Integrating spatiotemperporal features into fault prediction using a multi-dimensional method
This study proposes a method to validate multidimensional fault prediction models. It integrates vibration and current data, analyzes spatiotemporal characteristics, and uses support vector machines and random forest algorithms to analyze fault characteristics. The short-time Fourier transform is us...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025019279 |
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| author | Chun-Yi Lin Yu-Chuan Tseng Wu-Sung Yao |
| author_facet | Chun-Yi Lin Yu-Chuan Tseng Wu-Sung Yao |
| author_sort | Chun-Yi Lin |
| collection | DOAJ |
| description | This study proposes a method to validate multidimensional fault prediction models. It integrates vibration and current data, analyzes spatiotemporal characteristics, and uses support vector machines and random forest algorithms to analyze fault characteristics. The short-time Fourier transform is used to convert spatiotemporal data into the frequency domain for classification, and high-order features are extracted through convolutional networks. The model considers three spatial dimensions and three vibration measurement sources to form a nine-dimensional data structure, and a fault prediction algorithm based on these dimensions is established. The study evaluates the model using metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic area under curve (ROC-AUC), and visualizes performance through confusion matrices, ROC curves, and precision-recall curves. In order to further verify the significance of the model, the experiment is conducted on mercury induction motors of industrial water pumps, and an McNemar’s Chi-Square Test was used to statistically test the fault discrimination ability of the model under various conditions. Results show that this model achieves the stability and accuracy of the fault prediction under multi-dimensional data, effectively identifies abnormal conditions, and the technical support for smart monitoring and maintenance can be provided. |
| format | Article |
| id | doaj-art-73a2ab1af8bd4d4ca6faab3049e06989 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-73a2ab1af8bd4d4ca6faab3049e069892025-08-20T03:15:54ZengElsevierResults in Engineering2590-12302025-09-012710585610.1016/j.rineng.2025.105856Integrating spatiotemperporal features into fault prediction using a multi-dimensional methodChun-Yi Lin0Yu-Chuan Tseng1Wu-Sung Yao2National Kaohsiung University of science and technology, Department of Mechatronics Engineering; No. 1, Daxue Rd., Yanchao Dist., Kaohsiung City 824005, TaiwanNational Kaohsiung University of science and technology, Department of Mechatronics Engineering; No. 1, Daxue Rd., Yanchao Dist., Kaohsiung City 824005, TaiwanCorresponding author.; National Kaohsiung University of science and technology, Department of Mechatronics Engineering; No. 1, Daxue Rd., Yanchao Dist., Kaohsiung City 824005, TaiwanThis study proposes a method to validate multidimensional fault prediction models. It integrates vibration and current data, analyzes spatiotemporal characteristics, and uses support vector machines and random forest algorithms to analyze fault characteristics. The short-time Fourier transform is used to convert spatiotemporal data into the frequency domain for classification, and high-order features are extracted through convolutional networks. The model considers three spatial dimensions and three vibration measurement sources to form a nine-dimensional data structure, and a fault prediction algorithm based on these dimensions is established. The study evaluates the model using metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic area under curve (ROC-AUC), and visualizes performance through confusion matrices, ROC curves, and precision-recall curves. In order to further verify the significance of the model, the experiment is conducted on mercury induction motors of industrial water pumps, and an McNemar’s Chi-Square Test was used to statistically test the fault discrimination ability of the model under various conditions. Results show that this model achieves the stability and accuracy of the fault prediction under multi-dimensional data, effectively identifies abnormal conditions, and the technical support for smart monitoring and maintenance can be provided.http://www.sciencedirect.com/science/article/pii/S2590123025019279Fault predictionSupport vector machineRandom forestShort-time Fourier transformConvolutional network |
| spellingShingle | Chun-Yi Lin Yu-Chuan Tseng Wu-Sung Yao Integrating spatiotemperporal features into fault prediction using a multi-dimensional method Results in Engineering Fault prediction Support vector machine Random forest Short-time Fourier transform Convolutional network |
| title | Integrating spatiotemperporal features into fault prediction using a multi-dimensional method |
| title_full | Integrating spatiotemperporal features into fault prediction using a multi-dimensional method |
| title_fullStr | Integrating spatiotemperporal features into fault prediction using a multi-dimensional method |
| title_full_unstemmed | Integrating spatiotemperporal features into fault prediction using a multi-dimensional method |
| title_short | Integrating spatiotemperporal features into fault prediction using a multi-dimensional method |
| title_sort | integrating spatiotemperporal features into fault prediction using a multi dimensional method |
| topic | Fault prediction Support vector machine Random forest Short-time Fourier transform Convolutional network |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025019279 |
| work_keys_str_mv | AT chunyilin integratingspatiotemperporalfeaturesintofaultpredictionusingamultidimensionalmethod AT yuchuantseng integratingspatiotemperporalfeaturesintofaultpredictionusingamultidimensionalmethod AT wusungyao integratingspatiotemperporalfeaturesintofaultpredictionusingamultidimensionalmethod |