An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system
Three-phase pulsewidth modulation inverter fed induction motor drive system is widely applied in high power drive applications. Sensor faults are very common in the drive system, which, once occur, might result in degraded system performance or even system shutdown. In order to rapidly and accuratel...
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2019
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| Online Access: | https://hdl.handle.net/10356/107573 http://hdl.handle.net/10220/50320 http://dx.doi.org/10.1109/TIE.2018.2880719 |
| _version_ | 1811689431118118912 |
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| author | Gou, Bin Xu, Yan Xia, Yang Wilson, Gary Liu, Shuyong |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Gou, Bin Xu, Yan Xia, Yang Wilson, Gary Liu, Shuyong |
| author_sort | Gou, Bin |
| collection | NTU |
| description | Three-phase pulsewidth modulation inverter fed induction motor drive system is widely applied in high power drive applications. Sensor faults are very common in the drive system, which, once occur, might result in degraded system performance or even system shutdown. In order to rapidly and accurately diagnose the sensor faults, this paper proposes an intelligent time-adaptive data-driven method to identify the fault location and fault type of sensors in the drive system. An emerging machine learning technology named extreme learning machine (ELM) is applied to learn the sensor fault dataset; an ensemble ELM classifier is then designed to improve diagnostic accuracy, based on which a time-adaptive fault diagnosis process is proposed to achieve a high and balanced diagnostic accuracy and speed. As a data-driven method, the proposed method only employs the phase current, dc-link voltage, and speed signals as the inputs to the ensemble ELM classifiers and requires no additional sensors and other hardware. Simulated and experimental tests show that the proposed method can rapidly and accurately detect the fault sensor location and identify offset fault, stuck fault, and noise faults with an average diagnostic accuracy of 98% and the average decision time of 10 ms after the fault occurs. Moreover, such diagnosis method is robust to the fluctuation of catenary voltage and dc-link voltage, fault severity, and variation of model parameters, speed, and load. |
| first_indexed | 2024-10-01T05:47:59Z |
| format | Journal Article |
| id | ntu-10356/107573 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:47:59Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/1075732019-12-06T22:34:33Z An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system Gou, Bin Xu, Yan Xia, Yang Wilson, Gary Liu, Shuyong School of Electrical and Electronic Engineering Electrical Capability Group, Rolls-Royce Singapore Data-driven Method Engineering::Electrical and electronic engineering Inverters Three-phase pulsewidth modulation inverter fed induction motor drive system is widely applied in high power drive applications. Sensor faults are very common in the drive system, which, once occur, might result in degraded system performance or even system shutdown. In order to rapidly and accurately diagnose the sensor faults, this paper proposes an intelligent time-adaptive data-driven method to identify the fault location and fault type of sensors in the drive system. An emerging machine learning technology named extreme learning machine (ELM) is applied to learn the sensor fault dataset; an ensemble ELM classifier is then designed to improve diagnostic accuracy, based on which a time-adaptive fault diagnosis process is proposed to achieve a high and balanced diagnostic accuracy and speed. As a data-driven method, the proposed method only employs the phase current, dc-link voltage, and speed signals as the inputs to the ensemble ELM classifiers and requires no additional sensors and other hardware. Simulated and experimental tests show that the proposed method can rapidly and accurately detect the fault sensor location and identify offset fault, stuck fault, and noise faults with an average diagnostic accuracy of 98% and the average decision time of 10 ms after the fault occurs. Moreover, such diagnosis method is robust to the fluctuation of catenary voltage and dc-link voltage, fault severity, and variation of model parameters, speed, and load. Accepted version 2019-11-04T08:07:29Z 2019-12-06T22:34:33Z 2019-11-04T08:07:29Z 2019-12-06T22:34:33Z 2018 Journal Article Gou, B., Xu, Y., Xia, Y., Wilson, G., & Liu, S. (2019). An Intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system. IEEE Transactions on Industrial Electronics, 66(12), 9817-9827. doi:10.1109/TIE.2018.2880719 0278-0046 https://hdl.handle.net/10356/107573 http://hdl.handle.net/10220/50320 http://dx.doi.org/10.1109/TIE.2018.2880719 en IEEE Transactions on Industrial Electronics © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIE.2018.2880719. 10 p. application/pdf |
| spellingShingle | Data-driven Method Engineering::Electrical and electronic engineering Inverters Gou, Bin Xu, Yan Xia, Yang Wilson, Gary Liu, Shuyong An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system |
| title | An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system |
| title_full | An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system |
| title_fullStr | An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system |
| title_full_unstemmed | An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system |
| title_short | An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system |
| title_sort | intelligent time adaptive data driven method for sensor fault diagnosis in induction motor drive system |
| topic | Data-driven Method Engineering::Electrical and electronic engineering Inverters |
| url | https://hdl.handle.net/10356/107573 http://hdl.handle.net/10220/50320 http://dx.doi.org/10.1109/TIE.2018.2880719 |
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