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Predictive-Maintenance Approach Uses Machine Learning To Increase Efficiency

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.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 28990, “Increasing Production Efficiency Through Compressor-Failure Predictive Analytics Using Machine Learning,” by D. Pandya, A. Srivastava, A. Doherty, S. Sundareshwar, C. Needham, A. Chaudry, and S. Krishnalyer, Shell, prepared for the 2018 Offshore Technology Conference, Houston, 30 April–3 May. The paper has not been peer reviewed. Copyright 2018 Offshore Technology Conference. Reproduced by permission.
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Predictive-Maintenance Approach Uses Machine Learning To Increase Efficiency

01 December 2018

Volume: 70 | Issue: 12

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