Predictive-Maintenance Approach Uses Machine Learning To Increase Efficiency

Abstract

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 The paper has not been peer reviewed.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. 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. Support Vector Machines (SVMs) SVMs are used in this study as a classifier for detecting abnormal machine states. SVMs were developed for binary classification. Some authors have argued that the SVM classifier has better results compared with techniques such as linear discriminant analysis and back-propagation neural networks. The compressor usually operates under normal working conditions. This poses a highly unbalanced problem for a two-class classification. Because of this, one-class classification using an SVM is implemented. The algorithm is trained on only normal data and creates a representation of this data. When the new points inferred are substantially different from the modeled class, they are labeled as outliers. Linear as well as radial kernel functions are explored. One of the properties of SVMs is that they may create a nonlinear decision boundary by projecting the data through nonlinear function to a space in higher dimension (Fig. 1). The one-class SVM creates a binary function that captures the region in the input space where most of the data exist. The function, thus formed, returns +1 for the region defined by training data points and –1 everywhere else.