Application of translation invariant wavelet de-nosing to axle of railway vehicles fault diagnosis online

Some important information pertaining to blade fault is thought to be concealed in highly unsteady casing vibration. This paper explores suitable methods to best reconstruct blade related signals from raw casing vibration, which could be used for diagnosis of blade fault. The feasibility of translation invariant wavelet transform and cycle spinning (TIWT-CS) technique in reconstruction of these signals is investigated in this paper. Subsequently, a new parameter for blade fault diagnosis, namely, the energy profile of blade signal (EPBS), is formulated. Experimental results show that TIWT-CS method effectively retained blade related signals, while other unwanted signals such as system noises and aerodynamic induced vibration are reasonably suppressed. EPBS provides an indication of the condition of blade faults in rotor system, whereby the exact position and the quantity of faulty blades, as well as the root cause of blade fault, can be identified. In comparison, the energy profile plots using unfiltered casing vibration were found to be highly unstable and therefore provides inconsistent results for diagnosis of blade fault.

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Fault Diagnosis of Rolling Bearing of Railway Vehicles Based on Correlation Dimension

Lecture Notes in Electrical Engineering - Innovative Computing ◽

10.1007/978-981-16-4258-6_64 ◽

2022 ◽

pp. 515-520

Author(s):

Hang Zhang ◽

Haijun Cui

Keyword(s):

Fault Diagnosis ◽

Correlation Dimension ◽

Rolling Bearing ◽

Railway Vehicles

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A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction

Shock and Vibration ◽

10.1155/2016/5714195 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 26

Author(s):

Yongjian Li ◽

Weihua Zhang ◽

Qing Xiong ◽

Tianwei Lu ◽

Guiming Mei

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Stable Distribution ◽

Small Scale ◽

Vibration Signals ◽

Railway Vehicles ◽

Alpha Stable Distribution ◽

Fault Type ◽

Diagnosis Model

Axle box bearings are the most critical mechanical components of railway vehicles. Condition monitoring is of great benefit to ensure the healthy status of bearings in the railway train. In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM) using vibration signals is proposed which is conducted in three main steps. Firstly, fast nonlocal means is used for denoising and ensemble empirical mode decomposition is applied to extract fault feature information. Then a new statistical method of feature extraction, symmetric alpha-stable distribution, is employed to obtain representative features from intrinsic mode functions. Additionally, the hybrid fault feature sets are input into LS-SVM to identify the fault type. To enhance the performance of LS-SVM in the case of small-scale samples, Morlet wavelet kernel function is combined with LS-SVM for the classification of fault type and fault severity and the particle swarm optimization is used for the optimization of LS-WSVM parameters. Finally, the experimental results demonstrate that the proposed approach performs more effectively and robustly than the other methods in small-scale samples for fault detection and classification of railway vehicle bearings.

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