Integration of IoT and Machine Learning for Predictive Maintenance in Manufacturing Industries
DOI:
https://doi.org/10.71143/sdwjfp14Keywords:
Predictive Maintenance, IoT, Machine Learning, Manufacture Industry, Industry 4.0Abstract
The Internet of Things (IoT) and Machine Learning (ML) have formed a sort of breakthrough to the solution of predictive maintenance that is witnessed in the manufacturing industry. Traditional maintenance programs have also been reported to encourage uncontrolled outages, higher operating costs and inefficient management of resources. With the help of IoT and ML, the manufacturers can obtain an up-to-date sense of the equipment health, predicting its breakdowns, and offering pre-emptive care. The paper would discuss the emergence of IoT sensors and the ML algorithms in predictive maintenance in manufacturing and what it means to efficiency of operations, savings of costs as well as better production and its productivity. The paper and the discussion of the IoT sensor technologies used in the sphere of controlling the key equipment also discuss the significance of the ML models to analyse the sensor data and at the last, determines the effective examples of using the same technologies in manufacturing plants. The research concluded that predictive maintenance carried out by IoT and ML could lead to improved decision-making activities, extend the life cycle of equipment, reduce downtimes and cost a significant amount of money. The problems and limitations of such processes as the deployment of IoT and ML in the predictive maintenance systems are also mentioned in the paper. There are potential directions of future research and technological development in the area of the studied predictive maintenance, as the conclusion offers. The directions are especially relevant in the context of the industry 4.0.
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