Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
Artificial intelligence has succeeded in many different areas in recent years. Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. This paper explores a case study of applying machine learning techniques and neural networks to detect ten d...
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MDPI AG
2024-10-01
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| Online Access: | https://www.mdpi.com/1999-4893/17/10/441 |
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| author | Harry Hoa Huynh Cheol-Hong Min |
| author_facet | Harry Hoa Huynh Cheol-Hong Min |
| author_sort | Harry Hoa Huynh |
| collection | DOAJ |
| description | Artificial intelligence has succeeded in many different areas in recent years. Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. This paper explores a case study of applying machine learning techniques and neural networks to detect ten different machinery fault conditions using publicly available data sets collected from a tachometer, two accelerometers, and a microphone. Ten different conditions were classified using machine learning algorithms. Fifty-eight different features are extracted from time and frequency by applying the Short-Time Fourier Transform to the data with the window size of 1000 samples with 50% overlap. The Support Vector Machine models provided fault classification with 99.8% accuracy using all fifty-eight features. The proposed study explores the dimensionality reduction of the extracted features. Fifty-eight features were ranked using the Decision Tree model to identify the essential features as the classifier predictors. Based on feature extraction and raking, eleven predictors were extracted leading to reduced training complexity, while achieving a high classification accuracy of 99.7% could be obtained in less than half of the training time. |
| format | Article |
| id | doaj-art-b06da2ec513444cca6bcd6cf59683ed8 |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-b06da2ec513444cca6bcd6cf59683ed82025-08-20T02:11:11ZengMDPI AGAlgorithms1999-48932024-10-01171044110.3390/a17100441Rotating Machinery Fault Detection Using Support Vector Machine via Feature RankingHarry Hoa Huynh0Cheol-Hong Min1Electrical and Computer Engineering, University of St. Thomas, St. Paul, MN 55105, USAElectrical and Computer Engineering, University of St. Thomas, St. Paul, MN 55105, USAArtificial intelligence has succeeded in many different areas in recent years. Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. This paper explores a case study of applying machine learning techniques and neural networks to detect ten different machinery fault conditions using publicly available data sets collected from a tachometer, two accelerometers, and a microphone. Ten different conditions were classified using machine learning algorithms. Fifty-eight different features are extracted from time and frequency by applying the Short-Time Fourier Transform to the data with the window size of 1000 samples with 50% overlap. The Support Vector Machine models provided fault classification with 99.8% accuracy using all fifty-eight features. The proposed study explores the dimensionality reduction of the extracted features. Fifty-eight features were ranked using the Decision Tree model to identify the essential features as the classifier predictors. Based on feature extraction and raking, eleven predictors were extracted leading to reduced training complexity, while achieving a high classification accuracy of 99.7% could be obtained in less than half of the training time.https://www.mdpi.com/1999-4893/17/10/441artificial intelligencemachine learningfault detectionsupport vector machine (SVM)feature extractiondecision tree |
| spellingShingle | Harry Hoa Huynh Cheol-Hong Min Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking Algorithms artificial intelligence machine learning fault detection support vector machine (SVM) feature extraction decision tree |
| title | Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking |
| title_full | Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking |
| title_fullStr | Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking |
| title_full_unstemmed | Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking |
| title_short | Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking |
| title_sort | rotating machinery fault detection using support vector machine via feature ranking |
| topic | artificial intelligence machine learning fault detection support vector machine (SVM) feature extraction decision tree |
| url | https://www.mdpi.com/1999-4893/17/10/441 |
| work_keys_str_mv | AT harryhoahuynh rotatingmachineryfaultdetectionusingsupportvectormachineviafeatureranking AT cheolhongmin rotatingmachineryfaultdetectionusingsupportvectormachineviafeatureranking |