Machine Learning Enabled Predictive Maintenance of Electric Motors
Keywords:
Datasets; Electric motors; Machine learning; Predictive maintenance.Abstract
As industries increasingly depend on electric motors for critical operations, ensuring their reliability and efficiency is essential. This paper presents a comprehensive analysis of data-driven approach for predictive maintenance aimed at optimizing system performance and minimize unplanned downtime. Leveraging on advanced machine learning algorithms using Python programming language, the proposed method integrates data from electric motors—such as temperature, vibration, and current to develop predictive models. Using Python to implement predictive maintenance for electric motors entails a methodical process that includes data collection, preprocessing, model development, and real-time monitoring. By training these models on historical data, the framework was able to detect miniature changes in motor behavior that indicate potential issues. Results from the study show a range of output fault variations produced by all trained models. These miniature variations highlight how accurate the data-trained models are at predicting faults. By adopting data-driven methodologies, industries can enhance resource allocation, streamline maintenance operations, and extend the lifespan and efficiency of electric motor systems.
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