Study on Intelligent Predictive Maintenance for Industrial Machinery: A Review
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Abstract
The application of Intelligent Predictive Maintenance (IPdM) is transforming industrial asset management through the intersection of Industry 4.0 technologies such as; Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), the Internet of Things (IoT) and Edge Computing. A systematic review of existing literature on current intelligent predictive maintenance paradigms, architectures and methods for use with industrial equipment is examined within this study. Additionally, the major technologies to be used for sensor based condition monitoring, fault detection/diagnosis, remaining useful life prediction and anomaly detection will be reviewed. This includes analysis of CNN's, LSTM's, random forests, GAN's, and explainable AI (xai). Data imbalance challenges in deploying models at the edge of an IoT network and how to achieve real time performance are presented. Emerging solutions to these issues are also addressed. Finally, this review presents several areas for potential research, which may include the development of federated learning systems, incorporation of digital twins into IPdM systems, and the design of hybrid edge cloud architectures.
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