Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments
Abstract Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes. Industrial Machinery Health Management (...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-79151-2 |
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| author | Ali Saeed Muazzam A. Khan Usman Akram Waeal J. Obidallah Soyiba Jawed Awais Ahmad |
| author_facet | Ali Saeed Muazzam A. Khan Usman Akram Waeal J. Obidallah Soyiba Jawed Awais Ahmad |
| author_sort | Ali Saeed |
| collection | DOAJ |
| description | Abstract Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes. Industrial Machinery Health Management (IMHM) is a crucial element, based on the Industrial Internet of Things (IIoT), which focuses on monitoring the health and condition of industrial machinery. The academic community has focused on various aspects of IMHM, such as prognostic maintenance, condition monitoring, estimation of remaining useful life (RUL), intelligent fault diagnosis (IFD), and architectures based on edge computing. Each of these categories holds its own significance in the context of industrial processes. In this survey, we specifically examine the research on RUL prediction, edge-based architectures, and intelligent fault diagnosis, with a primary focus on the domain of intelligent fault diagnosis. The importance of IFD methods in ensuring the smooth execution of industrial processes has become increasingly evident. However, most methods are formulated under the assumption of complete, balanced, and abundant data, which often does not align with real-world engineering scenarios. The difficulties linked to these classifications of IMHM have received noteworthy attention from the research community, leading to a substantial number of published papers on the topic. While there are existing comprehensive reviews that address major challenges and limitations in this field, there is still a gap in thoroughly investigating research perspectives across RUL prediction, edge-based architectures, and complete intelligent fault diagnosis processes. To fill this gap, we undertake a comprehensive survey that reviews and discusses research achievements in this domain, specifically focusing on IFD. Initially, we classify the existing IFD methods into three distinct perspectives: the method of processing data, which aims to optimize inputs for the intelligent fault diagnosis model and mitigate limitations in the training sample set; the method of constructing the model, which involves designing the structure and features of the model to enhance its resilience to challenges; and the method of optimizing training, which focuses on refining the training process for intelligent fault diagnosis models and emphasizes the importance of ideal data in the training process. Subsequently, the survey covers techniques related to RUL prediction and edge-cloud architectures for resource-constrained environments. Finally, this survey consolidates the outlook on relevant issues in IMHM, explores potential solutions, and offers practical recommendations for further consideration. |
| format | Article |
| id | doaj-art-4017033b5a454adfac447359647b037d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-4017033b5a454adfac447359647b037d2025-01-12T12:18:17ZengNature PortfolioScientific Reports2045-23222025-01-0115113010.1038/s41598-024-79151-2Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environmentsAli Saeed0Muazzam A. Khan1Usman Akram2Waeal J. Obidallah3Soyiba Jawed4Awais Ahmad5Department of Computer Sciences, Quaid-i-Azam UniversityDepartment of Computer Sciences, Quaid-i-Azam UniversityDepartment of Computer and Software Engineering, NUST College of Electrical and Mechanical EngineeringCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Computer and Software Engineering, NUST College of Electrical and Mechanical EngineeringCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)Abstract Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes. Industrial Machinery Health Management (IMHM) is a crucial element, based on the Industrial Internet of Things (IIoT), which focuses on monitoring the health and condition of industrial machinery. The academic community has focused on various aspects of IMHM, such as prognostic maintenance, condition monitoring, estimation of remaining useful life (RUL), intelligent fault diagnosis (IFD), and architectures based on edge computing. Each of these categories holds its own significance in the context of industrial processes. In this survey, we specifically examine the research on RUL prediction, edge-based architectures, and intelligent fault diagnosis, with a primary focus on the domain of intelligent fault diagnosis. The importance of IFD methods in ensuring the smooth execution of industrial processes has become increasingly evident. However, most methods are formulated under the assumption of complete, balanced, and abundant data, which often does not align with real-world engineering scenarios. The difficulties linked to these classifications of IMHM have received noteworthy attention from the research community, leading to a substantial number of published papers on the topic. While there are existing comprehensive reviews that address major challenges and limitations in this field, there is still a gap in thoroughly investigating research perspectives across RUL prediction, edge-based architectures, and complete intelligent fault diagnosis processes. To fill this gap, we undertake a comprehensive survey that reviews and discusses research achievements in this domain, specifically focusing on IFD. Initially, we classify the existing IFD methods into three distinct perspectives: the method of processing data, which aims to optimize inputs for the intelligent fault diagnosis model and mitigate limitations in the training sample set; the method of constructing the model, which involves designing the structure and features of the model to enhance its resilience to challenges; and the method of optimizing training, which focuses on refining the training process for intelligent fault diagnosis models and emphasizes the importance of ideal data in the training process. Subsequently, the survey covers techniques related to RUL prediction and edge-cloud architectures for resource-constrained environments. Finally, this survey consolidates the outlook on relevant issues in IMHM, explores potential solutions, and offers practical recommendations for further consideration.https://doi.org/10.1038/s41598-024-79151-2Intelligent fault diagnosisDeep learningTransfer learningIndustrial Internet of ThingsEdge computingRemaining useful life |
| spellingShingle | Ali Saeed Muazzam A. Khan Usman Akram Waeal J. Obidallah Soyiba Jawed Awais Ahmad Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments Scientific Reports Intelligent fault diagnosis Deep learning Transfer learning Industrial Internet of Things Edge computing Remaining useful life |
| title | Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments |
| title_full | Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments |
| title_fullStr | Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments |
| title_full_unstemmed | Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments |
| title_short | Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments |
| title_sort | deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource constrained environments |
| topic | Intelligent fault diagnosis Deep learning Transfer learning Industrial Internet of Things Edge computing Remaining useful life |
| url | https://doi.org/10.1038/s41598-024-79151-2 |
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