Enhancing predictive maintenance method using machine learning to improve IoT-embedded machinery efficiency and performance

Predictive maintenance plays a crucial role in minimizing unplanned downtimes, reducing maintenance costs, and optimizing the operational efficiency of IoT-embedded industrial machinery. Despite its transformative potential, traditional predictive maintenance methods often face challenges such as limited accuracy, high latency, and inefficiencies in processing large and imbalanced datasets. This study proposes an enhanced predictive maintenance method using the Sliding Window Method with XGB model (E.XGB), incorporating advanced data preprocessing, permutation importance, and hyperparameter optimization to address these limitations. The proposed method was evaluated on two datasets, which are the synthetic AI4I 2020 Predictive Maintenance Dataset and the real-world CNC Milling Dataset. A comparative analysis with a predictive maintenance method using E.AB from prior research as a benchmark, along with several baseline models, DT, RF, and SVM, revealed that the E.XGB model consistently outperformed other methods in accuracy, precision, recall, and F1-scores. On the AI4I2020 dataset, the E.XGB model achieved an accuracy of 99.05%, while on the CNC Milling dataset, it attained an accuracy of 99.01%. Additionally, the E.XGB model also demonstrated reduced training and prediction times, meeting the real-time requirements of industrial applications. The proposed model demonstrated training speed of approximately 94% and prediction speeds of approximately 99.8% improvement over the E.AB model, making it highly suitable for real-time industrial applications. By improving accuracy, training speed, and prediction latency, the predictive maintenance method offers a robust, scalable, and reliable solution for predictive maintenance across diverse industrial contexts.

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