Bearing anomaly detection in an air compressor using an LSTM and RNN-based machine learning model

Smart systems such as data-driven machine health monitoring are emerging as powerful technology for advanced manufacturing as a result of the availability of low-cost sensors, wireless communication, and advances in Machine Learning (ML) and Artificial Intelligence (AI). Predictive maintenance (PdM) has become increasingly popular in manufacturing, which can identify approaching failures, determine root causes of operation anomalies, estimate the current health state of a system, and predict the future state and time when a component will fail in the absence of an intervention. One weakness of many past studies is the lack of run-to-failure data from an actual production environment. This paper presents run-to-failure data for the air compressor of an injection molding machine. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is proposed to detect bearing faults in the air compressor, which can capture the long-term dependencies without losing the capability to identify local dependencies. The model achieves a 97.4% of prediction accuracy (95.3% of overall accuracy). Experiments for machine state classification are also conducted, and the classification performance compares favorably with conventional models.