Machine Learning Based Time-to-Event Models for Optimizing Asset Health and Prognostics
Authors: Rai P. , Affonso M. A. , Gulati N. 
Published: October 2023

With the advancement in data-driven asset performance management coupled with machine learning, advanced analytical techniques have been shown to be successful in driving commercial value to oil and gas operators by offering insights into asset health and performance. Evolving asset strategy for critical assets, from reactive to proactive, have several components that help reliability and maintenance engineers optimize their strategy. Predicting critical events of failure, forecasting degradation profile of critical equipment or time- to-complete failure, are some of the key aspects of a predictive maintenance solution. Accurate estimation of time remaining for events of interest (failure, fouling, maintenance procedures, etc.) using equipment and process data through sensors, has been found to be of critical value in driving operational excellence for assets in the energy industry. Methods that predict such events are known under the umbrella term ‘Prognostics’.

Although a variety of prognostics methods have been proposed in the literature, the applicability of a method depends on the quality and quantity of available data and the specific goal [1]. For large assets, relatively few, with only limited operational data available, it is imperative that classical time to event analysis such as Weibull distribution may not be applicable as such analysis does not consider specific operational characteristics of the asset. In this paper, we focus on condition-based prognostics, that explicitly consider not just operational characteristics but also variability in assets performance due to difference in manufacturing, installation, and maintenance actions. In this class of methods, there are three components. The first is a condition indicator, also known as prognostic parameter, that can be inferred from sensor readings which characterizes system degradation (for e.g., due to fouling in a heat exchanger). The second is a threshold of this condition indicator at which the asset no longer performs to its specifications. Third and the most important ingredient is the modeling algorithm that determines the evolution of condition indicator in time based on feature derived from operational process variables. General Path Model and its extensions are the most common methods in this category [3,4].

While key ideas in this paper are applicable in general to a large class of assets, we consider the specific use case of degradation in performance due to fouling in heat exchangers. In [2], a comprehensive study of this use case and empirical modeling for estimation of Remaining Useful Life shows promising results. Our main contribution compared to earlier works is the specific machine learning modeling choice that allows design and use of specific features most predictive of fouling and its variation (for e.g., due to interventions) in an operation campaign. The method therefore is more flexible and allows features to be shared across related assets (e.g., furnace and heat exchanger loop) thus allowing analysis and control for the purpose of process optimization. Although the current model is deterministic, it can be readily extended as a probabilistic model using techniques in Uncertainty Quantification (UQ).