Real-Time Equipment Health State Prediction with LSTM Networks and Bayesian Inference

摘要

Due to the emergence of sensing technology, a large number of sensors is used to monitor the health state of manufacturing equipment, thus enhancing the capabilities of predicting abnormal behaviours in (near) real-time. However, existing algorithms in predictive maintenance suffer from several limitations related to their scalability, efficiency, and reliability preventing their wide application to various industries. This paper proposes an approach for real-time prediction of the equipment health state using time-domain features extraction, Long Short-Term Memory (LSTM) Neural Networks, and Bayesian Online Changepoint Detection (BOCD). The proposed approach is applied to a real-life case in the steel industry and extensive experiments are performed. The paper also discusses the results and the conclusions drawn from the proposed approach.