Abstract
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.
Highlights
Wheelset bearing is a critical and damaged component of a high-speed train
A novel bearing fault detection method, adaptive Union of convolutional dictionary learning algorithm (UC-DLA) combined with bandwidth optimization (AUC-DLA-BO), is proposed. e 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. e 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
A novel fault detection method, named AUCDLA-OB, was proposed. e new method has the following innovations: (1) e bearing fault impulses are expressed as the convolution of the shock response and the location coefficients, in which the shock response is related to the resonance frequency and damping coefficient, and the location coefficients characterize the time location and intensity of the impulses
Summary
Wheelset bearing is a critical and 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. e 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. E 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. Dictionary learning is affected by foundation vibration and harmonic interference and cannot learn the key structure related to fault impulses. Erefore, a powerful wheelset bearing fault diagnosis technique is very important to avoid catastrophic failures in high-speed trains. Sparse representation based on analytic dictionary might fail to capture fault features in the target signal. Many advanced and effective dictionary learning methods such as K-SVD [14], shiftinvariant sparse coding-based feature-sign search (SISCFSS) [17], shift-invariant K-SVD (SI-K-SVD) [21], and convolutional sparse coding based on ADMM (CSCADMM) [22] have been proposed and successfully applied to the learning of bearing fault feature structures. Sparse representations based on learning dictionary have been applied successfully in the field of fault detection, there are still two major shortcomings which are difficult to solve in practical engineering application:
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
