Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal Fusion
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 motor...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/3/756 |
| _version_ | 1827660851360825344 |
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| author | Yahui Zhang Kai Yang |
| author_facet | Yahui Zhang Kai Yang |
| author_sort | Yahui Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:59:18Z |
| format | Article |
| id | doaj.art-71d0be6868ca4bf9864d4a46d9940d82 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T23:59:18Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-71d0be6868ca4bf9864d4a46d9940d822023-11-23T16:19:22ZengMDPI AGEnergies1996-10732022-01-0115375610.3390/en15030756Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal FusionYahui Zhang0Kai Yang1School of Electrical and Electronics Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Electrical and Electronics Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaAs 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.https://www.mdpi.com/1996-1073/15/3/756submersible motorfault diagnosismulti-signal fusionfusion correlation spectrumneural networkgenetic algorithm |
| spellingShingle | Yahui Zhang Kai Yang Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal Fusion Energies submersible motor fault diagnosis multi-signal fusion fusion correlation spectrum neural network genetic algorithm |
| title | Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal Fusion |
| title_full | Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal Fusion |
| title_fullStr | Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal Fusion |
| title_full_unstemmed | Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal Fusion |
| title_short | Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal Fusion |
| title_sort | fault diagnosis of submersible motor on offshore platform based on multi signal fusion |
| topic | submersible motor fault diagnosis multi-signal fusion fusion correlation spectrum neural network genetic algorithm |
| url | https://www.mdpi.com/1996-1073/15/3/756 |
| work_keys_str_mv | AT yahuizhang faultdiagnosisofsubmersiblemotoronoffshoreplatformbasedonmultisignalfusion AT kaiyang faultdiagnosisofsubmersiblemotoronoffshoreplatformbasedonmultisignalfusion |