A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction
Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extr...
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| Format: | Article |
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MDPI AG
2020-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/1/368 |
| _version_ | 1819171074566258688 |
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| author | Hung-Cuong Trinh Yung-Keun Kwon |
| author_facet | Hung-Cuong Trinh Yung-Keun Kwon |
| author_sort | Hung-Cuong Trinh |
| collection | DOAJ |
| description | Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems. |
| first_indexed | 2024-12-22T19:45:30Z |
| format | Article |
| id | doaj.art-f0d7e3ac5d9a448598cb0e8005357496 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-22T19:45:30Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f0d7e3ac5d9a448598cb0e80053574962022-12-21T18:14:42ZengMDPI AGApplied Sciences2076-34172020-01-0110136810.3390/app10010368app10010368A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life PredictionHung-Cuong Trinh0Yung-Keun Kwon1Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh 758307, VietnamDepartment of Electrical/Electronic and Computer Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 680-749, KoreaMachinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems.https://www.mdpi.com/2076-3417/10/1/368data-drivendiagnosticsprognosticsgenetic algorithmensemblefault-typesremaining useful life |
| spellingShingle | Hung-Cuong Trinh Yung-Keun Kwon A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction Applied Sciences data-driven diagnostics prognostics genetic algorithm ensemble fault-types remaining useful life |
| title | A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction |
| title_full | A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction |
| title_fullStr | A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction |
| title_full_unstemmed | A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction |
| title_short | A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction |
| title_sort | data independent genetic algorithm framework for fault type classification and remaining useful life prediction |
| topic | data-driven diagnostics prognostics genetic algorithm ensemble fault-types remaining useful life |
| url | https://www.mdpi.com/2076-3417/10/1/368 |
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