Composite Fault Diagnosis of Water-jet Pump Based on Deep Learning Information Fusion
As the core driving component of ship navigation,the water-jet pump faces chal-lenges in monitoring and diagnosing important components such as bearings and impellers under complex working conditions and harsh environments,which in turn affects the normal operation of the ship.A deep learning fault diagnosis method based on improved soft voting multi-source information fusion is proposed to address the diagnostic challenges of composite faults such as damage to spray pump bearings and impeller scraping under complex working condi-tions.Firstly,using the FFT method,the time-domain vibration signals of the guide vane casing and impeller casing are respectively transformed into frequency-domain signals,and the signals are input into the Yu norm based deep metric learning model and deep confidence model for diag-nostic analysis to obtain preliminary diagnostic results respectively.Then,using the improved soft voting method,these preliminary diagnostic results are fused to obtain the final diagnostic result.Finally,a fault simulation is conducted on the water-jet pump guide vane bearing,impeller scraping and other composite faults through a test bench,and the proposed diagnostic method is used for fault analysis.The results show that the diagnostic accuracy of this proposed method is 99.667％,which is significantly better than that of the diagnostic model based on single sensor signal and multi-source information fusion based on soft voting.
water-jet pumpmulti-source information fusionimproved soft voting methoddeep learningcomposite fault diagnosis
NETL
NSTL
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