Saeed Ali, A Khan Muazzam, Akram Usman, J Obidallah Waeal, Jawed Soyiba, Ahmad Awais
Department of Computer Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
ICESCO Chair Big Data Analytics and Edge Computing, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
Sci Rep. 2025 Jan 7;15(1):1114. doi: 10.1038/s41598-024-79151-2.
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.
工业 4.0 代表第四次工业革命，其特点是将数字技术、物联网（IoT）、人工智能、大数据和其他先进技术融入工业流程。基于工业物联网（IIoT）的工业机械健康管理（IMHM）是一个关键要素，它专注于监测工业机械的健康状况。学术界关注 IMHM 的各个方面，如预测性维护、状态监测、剩余使用寿命（RUL）估计、智能故障诊断（IFD）以及基于边缘计算的架构。这些类别中的每一个在工业流程中都有其自身的重要性。在本次综述中，我们专门研究关于 RUL 预测、基于边缘的架构和智能故障诊断的研究，主要关注智能故障诊断领域。IFD 方法在确保工业流程顺利执行方面的重要性日益明显。然而，大多数方法是在数据完整、平衡且丰富的假设下制定的，这往往与实际工程场景不符。与 IMHM 这些分类相关的困难已受到研究界的显著关注，导致大量关于该主题的论文发表。虽然现有全面综述涉及该领域的主要挑战和局限性，但在全面研究 RUL 预测、基于边缘的架构和完整智能故障诊断过程的研究视角方面仍存在差距。为填补这一差距，我们进行了一项全面综述，回顾并讨论该领域的研究成果，特别关注 IFD。首先，我们将现有的 IFD 方法分为三个不同的视角：数据处理方法，旨在优化智能故障诊断模型的输入并减轻训练样本集的局限性；模型构建方法，涉及设计模型的结构和特征以增强其应对挑战的能力；优化训练方法，专注于完善智能故障诊断模型的训练过程并强调训练过程中理想数据的重要性。随后，综述涵盖与 RUL 预测以及资源受限环境下的边缘 - 云架构相关的技术。最后，本次综述总结了 IMHM 相关问题的展望，探索了潜在解决方案，并提供了供进一步考虑的实用建议。
