Data-driven predictive maintenance research on hydraulic motor
Predictive maintenance is one of the typical applications of digital twin,and data-driven is the main way to realize predictive maintenance.Aiming at the problems of difficult fault feature extraction and large deviation of prediction results in predictive maintenance,a predictive maintenance model building method based on the combination of VMD(variational mode decomposition)algorithm and HHT(Hilbert-Huang transform)algorithm was proposed.The time-domain features of the vibration signal were extracted by VMD+HHT algorithm,the data dimension was reduced by combining deep sparse auto-encoder(DSAE),support vector data description(SVDD)algorithm was used to form a health index curve,and a predictive maintenance model was established based on long short-term memory(LSTM)algorithm.The method was applied to the predictive maintenance of a hydraulic motor of a rotary drilling rig.The vibration signal of the motor housing was extracted,the predictive maintenance model of the hydraulic motor was constructed,and the validity and accuracy of the method were verified by test.The test results showed that adopting the predictive maintenance model based on DSAE+SVDD+LSTM algorithm could avoid the problems of mode aliasing and endpoint effect,the prediction accuracy could reach more than 90%,and the model had practical value.The research results can provide important reference for the construction of hydraulic component digital twin predictive maintenance application scenarios.
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