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

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.

Empirical Comparison of Supervised Learning Methods for Assessing the Stability of Slopes Adjacent to Military Operation Roads

“…Compared with random forest, XGBoost, as a tree-based model, includes more tuneable hyperparameters, and in boosting, boosted trees are grown sequentially. Specifically, each of the trees is grown using information from previously grown trees, unlike bagging, where multiple copies of original training data are created and fitted to separate decision trees [51]. This may explain why XGBoost generally performs better than random forest.…”

Kiln Process Fan Vibrations Prediction Based on Machine Learning Models: Application to the Raw Mill Fan

“…Acquiring process fan vibration data can be useful in detecting potential mechanical issues or faults that may lead to failure or decreased performance of the fan. Here are the steps to acquire process fan vibration data and store it in the database [4,5]:…”

A review of failure prognostics for predictive maintenance of offshore wind turbines

“…Two other papers provide a thorough overview of data-driven models. Lu et al (2017) review different ML approaches for offshore assets and include a complete review of statistical metrics and cross validation methods for model evaluation [17]. Garcia and Peinado review the frequently used ML methods (NN, fuzzy logic, genetic algorithm, etc.)…”

“…algorithm and trained by input data. It can identify underlying connections among data sets and predict failure type or time [17]. Methods can be roughly classified into classical methods, deep learning methods, and ensemble learning methods.…”