AutoML as a Catalyst for Predictive Maintenance Innovation: Strategies and Outcomes

The intersection of predictive maintenance and Automated Machine Learning (AutoML) ushers in a transformative era for industrial optimization, wherein the foresight of equipment failures becomes both attainable and actionable. Conventional methodologies for predictive maintenance often necessitate profound technical knowledge and comprehensive data manipulation. This proposed work introduces an innovative framework based on AutoML, employing tools such as AutoKeras, Sweetviz, NumPy, pandas, Streamlit, and PyCaret, to democratize the crafting of predictive models. The outlined methodology automates the stages of data preprocessing, feature selection, and model optimization, markedly simplifying the process of predictive maintenance. The application of this framework illustrates a streamlined model development cycle, attaining significant achievements in the prediction of equipment failures, thereby augmenting operational efficiency, and diminishing downtimes. Through practical deployment, the proposed work demonstrates how this strategy not only alleviates the complexity inherent in predictive maintenance but also yields considerable enhancements in maintenance strategy and resource allocation. This exploration validates the potential of AutoML to revolutionize predictive maintenance, enhancing its efficiency and accessibility.