This paper focuses on compressor systems associated with major production deferments. An advanced machine-learning approach is presented for determining anomalous behavior to predict a potential trip and probable root cause with sufficient warning to allow for intervention. This predictive-maintenance approach has the potential to reduce downtime associated with rotating-equipment failures.

Introduction

The first step in using a machine-learning system is to train the model to identify normal and abnormal operating conditions. The model can then classify real-time data from the equipment and indicate when the equipment’s performance strays outside the identified steady state. The ability to identify anomalies is a major difference between the proposed approach and traditional monitoring tools. With advances in digital technologies, correlations and warnings can be achieved in a matter of minutes, allowing engineers to take appropriate preventative action when they receive a failure warning.

The authors used historical data for 2016 in their analysis of system efficiency in predicting failures. The proof-of-concept system correctly predicted 11 trip events over the course of the year, almost 50% of the 23 failures that occurred during that period. One of the more important findings was that the machine-learning model predicted many failures hours in advance. In one case, it gave 36 hours’ notice. The median period of notice for eight events that were subsequently analyzed was approximately 7 hours.