A learning-based method for speed sensor fault diagnosis of induction motor drive systems
This article proposes a speed sensor fault diagnosis methodology based on a learning-based data-driven principle in induction motor drive systems. The proposed method is derived from signal estimation and residual evaluation. First, a speed estimator is designed with a nonlinear autoregressive exoge...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/163775 |
| _version_ | 1826115142590398464 |
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| author | Xia, Yang Xu, Yan Gou, Bin Deng, Qingli |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Xia, Yang Xu, Yan Gou, Bin Deng, Qingli |
| author_sort | Xia, Yang |
| collection | NTU |
| description | This article proposes a speed sensor fault diagnosis methodology based on a learning-based data-driven principle in induction motor drive systems. The proposed method is derived from signal estimation and residual evaluation. First, a speed estimator is designed with a nonlinear autoregressive exogenous (NARX) learning model and a randomized learning technique called random vector functional link (RVFL) network. A data preprocessing method by discrete wavelet transform (DWT) is applied to better trace the signal trends, in order to further improve the speed estimation accuracy. After the estimation, the residual between the measured and estimated signals can be obtained, and a decision-making mechanism is developed for fault diagnosis based on an outlier test. The offline test results show that the proposed method can accurately estimate the speed signal with a 1.554e -4 root-mean-square error (RMSE) and outperforms the state-of-the-art methods. Moreover, real-time tests are also carried out to verify the feasibility and stability during the online stage. Moreover, the proposed approach does not require any motor parameters and other additional hardware, which makes it quite suitable for online practical applications. |
| first_indexed | 2024-10-01T03:50:43Z |
| format | Journal Article |
| id | ntu-10356/163775 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:50:43Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1637752022-12-16T05:24:51Z A learning-based method for speed sensor fault diagnosis of induction motor drive systems Xia, Yang Xu, Yan Gou, Bin Deng, Qingli School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Discrete Wavelet Transforms Fault Diagnosis This article proposes a speed sensor fault diagnosis methodology based on a learning-based data-driven principle in induction motor drive systems. The proposed method is derived from signal estimation and residual evaluation. First, a speed estimator is designed with a nonlinear autoregressive exogenous (NARX) learning model and a randomized learning technique called random vector functional link (RVFL) network. A data preprocessing method by discrete wavelet transform (DWT) is applied to better trace the signal trends, in order to further improve the speed estimation accuracy. After the estimation, the residual between the measured and estimated signals can be obtained, and a decision-making mechanism is developed for fault diagnosis based on an outlier test. The offline test results show that the proposed method can accurately estimate the speed signal with a 1.554e -4 root-mean-square error (RMSE) and outperforms the state-of-the-art methods. Moreover, real-time tests are also carried out to verify the feasibility and stability during the online stage. Moreover, the proposed approach does not require any motor parameters and other additional hardware, which makes it quite suitable for online practical applications. This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 51907163 and Grant U1934204. 2022-12-16T05:24:51Z 2022-12-16T05:24:51Z 2021 Journal Article Xia, Y., Xu, Y., Gou, B. & Deng, Q. (2021). A learning-based method for speed sensor fault diagnosis of induction motor drive systems. IEEE Transactions On Instrumentation and Measurement, 71, 3504410-. https://dx.doi.org/10.1109/TIM.2021.3132053 0018-9456 https://hdl.handle.net/10356/163775 10.1109/TIM.2021.3132053 2-s2.0-85120552830 71 3504410 en IEEE Transactions on Instrumentation and Measurement © 2021 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Discrete Wavelet Transforms Fault Diagnosis Xia, Yang Xu, Yan Gou, Bin Deng, Qingli A learning-based method for speed sensor fault diagnosis of induction motor drive systems |
| title | A learning-based method for speed sensor fault diagnosis of induction motor drive systems |
| title_full | A learning-based method for speed sensor fault diagnosis of induction motor drive systems |
| title_fullStr | A learning-based method for speed sensor fault diagnosis of induction motor drive systems |
| title_full_unstemmed | A learning-based method for speed sensor fault diagnosis of induction motor drive systems |
| title_short | A learning-based method for speed sensor fault diagnosis of induction motor drive systems |
| title_sort | learning based method for speed sensor fault diagnosis of induction motor drive systems |
| topic | Engineering::Electrical and electronic engineering Discrete Wavelet Transforms Fault Diagnosis |
| url | https://hdl.handle.net/10356/163775 |
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