A new fault diagnosis method for wheelset-bearing system based on VME convergence tendency diagram

被引:5
作者
Li, Cuixing [1 ,2 ]
Liu, Yongqiang [1 ,3 ]
Liu, Zechao [1 ]
Liu, Wenpeng [1 ]
机构
[1] Shijiazhuang Tiedao Univ, State Key Lab Mech Behav & Syst Safety Traff Engn, Shijiazhuang 050043, Peoples R China
[2] Shijiazhuang Tiedao Univ, Sch Traff & Transportat, Shijiazhuang 050043, Peoples R China
[3] Shijiazhuang Tiedao Univ, Sch Mech Engn, Shijiazhuang 050043, Peoples R China
基金
中国国家自然科学基金;
关键词
wheelset-bearing system; self-adaptive variational mode extraction; initial center frequency; penalty factor; VME convergence tendency diagram; EMPIRICAL MODE DECOMPOSITION; CORRELATED KURTOSIS DECONVOLUTION; SPECTRAL KURTOSIS; ADAPTIVE VMD; DEMODULATION; EXTRACTION; SIGNALS; BAND;
D O I
10.1088/1361-6501/accc9f
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Aiming at the difficulty of accurate diagnosis of wheelset-bearing system composite faults, a multi-fault feature extraction method based on self-adaptive variational mode extraction (SAVME) was proposed. Variational mode extraction (VME) can extract a specific sub-signal from a multi-component signal. The key to the success of this algorithm is to determine appropriate initial parameters in advance, including initial center frequency (ICF) and penalty factor. To determine the key parameters of VME adaptively, the convergence characteristics of VME are analyzed deeply, and the VME convergence tendency diagram is proposed creatively according to the trend of the iterative curve of the center frequency of the desired mode. By analyzing the test signal with the VME convergence tendency diagram, the number of main latent sub-signals in the test signal and the ICF of each sub-signal corresponding to the VME can be determined efficiently. Then, according to the position of the ICF of each sub-signal in the frequency domain, the empirical formula of the penalty factor is used to quickly obtain the appropriate penalty factor. The proposed SAVME method not only improves the parameter selection adaptability of the traditional VME algorithm but also extends the VME algorithm to the field of multi-fault diagnosis. By analyzing the simulated signal and two experimental signals, the effectiveness of the SAVME algorithm is verified. Compared with the fast kurtogram method and the adaptive variational mode decomposition method, the proposed method is more accurate and superior in the multi-fault feature extraction of the wheelset-bearing system.
引用
收藏
页数:21
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