Predictive Maintenance Using Machine Learning And Sensor Data

Résumé: This thesis explores the relationship between Machine Learning (ML) and sensor data to achieve predictive maintenance (PdM) in Industry 4.0. The focus is on Deep Learning (DL) methods for developing a comprehensive predictive maintenance process using time series data from vibration sensors. The IMS (Center for Intelligent Maintenance Systems) bearing dataset serves as the foundation for this exploration. The process progresses from predicting the machine's next operational state, where the best result was achieved by an LSTM (Long Short-Term Memory) single model in the frequency domain, with an RMSE (Root Mean Square Error) of 0.0005, MAE (Mean Absolute Error) of 0.00004, and an R² (coefficient of determination) of 0.93. It then identifies change points indicative of anomalies. Finally, the goal is to predict the remaining useful life (RUL) of the machinery, where the best result was achieved using the hybrid DL model LSTM-CNN (Long Short-Term Memory - Convolutional Neural Networks), with an RMSE of 0.001 and an MAE of 0.0007. By implementing these techniques, the thesis aims to demonstrate the efficacy of ML and sensor data in establishing proactive maintenance strategies within the industry 4.0 framework, leading to significant cost savings and enhanced operational efficiency.

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