Advancements in predictive maintenance modelling for industrial electrical motors: integrating machine learning and sensor technologies

Predictive maintenance is crucial in modern industrial settings, aiming to optimise performance, minimise downtime, and prevent costly equipment failures. The advancements in the use of machine learning techniques, sensor technologies, and data acquisition systems within Industry 4.0 has generated a lot of interest lately. In this paper, a novel predictive maintenance model is developed using machine learning approach for modelling vibration and temperature data collection for electrical motors of power press machines in Mitsubishi Electric Air Conditioning production (MACE) factory in the United Kingdom. Vibration and temperature data were collected using Bluetooth sensors installed on the motors, transmitted to a central data storage system for further analysis. To ensure the accuracy as well as the quality and reliability of the collected data sets for further analysis, pre-processing of the data was conducted, and the Isolation Forest (IF) outlier detection method was employed to filter out the anomalies. Machine learning algorithms including Auto-Regressive Integrated Moving Average (ARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM) networks were employed for predicting vibration signals, with hyperparameter tuning conducted using Sequential Model-Based optimisation (SMBO) with the Tree Parzen Estimator (TPE).

The results and concluding remarks presented in this paper show how the performance of the optimized ARIMA model can be used in predicting future vibration levels of the electrical motor. The residual analysis is also used to monitor discrepancies between predicted and observed vibration values, enabling proactive identification of emerging issues.