Application of composite multi-scale attention entropy in damage identification of rotating machinery under multiple working conditions
Aiming at the problems of low model accuracy and poor noise resistance in the traditional damage identification methods for rotating machinery,a multi working condition damage identification method for rotating machinery based on composite multi-scale attention entropy(CMATE)and random forest(RF)was proposed.Firstly,a new nonlinear dynamic tool—composite multi-scale attention entropy,was proposed to measure the complexity of time series.Then,the damage characteristics of vibration signals of rotating machinery were extracted by CMATE to characterize the dynamic characteristics of rotating machinery under different working conditions.Then,the damage features were input into a multi category classifier based on random forest to identify the damage.Finally,three types of rotating machinery datasets,rolling bearings-gearboxes,gearboxes and centrifugal pumps,were used to construct multi-scale condition damage datasets for 9 working conditions and 20 working conditions respectively,and experimental research was conducted on the damage identification method.The results show that the method achieves the recognition accuracy of 95%,97%and 100%respectively,which is superior to other nonlinear dynamics tools in accuracy and feature extraction efficiency.Moreover,it still achieves good damage recognition results under the noise interference of 0 dB,1 dB,2 dB and 3 dB with different signal-to-noise ratios,which proves that the model has remarkable anti-noise property.At the same time,the damage identification method can stably identify the damage of rotating machinery under different loads and speeds,with an average recognition accuracy of 97.2%and 93.5%,respectively,which has application potential.
万方数据
维普
