LSTM and Isolation Forest integration for industrial motor failure prediction using vibration analysis

This paper explores the integration of Long Short-Term Memory (LSTM) neural networks and Isolation Forest algorithms to predict failures in three-phase electric motors at a cement plant, specifically targeting a hot gas exhaust fan used in the clinkerization process. The LSTM model is employed to capture and analyze complex temporal patterns in vibration, speed, and power data, projecting future values with high accuracy. The Isolation Forest algorithm is then used to detect anomalies in these projections by identifying significant deviations from normal behavior. This combined approach allows for continuous monitoring of equipment, enabling timely interventions before failures occur. The implementation of these machine learning techniques demonstrates significant improvements in predictive accuracy, enabling timely interventions that can prevent critical failures and minimize downtime.