Sudden equipment breakdowns in industries can severely disrupt operations which can cause significant production delays, expensive repairs, and even hazardous safety issues. Older conventional techniques, such as waiting for a device to break to repair it (reactive maintenance) or checking in on it periodically regardless if it needs attention or not (preventive maintenance), are not efficient in the best uses of resources. This research investigates a better way: predictive maintenance, which implies the maintenance of equipment with the use of IoT sensors, real-time monitoring, and machine learning for analysing contextual data to identify possible equipment failures before they happen. The system enables industries to intervene at the right moment by identifying early indicators and mitigating machine downtime while ensuring smooth operations. As the systems become more autonomous, the study seeks new methods for more complex machine learning approaches aimed at fine-tuning the failure prediction to improve the efficiency of industrial operations and make them safer and cheaper. Predictive maintenance opens the doors to better equip the industry to adapt to a more evidence based, forward-thinking, and robust approach toward machinery management.
Python, Machine Learning (ML), Internet of Things (IoT), MYSQL, Mongodb.
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