A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine
Abstract The scarcity of samples and disunity of feature inputs hinder the enhancement of transformer fault diagnosis performance, and there are mutual influences between model construction and feature selection, which cannot only consider a single process. Therefore, this study proposes a novel two...
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| Format: | Article |
| Language: | English |
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Wiley
2023-03-01
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| Series: | IET Electric Power Applications |
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| Online Access: | https://doi.org/10.1049/elp2.12270 |
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| author | Hanyu Shi Mingxia Chen |
| author_facet | Hanyu Shi Mingxia Chen |
| author_sort | Hanyu Shi |
| collection | DOAJ |
| description | Abstract The scarcity of samples and disunity of feature inputs hinder the enhancement of transformer fault diagnosis performance, and there are mutual influences between model construction and feature selection, which cannot only consider a single process. Therefore, this study proposes a novel two‐stage transformer fault diagnosis strategy, which includes a multi‐filter interactive feature selection method (MIFS) constructed, and a diagnosis model ASSA‐SVM based on the adaptive sparrow algorithm (ASSA) optimised support vector machine (SVM). Firstly, the proposed MIFS incorporates ReliefF and mRMR to establish a comprehensive criterion ReliefF‐mRMR for feature importance ranking, and then performs dimension‐by‐dimension input classifier interaction selection based on the ranking results to obtain the optimal feature subset. Secondly, ASSA was proposed to optimise the kernel parameters of SVM. A two‐stage integration model MIFS‐ASSA‐SVM was developed. Finally, Experiments were conducted using real fault data, and the diagnostic performance of different feature inputs, optimisation algorithms and classifiers were compared. The results show that the proposed method performs well on parameter optimisation, can dynamically and interactively select feature subsets with few dimensions and good generalisation performance, its overall diagnosis accuracy reached 92.47%, and the diagnosis performance of each fault type has good performance in multiple evaluation metrics. |
| format | Article |
| id | doaj-art-e874b19b03244ed7a3e48d8fca6ff44a |
| institution | DOAJ |
| issn | 1751-8660 1751-8679 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Electric Power Applications |
| spelling | doaj-art-e874b19b03244ed7a3e48d8fca6ff44a2025-08-20T03:11:24ZengWileyIET Electric Power Applications1751-86601751-86792023-03-0117334135710.1049/elp2.12270A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machineHanyu Shi0Mingxia Chen1School of Mechanical and Control Engineering Guilin University of Technology Guilin Guangxi ChinaSchool of Mechanical and Control Engineering Guilin University of Technology Guilin Guangxi ChinaAbstract The scarcity of samples and disunity of feature inputs hinder the enhancement of transformer fault diagnosis performance, and there are mutual influences between model construction and feature selection, which cannot only consider a single process. Therefore, this study proposes a novel two‐stage transformer fault diagnosis strategy, which includes a multi‐filter interactive feature selection method (MIFS) constructed, and a diagnosis model ASSA‐SVM based on the adaptive sparrow algorithm (ASSA) optimised support vector machine (SVM). Firstly, the proposed MIFS incorporates ReliefF and mRMR to establish a comprehensive criterion ReliefF‐mRMR for feature importance ranking, and then performs dimension‐by‐dimension input classifier interaction selection based on the ranking results to obtain the optimal feature subset. Secondly, ASSA was proposed to optimise the kernel parameters of SVM. A two‐stage integration model MIFS‐ASSA‐SVM was developed. Finally, Experiments were conducted using real fault data, and the diagnostic performance of different feature inputs, optimisation algorithms and classifiers were compared. The results show that the proposed method performs well on parameter optimisation, can dynamically and interactively select feature subsets with few dimensions and good generalisation performance, its overall diagnosis accuracy reached 92.47%, and the diagnosis performance of each fault type has good performance in multiple evaluation metrics.https://doi.org/10.1049/elp2.12270feature selectionsparrow algorithmsupport vector machinetransformer fault diagnosis |
| spellingShingle | Hanyu Shi Mingxia Chen A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine IET Electric Power Applications feature selection sparrow algorithm support vector machine transformer fault diagnosis |
| title | A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine |
| title_full | A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine |
| title_fullStr | A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine |
| title_full_unstemmed | A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine |
| title_short | A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine |
| title_sort | two stage transformer fault diagnosis method based multi filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine |
| topic | feature selection sparrow algorithm support vector machine transformer fault diagnosis |
| url | https://doi.org/10.1049/elp2.12270 |
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