Gate Fault Diagnosis and Analysis Based on 1DCNN-LSTM Neural Network Model
Aiming at the problem of safety accidents caused by gate faults,a 1DCNN-LSTM fault diagnosis model is proposed.The proposed method combines the ability of spatial feature extraction and temporal feature to understand the feature information contained in the signal more comprehensively.The working state of the gate can be detected more efficiently,and the task of gate fault diagnosis can be completed.The results show that the classification accuracy of the proposed method reaches 93.7％,and the comprehensive performance is fine,which has significant advantages over the comparison model,and fully proves its effectiveness.
Hydraulic gateFault diagnosisConvolutional networksInternet of Things
NETL
NSTL
万方数据
