Advanced Detection of Machine Failure in Automated Industries using Long-Short Term Memory and LightGBM

In the rapidly evolving industrial landscape, companies are using various technologies to optimize their operations which thereby reduce costs, and they focus on increasing their profits. Proactive maintenance has emerged as an important strategy that enable industries to foresee and prevent machine failures beforehand. This proactive approach not only extends the machine lifespan but also leads to cost savings. Machine failure detection is very essential in automated industries, medical fields, aerospace, transportation and many more industries where machinery is central for the production process. Traditional reactive maintenance methods only focus on repairing the machines after the occurrence of the failure which greatly affects the production. Machine learning, with it’s ability to analyze large datasets and identify the unusual patterns based on the historical data. This paper mainly proposes using a hybrid approach of the ML algorithms to increase the accuracy and efficiency of the detection of machine failures. The algorithms are Long-Short Term Memory (LSTM) networks and Light Gradient Boosting Machine (LightGBM). This approach is mainly used for early fault detection in machines. We combine the features of LSTM to find temporal patterns in sensor data with LightGBM’s capacity to refine predictions and analyze feature importance. This approach accurately finds the failure predictions, then classify failure severity that can be visualized using graphs and trigger actionable maintenance alerts. By this proposed approach we achieved a result of 98% accuracy on real sensor data when compared to traditional methods, outperforming them by 7%. This research fills the gap between the traditional reactive maintenance and the current predictive maintenance.