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

Abstract

The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing. However, it is difficult for traditional sparse representation to extract fault features ideally when some strong interference components are imposed on the signal. Therefore, this paper proposes a novel feature extraction method of wheelset-bearing fault based on the wavelet sparse representation with adaptive local iterative filtering. In this method, the adaptive local iterative filtering reduces the impact of interference components effectively and contributes to the extraction of sparse impulses. The wavelet sparse representation, which adopts L1-regularized optimization for a globally optimal solution in sparse coding, extracts intrinsic features of fault in the wavelet domain. To validate the effectiveness of this proposed method, both simulated signals and experimental signals are analyzed. The results show that the fault features of wheelset-bearing are sufficiently extracted by the proposed method.

A Novel Blind Deconvolution Method with Adaptive Period Estimation Technique and Its Application to Fault Feature Enhancement of Bearing

“…is constraint range can be used by APE to estimate the period, avoiding interference from other local maximums. e steps of constraint construction are as follows: Firstly, the theoretical calculation period T is calculated based on [6,36,37]. Secondly, based on the random fluctuation range in [28,29], in order to extend the application range of the proposed method, 5% random fluctuation is used in this paper to construct this constraint,…”

“…Many advanced fault detection methods have been proposed and applied in engineering practice. ese methods can be divided into several categories, including spectral kurtosis [1][2][3], sparse representation [4][5][6], cyclostationarity methods [7][8][9][10], signal mode decomposition methods [11][12][13], deep learning [14,15], and blind deconvolution (BD) [16][17][18][19]. BD methods can recover fault impulse adaptively from noise observation, which has been developed and promoted in fault diagnosis.…”

“…However, when the SNR is very low, especially when there are concurrent faults in the vibration signal [34], APE might fail to detect the real fault period, which will cause MCG-Lp/Lq-D to converge to the incorrect result [34,35]. Actually, in most engineering practice, the fault period can be obtained by theoretical calculation according to the rotation speed and the component geometry parameter [6,36,37]. Although the rotation speed fluctuation and the measurement error of component geometry parameter might cause the difference between the theoretical calculation value and the real value of the fault impulse period, it is an approximate estimation of the real fault period.…”

Digital Twin Inspired Intelligent Bearing Fault Diagnosis Method Based on Adaptive Correlation Filtering and Improved SAE Classification Model

“…At present, many studies have focused on bearing fault diagnosis [3][4][5], and the use of vibration signal as a diagnostic signal is one of the main methods. e key steps of the diagnosis method based on vibration signal include the extraction of the signal characteristics in the time and frequency domains or time-frequency domain.…”

Impulse Signal Detection for Bearing Fault Diagnosis via Residual-Variational Mode Decomposition

“…Rotation equipment has been widely used in modern industries, such as land transportation, ocean shipping, airlift, and some power transmission fields [1][2][3][4]. Although its strength and lifespan have been considered in the design stage, heavy loads and a terrible operating environment may accelerate the operating area deterioration (i.e., the interface between rolling elements and raceways in bearing and the meshing surface of two gears).…”