Assessment of Data-Driven, Machine Learning Techniques for Machinery Prognostics of Offshore Assets

Abstract

Abstract Accurate prediction of machinery failure is a challenging and important task for the offshore industry. Early diagnosis and prognosis of machinery failure has become a necessity to drive high levels of safety and performance in oil and gas operations. Prognostics enabled by data-driven machine learning techniques offers new insights into the health and performance of machinery and thereby improves operational efficiency. Advances in this topic are important because of the challenging nature of prognostics and the large degree of uncertainty that is associated. In this work, we demonstrate a practical approach to build and perform robust predictive machine learning models that are capable of detecting critical machinery failure early. In addition, a review of recent state-of-art machine learning approaches employed in modeling of machinery failure prediction is presented. Predictive models discussed here are based on various supervised machine learning techniques as well as on different input features. A variety of these newer algorithms include baggings, boosting, support vector machines, ramdon forest, etc., all of which have been widely applied in predictive models. Although it is evident that machine learning methods can improve our understanding of failure progression, appropriate validation schemes are necessary to evaluate machine learning models to assist in effective and accurate decision making. Therefore, we illustrate different levels of evaluation methodologies that can be trusted for these methods to be considered in the everyday operational practice. The machine learning models mentioned in this manuscript is then applied to a case of bearing failure on wind turbine gearbox. A machine learning model by utilizing XGBoost is proposed for prediction of remaining useful life with improved accuracy. This paper could also serve as a guidline to assess machine learning data analytic methods for prognostics relevant to common machinery types on offshore assets.