Based on IoT and Fog Computing, A Machine Learning-Based Predictive Maintenance Approach for Optimal Asset Management in Industry 4.0.

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Dr. K.Sai Manoj


Industry 4.0 enables smart production by allowing technical trends likeĀ  Big Data Analytics and Machine Learning to be integrated into and blended with existing production processes. By putting IoT sensors on physical sources, intelligent manufacturing systems make utilization of Industrial Internet of Things (IIoT) technology to enhance manufacturing activities. Smart industrial plants can communicate information independently thanks to IoT sensors, which can be used to make smarter business decisions. Smart manufacturing processes give businesses a comparative advantage by allowing them to boost profit margins, cut the cost of maintenance, save fuel, and manufacture higher-quality goods.The data generated by the Industrial Internet of Things (IIoT) facilitates information openness and process control in Industry 4.0. Before a part fails and interrupts the entire manufacturing line, proactive maintenance permits the company administrator to make decisions like whether to repair or replace it. As a result, to optimize work allocations and sustain a predictive maintenance system, Industry 4.0 needs good investment management.A study based on the ancillary vehicle industry is provided to demonstrate a predictive model for predicting abrupt breakdown in industrial equipment, allowing for a more efficient manufacturing and maintenance process. Real-time data and two-class logistic regression are used to create the proactive maintenance architecture using the proposed system.

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