Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line
In response to the lack of a unified cyber–physical system framework, which combined the Internet of Things, industrial big data, and deep learning algorithms for the condition monitoring of critical transmission components in a smart production line. In this study, based on the conceptualization of...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/19/8967 |
| _version_ | 1797516847092858880 |
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| author | Lin Song Liping Wang Jun Wu Jianhong Liang Zhigui Liu |
| author_facet | Lin Song Liping Wang Jun Wu Jianhong Liang Zhigui Liu |
| author_sort | Lin Song |
| collection | DOAJ |
| description | In response to the lack of a unified cyber–physical system framework, which combined the Internet of Things, industrial big data, and deep learning algorithms for the condition monitoring of critical transmission components in a smart production line. In this study, based on the conceptualization of the layers, a novel five-layer cyber–physical systems framework for smart production lines is proposed. This architecture integrates physics and is data-driven. The smart connection layer collects and transmits data, the physical equation modeling layer converts low-value raw data into high-value feature information via signal processing, the machine learning modeling layer realizes condition prediction through a deep learning algorithm, and scientific decision-making and predictive maintenance are completed through a cognition layer and a configuration layer. Case studies on three critical transmission components—spindles, bearings, and gears—are carried out to validate the effectiveness of the proposed framework and hybrid model for condition monitoring. The prediction results of the three datasets show that the system is successful in distinguishing condition, while the short time Fourier transform signal processing and deep residual network deep learning algorithm is superior to that of other models. The proposed framework and approach are scalable and generalizable and lay the foundation for the extension of the model. |
| first_indexed | 2024-03-10T07:06:34Z |
| format | Article |
| id | doaj.art-44375dc6a2ad487fade7c1008b5ad82f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:06:34Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-44375dc6a2ad487fade7c1008b5ad82f2023-11-22T15:45:42ZengMDPI AGApplied Sciences2076-34172021-09-011119896710.3390/app11198967Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production LineLin Song0Liping Wang1Jun Wu2Jianhong Liang3Zhigui Liu4College of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, ChinaCollege of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, ChinaState Key Laboratory of Tribology and Institute of Manufacturing Engineering, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Tribology and Institute of Manufacturing Engineering, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaCollege of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, ChinaIn response to the lack of a unified cyber–physical system framework, which combined the Internet of Things, industrial big data, and deep learning algorithms for the condition monitoring of critical transmission components in a smart production line. In this study, based on the conceptualization of the layers, a novel five-layer cyber–physical systems framework for smart production lines is proposed. This architecture integrates physics and is data-driven. The smart connection layer collects and transmits data, the physical equation modeling layer converts low-value raw data into high-value feature information via signal processing, the machine learning modeling layer realizes condition prediction through a deep learning algorithm, and scientific decision-making and predictive maintenance are completed through a cognition layer and a configuration layer. Case studies on three critical transmission components—spindles, bearings, and gears—are carried out to validate the effectiveness of the proposed framework and hybrid model for condition monitoring. The prediction results of the three datasets show that the system is successful in distinguishing condition, while the short time Fourier transform signal processing and deep residual network deep learning algorithm is superior to that of other models. The proposed framework and approach are scalable and generalizable and lay the foundation for the extension of the model.https://www.mdpi.com/2076-3417/11/19/8967cyber-physical systemcritical transmission componentssmart production linecondition monitoringmachine learning |
| spellingShingle | Lin Song Liping Wang Jun Wu Jianhong Liang Zhigui Liu Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line Applied Sciences cyber-physical system critical transmission components smart production line condition monitoring machine learning |
| title | Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line |
| title_full | Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line |
| title_fullStr | Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line |
| title_full_unstemmed | Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line |
| title_short | Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line |
| title_sort | integrating physics and data driven cyber physical system for condition monitoring of critical transmission components in smart production line |
| topic | cyber-physical system critical transmission components smart production line condition monitoring machine learning |
| url | https://www.mdpi.com/2076-3417/11/19/8967 |
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