Toward Proactive Maintenance: A Multi-Tiered Architecture for Industrial Equipment Health Monitoring and Remaining Useful Life Prediction

This research paper introduces a comprehensive proactive maintenance architecture designed for large-scale industrial machinery systems. The proposed architectural framework integrates supervised and unsupervised machine learning business processes in order to enhance maintenance capabilities. The primary objective of this architecture is to enhance operational efficiency and reduce the occurrence of problems in industrial equipment. The collection of data on the state of industrial machinery is conducted through the utilization of sensors that are attached to it. The recommended framework offers modules that might potentially implement capabilities such as immediate anomaly detection, pre-failure status prediction, and assessment of remaining usable life. We offer a prototype implementation to verify the appropriateness of the proposed framework for testing purposes. The prototype utilizes a simulation framework, Cooja, to model a sensor network. The concept entails the collection of status data from industrial machinery by each sensor. The prototype utilizes a machine learning library for data streams, the MOA framework, to design and implement a business process for anomaly detection using unsupervised machine learning, as well as a business process for early machine fault prediction using supervised machine learning. In addition, deep learning libraries are employed to construct a business process that predicts the remaining operational lifespan of industrial machinery that is anticipated to experience failure. Furthermore, we examined the efficacy of the prototype's integrated business protocols in this investigation. The proposed framework aligns effectively with software architectures designed to offer maintenance functionalities for industrial machinery, as indicated by our research findings.