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FAULT DIAGNOSIS METHOD OF RECIPROCATING MACHINERY BASED ON KEYPHASOR-FREE COMPLETE-CYCLE SIGNAL
FAULT DIAGNOSIS METHOD OF RECIPROCATING MACHINERY BASED ON KEYPHASOR-FREE COMPLETE-CYCLE SIGNAL
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机译:基于远程关键词完整周期信号的往复机械故障诊断方法
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摘要
The present disclosure relates to a fault diagnosis method of a reciprocating machinery based on a keyphasor-free complete-cycle signal. The method includes the following steps: 1) building a complete-cycle vibration signal image library; 2) training an image recognition model; 3) acquiring a complete-cycle data on a keyphasor-free basis; 4) building an automatic feature extraction model; and 5) inputting a hidden layer feature of an autoencoder into a support vector machine (SVM) classifier to obtain a diagnosis result. By using a deep cascade convolutional neural network (CNN), the present disclosure achieves the goal of complete-cycle data acquisition on a keyphasor-free basis, solves the problems that traditional intelligent fault diagnosis relies on a keyphasor signal and real-time diagnosis fails due to insufficient installation space. In addition, by using an autoencoder for automatic feature extraction, the present disclosure avoids manual feature selection, reduces labor costs.
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