Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal Fusion

Yahui Zhang and Kai Yang
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Yahui Zhang: School of Electrical and Electronics Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Kai Yang: School of Electrical and Electronics Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2022, vol. 15, issue 3, 1-13

Abstract: As an important production equipment of the offshore platform, the operation reliability of submersible motors is critical to oil and gas production, natural gas energy supplies, and social and economic benefits, etc. In order to realize the health management and fault diagnosis of submersible motors, a motor fault-monitoring method based on multi-signal fusion is proposed. The current signals and vibration signals were selected as characteristic signals. Through fusion correlation analysis, the correlation between different signals was established to enhance the amplitude at the same frequency, so as to highlight the motor fault characteristic frequency components, reduce the difficulty of fault identification, and provide sample data for motor fault pattern identification. Furthermore, the wavelet packet node energy analysis and back propagation neural network were combined to identify the motor faults and realize the real-time monitoring of the operating status of the submersible motor. The genetic algorithm was used to optimize the parameters of the neural network model to improve the accuracy of motor fault pattern recognition. The results show that the combination of multi-signal fusion monitoring and an artificial intelligence algorithm can diagnose motor fault types with high confidence. This research originally proposed the fusion correlation spectrum technology, which solved the shortcomings of the small amplitude and complex composition of the single signal spectrum components in the fault diagnosis and improved the reliability of the fault diagnosis. It further combined the neural network to realize the automatic monitoring and intelligent diagnosis of submersible motors, which has certain application value and inspiration in the field of electrical equipment intelligent monitoring.

Keywords: submersible motor; fault diagnosis; multi-signal fusion; fusion correlation spectrum; neural network; genetic algorithm; pattern recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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