Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning
•A methodology for fault diagnosis with limited available data is achieved.•A digital twin of the machine is used to provide fault condition data for pre-training.•One-shot learning is achieved with the proposed deep transfer learning method.•Activation function and cost function of the deep network...
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| Veröffentlicht in: | Reliability engineering & system safety Jg. 215; S. 107938 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
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Elsevier Ltd
01.11.2021
Elsevier BV |
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| ISSN: | 0951-8320, 1879-0836 |
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| Abstract | •A methodology for fault diagnosis with limited available data is achieved.•A digital twin of the machine is used to provide fault condition data for pre-training.•One-shot learning is achieved with the proposed deep transfer learning method.•Activation function and cost function of the deep network structure are improved.
Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity DT model of the physical assets can produce system performance data that is close to reality, which provides remarkable opportunities for machine fault diagnosis when the measured fault condition data are insufficient. This paper presents an intelligent fault diagnosis framework for machinery based on DT and deep transfer learning. First, the DT model of the machine is built by establishing the simulation model and with further updating through continuously measured data from the physical asset. Second, all important machine conditions can be simulated from the built DT. Third, a new-type deep structure based on novel sparse de-noising auto-encoder (NSDAE) is developed and pre-trained with condition data from the source domain, as generated from the DT. Then, to achieve accurate machine fault diagnosis with possible variations in working conditions and system characteristics, the pre-trained NSDAE is fine-tuned using parameter transfer with only one sample from the target domain. The presented method is validated through a case study of triplex pump fault diagnosis. The experimental results demonstrate that the proposed method achieves intelligent fault diagnosis with a limited amount of measured data and outperforms other state-of-the-art data-driven methods. |
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| AbstractList | •A methodology for fault diagnosis with limited available data is achieved.•A digital twin of the machine is used to provide fault condition data for pre-training.•One-shot learning is achieved with the proposed deep transfer learning method.•Activation function and cost function of the deep network structure are improved.
Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity DT model of the physical assets can produce system performance data that is close to reality, which provides remarkable opportunities for machine fault diagnosis when the measured fault condition data are insufficient. This paper presents an intelligent fault diagnosis framework for machinery based on DT and deep transfer learning. First, the DT model of the machine is built by establishing the simulation model and with further updating through continuously measured data from the physical asset. Second, all important machine conditions can be simulated from the built DT. Third, a new-type deep structure based on novel sparse de-noising auto-encoder (NSDAE) is developed and pre-trained with condition data from the source domain, as generated from the DT. Then, to achieve accurate machine fault diagnosis with possible variations in working conditions and system characteristics, the pre-trained NSDAE is fine-tuned using parameter transfer with only one sample from the target domain. The presented method is validated through a case study of triplex pump fault diagnosis. The experimental results demonstrate that the proposed method achieves intelligent fault diagnosis with a limited amount of measured data and outperforms other state-of-the-art data-driven methods. Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity DT model of the physical assets can produce system performance data that is close to reality, which provides remarkable opportunities for machine fault diagnosis when the measured fault condition data are insufficient. This paper presents an intelligent fault diagnosis framework for machinery based on DT and deep transfer learning. First, the DT model of the machine is built by establishing the simulation model and with further updating through continuously measured data from the physical asset. Second, all important machine conditions can be simulated from the built DT. Third, a new-type deep structure based on novel sparse de-noising auto-encoder (NSDAE) is developed and pre-trained with condition data from the source domain, as generated from the DT. Then, to achieve accurate machine fault diagnosis with possible variations in working conditions and system characteristics, the pre-trained NSDAE is fine-tuned using parameter transfer with only one sample from the target domain. The presented method is validated through a case study of triplex pump fault diagnosis. The experimental results demonstrate that the proposed method achieves intelligent fault diagnosis with a limited amount of measured data and outperforms other state-of-the-art data-driven methods. |
| ArticleNumber | 107938 |
| Author | Shu, Lei de Silva, Clarence W. Shao, Haidong Xia, Min Lu, Siliang Williams, Darren |
| Author_xml | – sequence: 1 givenname: Min surname: Xia fullname: Xia, Min organization: Department of Engineering, Lancaster University, Lancaster, LA1 4YW, United Kingdom – sequence: 2 givenname: Haidong surname: Shao fullname: Shao, Haidong email: hdshao@hnu.edu.cn organization: State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China – sequence: 3 givenname: Darren surname: Williams fullname: Williams, Darren organization: The Welding Institute, Cambridge, CB21 6AL, United Kingdom – sequence: 4 givenname: Siliang surname: Lu fullname: Lu, Siliang organization: College of Electrical Engineering and Automation, Anhui University, Hefei, 230601, China – sequence: 5 givenname: Lei surname: Shu fullname: Shu, Lei organization: College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China – sequence: 6 givenname: Clarence W. surname: de Silva fullname: de Silva, Clarence W. organization: Department of Mechanical Engineering, The University of British Columbia, Vancouver, V6T 1Z4, Canada |
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| Snippet | •A methodology for fault diagnosis with limited available data is achieved.•A digital twin of the machine is used to provide fault condition data for... Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity DT model of the physical assets can produce system performance... |
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| SubjectTerms | Coders Deep transfer learning Digital twin Digital twins Domains Fault diagnosis Learning Novel sparse de-noising auto-encoder Reliability engineering Transfer learning Working conditions |
| Title | Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning |
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