvolution, BD-SHIR) to realize condition monitoring of machine. However, BD-SHIR still has several obvious limitations, including the need for prior fault informa...Show More
In recent years, rolling bearing fault diagnosis technology based on deep learning (DL) has provided a more intelligent and reliable method for the safe operation of mechanical systems due to its powerful feature learning ability. However, in real industrial scenarios, the acquisition of fault samples is limited and knotty, which makes it difficult for DL methods that require a large number of fau...Show More
In recent years, the rolling bearing fault diagnosis technique based on deep learning (DL) provides a more intelligent and reliable way for the safe operation of mechanical systems. However, this technique still exists problems of high model complexity and poor generalization ability in the application. To solve the above problem, a novel periodic cyclic sparse network with entire domain adaptatio...Show More
Multi-Objective Sparsity Maximum Mode De-Composition: A New Method for Rotating Machine Fault Diagnosis on High-Speed Train Axle Box
Qiuyang Zhou;Cai Yi;Lei Yan;Xinwu Song;Du Xu;Chenguang Huang;Lu Zhou;Jianhui Lin
Decomposition techniques generally have two limitations. First, for adaptive correlated kurtogram and variational mode decomposition, etc., the definition of the decomposition target has no direct relationship with the features of fault impacts, i.e., the decomposition is not guided by health indices used to characterize bearing faults, resulting in fault impacts cannot be completely separated fro...Show More
An Adaptive Harmonic Product Spectrum for Rotating Machinery Fault Diagnosis
Cai Yi;Hao Wang;Qiuyang Zhou;Qiwei Hu;Pengcheng Zhou;Jianhui Lin
Although the frequency band segmentation rules used in fast kurtogram (FK) has made great achievements in locating fault resonance band, it still has the problem of underdecomposition or overdecomposition for resonance band. This will affect the performance of the methods based on this band segmentation rule in fault diagnosis. Aiming at this problem, an adaptive harmonic product spectrum (AHPS) i...Show More
A Blind Deconvolution Approach Based on Spectral Harmonics-to-Noise Ratio for Rotating Machinery Condition Monitoring
Harmonics-to-noise ratio (HNR) is an important health index of rotating machine, which has been applied in blind deconvolution (BD) method to realize periodic impulse detection. However, most fault impulses are not strictly periodic, but pseudo-cyclostationary, which will affect the performance of HNR in fault characterization to some extent. This limits its applications. Therefore, in this paper,...Show More
An Improved Sparse Representation Based on Local Orthogonal Matching Pursuit for Bearing Compound Fault Diagnosis
Cai Yi;Le Ran;Jiayin Tang;Hang Jin;Zhe Zhuang;Qiuyang Zhou;Jianhui Lin
Convolutional sparse representation based on local orthogonal matching pursuit (SR-LocOMP) can recover wheelset-bearing fault impulses without being affected by random slippage and plays an important role in wheelset-bearing fault diagnosis. However, the performance of SR-LocOMP at a low signal-to-noise ratio (SNR) is not satisfied. In addition, when the analyzed signals contain compound faults, t...Show More
Fast Convolutional Sparse Dictionary Learning Based on LocOMP and Its Application to Bearing Fault Detection
Sparse representations based on convolutional sparse dictionary learning (CSDL) provide an excellent framework for extracting fault impulse response caused by bearing faults. In order to achieve fast dictionary learning, most CSDL-based fault diagnosis techniques recommend truncating the original data. However, the choice of truncation length is very difficult. An improper truncation length will l...Show More
Fast kurtogram (FK) is an effective tool in fault diagnosis, but it still has two defects. Firstly, its indicator is easy to be affected by the random impact. Secondly, its fixed frequency band segmentation rules might lead to over-decomposition or under-decomposition problems. Therefore, by combining a more robust indicator, envelope harmonic-to-noise ratio (EHNR), with an adaptive frequency band...Show More
The health status of bearing is directly related to the safe operation of rotating machinery. Bearing fault detection technology is of great significance to reduce or eliminate safety accidents. Singular value decomposition (SVD), as an effective low-rank approximation tool of data matrix, is widely used in bearing fault detection. The construction of trajectory matrix and the selection of singula...Show More
Blind Deconvolution Technique Based on Improved Correlated Generalized Lp/Lq Norm for Extracting Repetitive Transient Feature
Qiuyang Zhou;Yuhui Zhang;Jiayin Tang;Jianhui Lin;Liu He;Cai Yi
Since minimum correlated generalized Lp/Lq deconvolution (MCG-Lp/Lq-D) cannot simultaneously have a good sensing ability to repetitive fault impulses and a strong robustness to noise when processing low SNR signals, a new deconvolution method, minimum improved correlated generalized Lp/Lq deconvolution (MICG-Lp/Lq-D), is proposed in this article. The proposed method is designed based on a novel no...Show More
Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse representation and the efficiency of sparse representation algorithm need to be improved. In order to accurately detect the fault impulse in the ...Show More
High-Speed Rail Suspension System Health Monitoring Using Multi-Location Vibration Data
Ning Hong;Lishuai Li;Weiran Yao;Yang Zhao;Cai Yi;Jianhui Lin;Kwok Leung Tsui
A novel data-driven framework to monitor the health status of high-speed rail suspension system by measuring train vibrations is proposed herein. Unlike existing methods, this framework does not rely on sophisticated dynamic models or high-fidelity simulations; it combines the power of data and domain knowledge to generate a model that can be trained quickly and adapted easily to different rail sy...Show More
Condition monitoring of wheel wear for high-speed trains: A data-driven approach
Peiwen Xu;Weiran Yao;Yang Zhao;Cai Yi;Lishuai Li;Jianhui Lin;Kwok Leung Tsui
2018 IEEE International Conference on Prognostics and Health Management (ICPHM)
Year: 2018 | Conference Paper |
Cited by: Papers (9)
Condition monitoring, as part of the intelligent infrastructure concept, can significantly improve the reliability, safety and efficiency of rail operations. Degradation in infrastructure can be detected before a problem occurs, without interrupting normal operations. This paper contributes to the development of analytics tools and methods for condition monitoring using sensor data. A data-driven ...Show More
Quantizing the Health State of Railway Axle Bearing via Signal-to-noise ratio defined by EEMD and SVD
2018 IEEE International Conference on Prognostics and Health Management (ICPHM)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
State quantized characterization is of great demand with the development of automation and intelligent machineries. It is helpful for monitoring the operation state for machines in real time, and is critical for detecting the defects and potential faults. State characterization is also a base rock to implement a state-based maintenance strategy for avoiding the unexpected accidents and significant...Show More
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