CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things
Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/16/7040 |
| _version_ | 1827728690290622464 |
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| author | Zhenwen Gui Shuaishuai He Yao Lin Xin Nan Xiaoyan Yin Chase Q. Wu |
| author_facet | Zhenwen Gui Shuaishuai He Yao Lin Xin Nan Xiaoyan Yin Chase Q. Wu |
| author_sort | Zhenwen Gui |
| collection | DOAJ |
| description | Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution to the accuracy of fault prediction, and the progress of equipment faults is non-stationary. More specifically, capturing the time point at which a fault first appears is more important for improving the accuracy of fault prediction. Moreover, the progress of the different faults of equipment varies significantly. Therefore, taking feature differences and time information into consideration, we propose a <b>Ca</b>usal-<b>F</b>actors-<b>A</b>ware Attention <b>Net</b>work, <b>CaFANet</b>, for equipment fault prediction in the Internet of Things. Experimental results and performance analysis confirm the superiority of the proposed algorithm over traditional machine learning methods with prediction accuracy improved by up to 15.3%. |
| first_indexed | 2024-03-10T23:36:34Z |
| format | Article |
| id | doaj.art-53e38e6861f145fcab8278b880226949 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:36:34Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-53e38e6861f145fcab8278b8802269492023-11-19T02:55:57ZengMDPI AGSensors1424-82202023-08-012316704010.3390/s23167040CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of ThingsZhenwen Gui0Shuaishuai He1Yao Lin2Xin Nan3Xiaoyan Yin4Chase Q. Wu5The 7th Rescarch Institute of Electronics Technology Group Corporation, Guangzhou 510310, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaDepartment of Data Science, New Jersey Institute of Technology, Newark, NJ 07102, USAExisting fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution to the accuracy of fault prediction, and the progress of equipment faults is non-stationary. More specifically, capturing the time point at which a fault first appears is more important for improving the accuracy of fault prediction. Moreover, the progress of the different faults of equipment varies significantly. Therefore, taking feature differences and time information into consideration, we propose a <b>Ca</b>usal-<b>F</b>actors-<b>A</b>ware Attention <b>Net</b>work, <b>CaFANet</b>, for equipment fault prediction in the Internet of Things. Experimental results and performance analysis confirm the superiority of the proposed algorithm over traditional machine learning methods with prediction accuracy improved by up to 15.3%.https://www.mdpi.com/1424-8220/23/16/7040fault predictionindustrial Internet of Thingscausal factorsattention mechanism |
| spellingShingle | Zhenwen Gui Shuaishuai He Yao Lin Xin Nan Xiaoyan Yin Chase Q. Wu CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things Sensors fault prediction industrial Internet of Things causal factors attention mechanism |
| title | CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things |
| title_full | CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things |
| title_fullStr | CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things |
| title_full_unstemmed | CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things |
| title_short | CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things |
| title_sort | cafanet causal factors aware attention networks for equipment fault prediction in the internet of things |
| topic | fault prediction industrial Internet of Things causal factors attention mechanism |
| url | https://www.mdpi.com/1424-8220/23/16/7040 |
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