Deep learning for prognostics and health management: State of the art, challenges, and opportunities
•The state-of-the-art deep models in PHM applications have been overviewed.•The models are classified into generative, discriminative and hybrid categories.•The transfer learning and domain adaptation in the context of PHM are discussed.•Important challenges and future research directions have been...
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| Vydáno v: | Measurement : journal of the International Measurement Confederation Ročník 163; s. 107929 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Elsevier Ltd
15.10.2020
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0263-2241, 1873-412X |
| On-line přístup: | Získat plný text |
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| Abstract | •The state-of-the-art deep models in PHM applications have been overviewed.•The models are classified into generative, discriminative and hybrid categories.•The transfer learning and domain adaptation in the context of PHM are discussed.•Important challenges and future research directions have been provided.
Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This paper provides a systematic review of state-of-the-art deep learning-based PHM frameworks. It emphasizes on the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. In addition, limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. |
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| AbstractList | Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This paper provides a systematic review of state-of-the-art deep learning-based PHM frameworks. It emphasizes on the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. In addition, limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. •The state-of-the-art deep models in PHM applications have been overviewed.•The models are classified into generative, discriminative and hybrid categories.•The transfer learning and domain adaptation in the context of PHM are discussed.•Important challenges and future research directions have been provided. Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This paper provides a systematic review of state-of-the-art deep learning-based PHM frameworks. It emphasizes on the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. In addition, limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. |
| ArticleNumber | 107929 |
| Author | Rezaeianjouybari, Behnoush Shang, Yi |
| Author_xml | – sequence: 1 givenname: Behnoush surname: Rezaeianjouybari fullname: Rezaeianjouybari, Behnoush email: b.rezaeianjouybari@mail.missouri.edu organization: Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO 65211, USA – sequence: 2 givenname: Yi surname: Shang fullname: Shang, Yi organization: Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA |
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| Snippet | •The state-of-the-art deep models in PHM applications have been overviewed.•The models are classified into generative, discriminative and hybrid... Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and... |
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| SubjectTerms | Acoustic emission Acoustics Aerospace engineering Anomalies Anomaly detection Artificial neural networks Condition monitoring Declination Deep learning Domain adaptation Fault diagnosis Machine learning Neural networks Nuclear energy Nuclear engineering Nuclear reactors Prognostics and health management Reliability engineering Signal monitoring State-of-the-art reviews Systems health monitoring Vibration Vibration monitoring |
| Title | Deep learning for prognostics and health management: State of the art, challenges, and opportunities |
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