Bearing Anomaly Detection in an Air Compressor using an LSTM and RNN-Based Machine Learning Model

Abstract Smart systems such as data-driven machine health monitoring are emerging as a powerful technology for advanced manufacturing as a result of the availability of low-cost sensors, wireless communication, and advances in computational capabilities. Machine learning represents a critical element of manufacturing equipment health monitoring due to its ability to efficiently process large amounts of data and move beyond traditional rule-based maintenance methods. Predictive maintenance (PdM) has become increasingly popular in manufacturing. PdM can detect faults, 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. 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.