Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift‐Invariant Dictionary Learning

Abstract

A wheelset bearing is a crucial energy transmission element in high-speed trains. Any parts of the wheelset bearing that have faults may endanger the safety of the railway service. Therefore, it is important to monitor the running condition of a wheelset bearing. The multifault on a wheelset bearing is very common, and these impulsive components generated by different types of faults may interact with each other, which increases the difficulty of entirely identifying those faults. To solve the multifault problem, this paper proposed a hierarchical shift-invariant K-means singular value decomposition (H-SI-K-SVD) to hierarchically separate those multifault impulsive components based on their fault power levels. Each of the separated impulse signals contains only one fault impulse, and the fault information could be highlighted both in time domain and frequency domain. In addition, the sparsity of envelope spectrum (SES) is introduced as an indicator to adaptively tune a key parameter in this method. The effectiveness of the proposed method is verified by both simulation and experimental signals. Compared with ensemble empirical model decomposition (EEMD), the proposed method exhibits better performance in separating the multifault impulsive components and detecting the faults of a wheelset bearing.

A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering

“…Here, x 0 (t) is directly written out, as shown in (24). In addition, η(t) denotes noise component of x(t), which can be described by Gaussian random noise, whose standard deviation σ 0 can be set to 1. e power ratio between the impulse component I(t) and the total noisy simulated signal x(t) is − 18.7008 dB:…”

“…In the traditional sparse representation, the power levels of different features will affect the results extracted by the sparse representation [24]. When the energy of interference, usually expressed as the nonstationary component, is stronger than the energy of fault features, it will detect and extract the interference component instead of the fault features.…”