Predictive Maintenance using LSTM and Adaptive Windowing

Predictive maintenance is a critical approach in modern industries, aiming to forecast equipment failures and reduce downtime by leveraging operational data. Traditional methods, such as time series analysis, struggle to capture complex temporal dependencies in large-scale datasets. In this study, we propose an innovative solution that integrates Long Short-Term Memory (LSTM) networks with an adaptive windowing strategy for predictive maintenance. Unlike conventional methods that rely on fixed window sizes, our approach dynamically adjusts the window size based on the data's characteristics, optimizing the temporal context provided to the model. We apply this method to the Microsoft Azure predictive maintenance dataset from Kaggle and demonstrate that the adaptive window size significantly enhances the precision of failure predictions. This research highlights the potential of combining LSTM with window size optimization to improve the accuracy and efficiency of predictive maintenance models in real-world industrial applications.