Research on Multi-Scale Filtering Method of Electromechanical Fault Signals under Stack Self-Encoder
Coal mine electromechanical fault signals are composed more complex,which leads the reduction of the quality of coal mine electromechanical fault diagnosis.In this regard,a multi-scale filtering method of coal mine electromechanical fault signals based on stack self-encoder is proposed.Firstly,the nonlinear time series and phase space reconstruction are used to construct the coal mine electromechanical fault function and collect the fault signal.Secondly,the stack self-encoder is designed by combining the cost function and sparse constraints,and the collected fault signals are input into the stack self-encoder to realize the classification of the fault signals.Finally,based on the classification results,the ensemble empirical modal decomposition(EEMD)is used to transform the fault non-smooth signals into smooth signals,and the multi-scale filtering is used to optimize the fault signals,in order to obtain the multi-scale mediation signals of the coal mine electromechanical failures,so as to realize the multi-scale filtering of coal mine electromechanical fault signals.After experimental verification,most frequency components are retained and unwanted frequency components are filtered out at the same time after multi-scale filtering of the fault signal by the proposed method.The average value of coal mine electromechanical fault diagnosis accuracy is 99.71％,and the average value of diagnosis time is 0.24 s.This method can realize accurate and fast fault diagnosis of coal mine electromechanical.
NETL
NSTL
万方数据
