Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion
To detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term Me...
| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
MDPI AG
2022-09-01
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| coleção: | Applied Sciences |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/2076-3417/12/19/9642 |
| _version_ | 1827655309978501120 |
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| author | Yong Wang Xiaoqiang Guo Xinhua Liu Xiaowen Liu |
| author_facet | Yong Wang Xiaoqiang Guo Xinhua Liu Xiaowen Liu |
| author_sort | Yong Wang |
| collection | DOAJ |
| description | To detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Attention Mechanism, and combines a Dilated Convolution Module (DCM) with LSTM to recognize complex signals of multiple sensors. By introducing an attention mechanism, the recognition performance of the network was improved. Finally, the real-time status information of the production line was obtained by integrating attention weight. Experimental results show that for the custom multi-sensor dataset of A-class insulation board production line, the proposed CNN-LSTM fault diagnosis method achieved 98.97% accuracy. Compared with other popular algorithms, the performance of the proposed CNN-LSTM model performed excellently in each evaluation index is better. |
| first_indexed | 2024-03-09T22:04:27Z |
| format | Article |
| id | doaj.art-5919c7fac2f747508aab673171308d6e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:04:27Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5919c7fac2f747508aab673171308d6e2023-11-23T19:43:07ZengMDPI AGApplied Sciences2076-34172022-09-011219964210.3390/app12199642Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data FusionYong Wang0Xiaoqiang Guo1Xinhua Liu2Xiaowen Liu3School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaSchool of Electrical Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaTo detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Attention Mechanism, and combines a Dilated Convolution Module (DCM) with LSTM to recognize complex signals of multiple sensors. By introducing an attention mechanism, the recognition performance of the network was improved. Finally, the real-time status information of the production line was obtained by integrating attention weight. Experimental results show that for the custom multi-sensor dataset of A-class insulation board production line, the proposed CNN-LSTM fault diagnosis method achieved 98.97% accuracy. Compared with other popular algorithms, the performance of the proposed CNN-LSTM model performed excellently in each evaluation index is better.https://www.mdpi.com/2076-3417/12/19/9642A-class thermal insulation panel production linefault diagnosisdeep learninglong short-term memoryattention mechanism |
| spellingShingle | Yong Wang Xiaoqiang Guo Xinhua Liu Xiaowen Liu Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion Applied Sciences A-class thermal insulation panel production line fault diagnosis deep learning long short-term memory attention mechanism |
| title | Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion |
| title_full | Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion |
| title_fullStr | Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion |
| title_full_unstemmed | Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion |
| title_short | Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion |
| title_sort | research on a fault diagnosis method of an a class thermal insulation panel production line based on multi sensor data fusion |
| topic | A-class thermal insulation panel production line fault diagnosis deep learning long short-term memory attention mechanism |
| url | https://www.mdpi.com/2076-3417/12/19/9642 |
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