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.
Highlights
In recent years, with the rapid development of high-speed railway all over the world, the occurrence of various kinds of railway accidents continuously increases [1]. e failure of railway vehicle often causes tremendous casualties and economic losses. erefore, the safety of the railway vehicle has gained more and more attention from the government, the industry, and the academia. e axle box bearing, which supports the weight of the vehicle and suffers various loads from the wheel set or other components of the bogie, is one of the key rolling components to guarantee the safety operation of the railway vehicle [2]. erefore, the fault diagnosis of axle box bearing is crucial for the operation safety of the railway vehicle [3]
E remaining contents of this paper are organized as follows: the principles of morphological filter (MF) and particle swarm optimization (PSO) are recalled in Section 2; the detail and procedures of the proposed method are introduced in Section 3; the simulation signal is analyzed by the proposed method in Section 4; some vibration signals of the axle box of the railway vehicle are provided to verify the effectiveness of the proposed method; the summary is drawn in the last section
To improve the performance of the morphological filter on diagnosing defects of railway vehicle bearing, the morphological filter based on particle swarm optimization was proposed. e main idea of the proposed method is firstly setting multiple dimensions to represent some typical operators and searching the optimal solutions of each dimension by the index of kurtosis and selecting the best solutions among the various dimensions. e selection scheme and diagnosis performance were verified by the analysis of a group of bearing fault signals added with wheel-track interference
Summary
With the rapid development of high-speed railway all over the world, the occurrence of various kinds of railway accidents continuously increases [1]. e failure of railway vehicle often causes tremendous casualties and economic losses. erefore, the safety of the railway vehicle has gained more and more attention from the government, the industry, and the academia. e axle box bearing, which supports the weight of the vehicle and suffers various loads from the wheel set or other components of the bogie, is one of the key rolling components to guarantee the safety operation of the railway vehicle [2]. erefore, the fault diagnosis of axle box bearing is crucial for the operation safety of the railway vehicle [3]. E purpose of this type of method is to narrow the analyzed frequency band and to help the further processes to perform better Another kind of approaches, such as envelope analysis [10], squared envelope analysis [11], fast spectral correlation [12], and cyclostationary analysis [13], focus on demodulating or revealing the faulty signal pattern based on the assumption that the impulses excited by the defect are generally considered as an amplitude modulated and quasiperiodic signal. The construction of SE is another significant factor that affects the performance of MF It would be complicated and time consuming to simultaneously select the optimal MF operator and SE when applying MF on new bearing fault signals. E remaining contents of this paper are organized as follows: the principles of MF and PSO are recalled in Section 2; the detail and procedures of the proposed method are introduced in Section 3; the simulation signal is analyzed by the proposed method in Section 4; some vibration signals of the axle box of the railway vehicle are provided to verify the effectiveness of the proposed method; the summary is drawn in the last section
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