Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope
With the acceleration of industrialization and the increase in the complexity of equipment, it is particularly important to accurately predict and effectively maintain equipment failures. In response to the problem of difficulty in accurately achieving predictive maintenance and the severely limited...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-01-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2024-0202 |
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| author | Bozhou Li Dajiang Ju Xingwang Li Yan Liu Hongru He Hao Wang |
| author_facet | Bozhou Li Dajiang Ju Xingwang Li Yan Liu Hongru He Hao Wang |
| author_sort | Bozhou Li |
| collection | DOAJ |
| description | With the acceleration of industrialization and the increase in the complexity of equipment, it is particularly important to accurately predict and effectively maintain equipment failures. In response to the problem of difficulty in accurately achieving predictive maintenance and the severely limited application range of new real-time disassembly sensors, this article conducts research on the synergistic effect of the new real-time disassembly sensors and artificial intelligence technology. First, relevant parameter data of a certain enterprise’s generator can be collected on-site, and the Pearson correlation coefficient method can be used to calculate the correlation between the generator data parameters and the target fault type, ensuring the degree of correlation in extracting data features. Then, based on the gated recurrent unit (GRU) model, the article applied the particle swarm optimization (PSO) algorithm to optimize the parameters of the GRU network and used the support vector machine (SVM) model to optimize the classification function of the network output. Finally, the optimized GRU model can be applied to predict the fault types of generators, and based on this, it can be applied to the energy industry, agriculture, medical and health, and transportation industries to verify the application scalability of artificial intelligence and new real-time disassembly sensors. The experimental results show that the optimized GRU model combined with the new real-time disassembly sensor achieved an average accuracy of 0.94 in generator fault prediction, with a cost loss rate of only 3.03%, a decrease of 5.91% compared to the single new real-time disassembly sensor. The combination of artificial intelligence technology for precise predictive maintenance of generators greatly reduces maintenance costs and overcomes the limitations of individual real-time sensor disassembly. This has to some extent expanded the application scope and promoted the intelligent development of various industries. |
| format | Article |
| id | doaj-art-a12f26b792a54c3ab8383ba67edabae6 |
| institution | Kabale University |
| issn | 2191-026X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-a12f26b792a54c3ab8383ba67edabae62025-02-02T15:45:53ZengDe GruyterJournal of Intelligent Systems2191-026X2025-01-01341118720710.1515/jisys-2024-0202Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scopeBozhou Li0Dajiang Ju1Xingwang Li2Yan Liu3Hongru He4Hao Wang5Xiangyang Electric Power Supply Company, STATE GRID Hubei Electric Power, Xiangyang, 441499, ChinaXiangyang Electric Power Supply Company, STATE GRID Hubei Electric Power, Xiangyang, 441499, ChinaXiangyang Electric Power Supply Company, STATE GRID Hubei Electric Power, Xiangyang, 441499, ChinaXiangyang Electric Power Supply Company, STATE GRID Hubei Electric Power, Xiangyang, 441499, ChinaXiangyang Electric Power Supply Company, STATE GRID Hubei Electric Power, Xiangyang, 441499, ChinaXiangyang Electric Power Supply Company, STATE GRID Hubei Electric Power, Xiangyang, 441499, ChinaWith the acceleration of industrialization and the increase in the complexity of equipment, it is particularly important to accurately predict and effectively maintain equipment failures. In response to the problem of difficulty in accurately achieving predictive maintenance and the severely limited application range of new real-time disassembly sensors, this article conducts research on the synergistic effect of the new real-time disassembly sensors and artificial intelligence technology. First, relevant parameter data of a certain enterprise’s generator can be collected on-site, and the Pearson correlation coefficient method can be used to calculate the correlation between the generator data parameters and the target fault type, ensuring the degree of correlation in extracting data features. Then, based on the gated recurrent unit (GRU) model, the article applied the particle swarm optimization (PSO) algorithm to optimize the parameters of the GRU network and used the support vector machine (SVM) model to optimize the classification function of the network output. Finally, the optimized GRU model can be applied to predict the fault types of generators, and based on this, it can be applied to the energy industry, agriculture, medical and health, and transportation industries to verify the application scalability of artificial intelligence and new real-time disassembly sensors. The experimental results show that the optimized GRU model combined with the new real-time disassembly sensor achieved an average accuracy of 0.94 in generator fault prediction, with a cost loss rate of only 3.03%, a decrease of 5.91% compared to the single new real-time disassembly sensor. The combination of artificial intelligence technology for precise predictive maintenance of generators greatly reduces maintenance costs and overcomes the limitations of individual real-time sensor disassembly. This has to some extent expanded the application scope and promoted the intelligent development of various industries.https://doi.org/10.1515/jisys-2024-0202new real-time disassembly sensorartificial intelligencesynergistic effectlimitations of maintenancescope of applicationsgenerator equipment |
| spellingShingle | Bozhou Li Dajiang Ju Xingwang Li Yan Liu Hongru He Hao Wang Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope Journal of Intelligent Systems new real-time disassembly sensor artificial intelligence synergistic effect limitations of maintenance scope of applications generator equipment |
| title | Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope |
| title_full | Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope |
| title_fullStr | Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope |
| title_full_unstemmed | Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope |
| title_short | Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope |
| title_sort | synergistic effect of artificial intelligence and new real time disassembly sensors overcoming limitations and expanding application scope |
| topic | new real-time disassembly sensor artificial intelligence synergistic effect limitations of maintenance scope of applications generator equipment |
| url | https://doi.org/10.1515/jisys-2024-0202 |
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