Device Status Evaluation Method Based on Deep Learning for PHM Scenarios
The emergence of fault prediction and health management (PHM) technology has proposed a new solution and is suitable for implementing the functions of improving the intelligent management and control system. However, the research and application of the PHM model in the intelligent management and con...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/3/779 |
| _version_ | 1797624734507073536 |
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| author | Pengjun Wang Jiahao Qin Jiucheng Li Meng Wu Shan Zhou Le Feng |
| author_facet | Pengjun Wang Jiahao Qin Jiucheng Li Meng Wu Shan Zhou Le Feng |
| author_sort | Pengjun Wang |
| collection | DOAJ |
| description | The emergence of fault prediction and health management (PHM) technology has proposed a new solution and is suitable for implementing the functions of improving the intelligent management and control system. However, the research and application of the PHM model in the intelligent management and control system of electronic equipment are few at present, and there are many problems that need to be solved urgently in PHM technology itself. In order to solve such problems, this paper studies the application of the equipment-status-assessment method based on deep learning in PHM scenarios, in order to conduct in-depth research on the intelligent control system of electronic equipment. The experimental results in this paper show that the change in unimproved deep learning is very subtle before the performance change point, while improvements in deep learning increase the health value by about 10 times. Thus, improved deep learning amplifies subtle changes in health early in degradation and slows down mutations in health late at performance failure points. At the same time, comparing health-index-evaluation indicators, it can be concluded that although the monotonicity of the health index is low, its robustness and correlation are significantly improved. Additionally, it is very close to 1, making the health index curve more in line with traditional cognition and convenient for application. Therefore, an in-depth study of methods for health assessment by improving deep learning is of practical significance. |
| first_indexed | 2024-03-11T09:46:44Z |
| format | Article |
| id | doaj.art-283f49af91c8484295262b24d8801c72 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T09:46:44Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-283f49af91c8484295262b24d8801c722023-11-16T16:31:22ZengMDPI AGElectronics2079-92922023-02-0112377910.3390/electronics12030779Device Status Evaluation Method Based on Deep Learning for PHM ScenariosPengjun Wang0Jiahao Qin1Jiucheng Li2Meng Wu3Shan Zhou4Le Feng5Department of Electronic Engineering, Tsinghua University, Beijing 100080, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100080, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100080, ChinaSmartbow Tech., Inc., Beijing 100080, ChinaSmartbow Tech., Inc., Beijing 100080, ChinaSmartbow Tech., Inc., Beijing 100080, ChinaThe emergence of fault prediction and health management (PHM) technology has proposed a new solution and is suitable for implementing the functions of improving the intelligent management and control system. However, the research and application of the PHM model in the intelligent management and control system of electronic equipment are few at present, and there are many problems that need to be solved urgently in PHM technology itself. In order to solve such problems, this paper studies the application of the equipment-status-assessment method based on deep learning in PHM scenarios, in order to conduct in-depth research on the intelligent control system of electronic equipment. The experimental results in this paper show that the change in unimproved deep learning is very subtle before the performance change point, while improvements in deep learning increase the health value by about 10 times. Thus, improved deep learning amplifies subtle changes in health early in degradation and slows down mutations in health late at performance failure points. At the same time, comparing health-index-evaluation indicators, it can be concluded that although the monotonicity of the health index is low, its robustness and correlation are significantly improved. Additionally, it is very close to 1, making the health index curve more in line with traditional cognition and convenient for application. Therefore, an in-depth study of methods for health assessment by improving deep learning is of practical significance.https://www.mdpi.com/2079-9292/12/3/779PHM scenedeep learninghealth status assessmentneural network |
| spellingShingle | Pengjun Wang Jiahao Qin Jiucheng Li Meng Wu Shan Zhou Le Feng Device Status Evaluation Method Based on Deep Learning for PHM Scenarios Electronics PHM scene deep learning health status assessment neural network |
| title | Device Status Evaluation Method Based on Deep Learning for PHM Scenarios |
| title_full | Device Status Evaluation Method Based on Deep Learning for PHM Scenarios |
| title_fullStr | Device Status Evaluation Method Based on Deep Learning for PHM Scenarios |
| title_full_unstemmed | Device Status Evaluation Method Based on Deep Learning for PHM Scenarios |
| title_short | Device Status Evaluation Method Based on Deep Learning for PHM Scenarios |
| title_sort | device status evaluation method based on deep learning for phm scenarios |
| topic | PHM scene deep learning health status assessment neural network |
| url | https://www.mdpi.com/2079-9292/12/3/779 |
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