Predictive Maintenance of Industrial Equipment Using Machine Learning and IOT Data Analytics: A Context-Aware, Edge-Cloud Framework with Operator-in-the-Loop Adaptation
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The reliable functioning of any manufacturing sys-tem presupposes smooth equipment performance; however, un-scheduled interruptions remain a major source of loss in terms of efficiency, safety, and costly maintenance expenses. Traditional approaches including proactive or reactive maintenance methods prove ineffective in highly dynamic and unpredictable production environments. While IoT technology-driven predictive mainte-nance offers superior alternatives, current solutions suffer from critical limitations concerning long response times, reliance on network infrastructure, fast model decay due to changing load regimes and aging systems, and low credibility and transparency of predictions. This paper presents a context-aware framework for predictive maintenance incorporating a hybrid cloud-edge architecture and adaptive maintenance techniques based on operator involvement. The proposed model uses real-time context data to continuously update its ability to detect anomalies and forecast gradual equipment deterioration.
The edge component carries out preliminary filtering of incoming raw data, performs feature engineering, and provides basic classification results, sending extracted contextual information about detected events to the cloud server for further processing. A key advantage is the ability to incorporate maintenance engineers’ feedback as contextual information into the learning algorithm. Maintenance technicians provide additional validation, explanation, or cor-rection to alerts raised by the algorithm, helping adjust the model to changing conditions. Combined with an explanatory engine, the proposed framework translates identified multivariate factors into understandable failure mode identification, prob- abilities, and maintenance procedure prioritization. Results of testing in industrial settings show improved equipment efficiency metrics, including significantly decreased false alert numbers, reduced maintenance diagnosis cycles, and effective inventory management. The described predictive maintenance technique proves efficient for ensuring operational resilience and seamless integration into existing industrial practices.
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