Fault diagnosis of traffic electromechanical bearings based on domain-weighted sparsity
Bearing is one of the core components of traffic electromechanical equipment that most often fails,and once the failure occurs,the whole system will be shut down,and even lead to catastrophic consequences,therefore,it is of great significance to carry out the fault detection of traffic electromechanical bearings.Aiming at the problem of difficult extraction of impulse features of traffic electromechanical bearings under low signal-to-noise ratio conditions,a fault diagnosis method of traffic electromechanical bearings based on domain-weighted sparse was proposed.Firstly,based on the classical sparse model,the Gini index was introduced as the weight to distinguish the contribution of sparse coefficients of each signal component;secondly,considering the correlation between the coefficients,the domain coefficients denoising was taken as an alternative to the threshold noise reduction method,to improve the accuracy of the estimation of the sparse reconstructed components;lastly,envelope detection was carried out on the reconstructed sparse signals to identify the faults.The validity of the proposed method was verifiy by using the simulated signals of electromechanical bearing faults and the measured signals of underground axle-box bearings,and the results show that the propose method could effectively achieve the weak fault feature extraction of traffic electromechanical bearings.
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