Fault Detection and Behavior Analysis of Wheelset Bearing Using Adaptive Convolutional Sparse Coding Technique Combined with Bandwidth Optimization

Wheelset bearing is a critical and easily damaged component of a high-speed train. Wheelset bearing fault diagnosis is of great significance to ensure safe operation of high-speed trains and realize intelligent operation and maintenance. The convolutional sparse coding technique based on the dictionary learning algorithm (CSCT-DLA) provides an effective algorithm framework for extracting the impulses caused by bearing defect. However, dictionary learning is easily affected by foundation vibration and harmonic interference and cannot learn the key structure related to fault impulses. At the same time, the detection performance of fault impulse heavily depends on the selection of parameters in this approach. Union of convolutional dictionary learning algorithm (UC-DLA) is an efficient algorithm in CSCT-DLA. In this paper, UC-DLA is introduced and improved for wheelset bearing fault detection. Finally, a novel bearing fault detection method, adaptive UC-DLA combined with bandwidth optimization (AUC-DLA-BO), is proposed. The mathematical formulation of AUC-DLA-BO is a sort of constrained optimization problem, which can overcome foundation vibration and harmonic interference and adaptively determine parameters related to UC-DLA. The proposed method can detect the fault resonance band adaptively, eliminate the noise with the same frequency band as the fault resonance band, and highlight the bearing fault impulses. Simulated signals and bench tests are used to verify the effectiveness of the proposed method. The results show that AUC-DLA-BO can effectively detect bearing faults and realize the refined analysis of fault behavior.

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An adaptive boundary determination method for empirical wavelet transform and its application in wheelset-bearing fault detection in high-speed trains

Measurement ◽

10.1016/j.measurement.2020.108746 ◽

2021 ◽

Vol 171 ◽

pp. 108746

Author(s):

Qingsong Zhang ◽

Jianming Ding ◽

Wentao Zhao

Keyword(s):

Wavelet Transform ◽

Fault Detection ◽

High Speed ◽

Determination Method ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Boundary Determination ◽

Empirical Wavelet Transform ◽

High Speed Trains

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Adaptive sparse representation based on circular-structure dictionary learning and its application in wheelset-bearing fault detection

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2018.04.012 ◽

2018 ◽

Vol 111 ◽

pp. 399-422 ◽

Cited By ~ 17

Author(s):

Jianming Ding ◽

Wentao Zhao ◽

Bingrong Miao ◽

Jianhui Lin

Keyword(s):

Fault Detection ◽

Sparse Representation ◽

Dictionary Learning ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Circular Structure

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Convolutional sparse coding using pathfinder algorithm-optimized orthogonal matching pursuit with asymmetric Gaussian chirplet model in bearing fault detection

IEEE Sensors Journal ◽

10.1109/jsen.2021.3086015 ◽

2021 ◽

pp. 1-1

Author(s):

Qiuyang Zhou ◽

Yuhui Zhang ◽

Cai Yi ◽

Jianhui Lin ◽

Liu He ◽

...

Keyword(s):

Fault Detection ◽

Sparse Coding ◽

Matching Pursuit ◽

Orthogonal Matching Pursuit ◽

Bearing Fault ◽

Bearing Fault Detection

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A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection

Sensors ◽

10.3390/s21186025 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6025

Author(s):

Feiyue Deng ◽

Chao Liu ◽

Yongqiang Liu ◽

Rujiang Hao

Keyword(s):

Fault Detection ◽

High Speed ◽

Working Environment ◽

Permutation Entropy ◽

Mechanical Equipment ◽

Noise Interference ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Self Adaptive

Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time−frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time−frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time−frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel’s subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time−frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment.

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Linear Bearing Fault Detection Using an Artificial Neural Network Based on a PI Servo System with the Observer for High-speed Automation Machine

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/717/1/012011 ◽

2020 ◽

Vol 717 ◽

pp. 012011

Author(s):

T Wanglomklang ◽

P Chommaungpuck ◽

J Srisertpol

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fault Detection ◽

High Speed ◽

Servo System ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Artificial Neural

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Wheelset-Bearing Fault Detection Using Adaptive Convolution Sparse Representation

Shock and Vibration ◽

10.1155/2019/7198693 ◽

2019 ◽

Vol 2019 ◽

pp. 1-26

Author(s):

Jianming Ding ◽

Zhaoheng Zhang ◽

Yanli Yin

Keyword(s):

Fault Detection ◽

High Speed ◽

Real Life ◽

High Speed Train ◽

Impulse Responses ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Convolution Model ◽

High Speed Trains ◽

Bench Testing

Wheelset bearings are crucial mechanical components of high-speed trains. Wheelset-bearing fault detection is of great significance to ensure the safety of high-speed train service. Convolution sparse representations (CSRs) provide an excellent framework for extracting impulse responses induced by bearing faults. However, the performance of CSR on extracting impulse responses is fairly sensitive to inappropriate selection of method-related parameters, and a convolution model for representing the impulse responses has not been discussed. In view of these two unsolved problems, a convolutional representation model of the impulse response series is developed. A novel fault detection method, named adaptive CSR (ACSR), is then proposed based on combinations of CSR and methods for estimating three parameters related to CSR. Finally, the effectiveness of the proposed ACSR method is validated via simulation, bench testing, and a real-life running test employing a high-speed train.

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