A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis

Abstract

With the rapid development of high-speed railway, the fault diagnosis of railway vehicles has become more and more important for ensuring the operating safety. The MF is a nonlinear signal processing method which can extract the modulated faulty information via reshaping the analyzed signal. However, the choices of operators and structure elements (SE) are numerous and complicated to determine the best MF solution for different bearing faulty signals. In this paper, the particle swarm optimization (PSO) was introduced to optimize the effect of MF among several classical MF operators and different SE parameters. The proposed method applied PSO to select the best MF result with respect to the fitness function adopting kurtosis. A set of bearing signals with additional interference of wheel-track excitement are analyzed to verify the effectiveness of the proposed method. The results demonstrated that the proposed method is capable of obtaining the optimized solution and accurately extracting the fault information. Furthermore, the shaft rotation frequency and wheel-track interference were reduced 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,…”

“…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.…”

Novel Rotating Machinery Structural Faults Signal Adaptive Multiband Filtering and Automatic Diagnosis

“…Reference [12] has proposed an adaptive stochastic resonance method based on coupled bistable systems and improved its performance in diagnosing faults in rolling bearings, but this approach is particularly sensitive to filter parameters. Fifth, intelligent filtering methods, such as genetic algorithm and particle swarm algorithm filters [13], have been used in [14] (specifically particle swarm optimization) to optimize morphological filtering and subsequently reduced shaft rotation frequency and wheel-track interference. While its use does not require extensive experience, this method is computationally expensive and can easily fall into the local optima.…”

Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing

“…In literature [ 5 ], a fault features extraction scheme based on MED-ICEEMDAN, mutual information, and sample entropy was proposed for the head sheave bearing vibration signals. Huang et al [ 6 ] used the morphological filtering method to analyze the vibration signals whose operators and structure elements are optimized by the particle swarm algorithm to diagnose the faults of railway vehicle bearing. The adaptive multipoint optimal minimum entropy deconvolution adjusted method was proposed in the paper [ 7 ] to extract fault-related features from noisy vibration signals.…”