Empirical Analysis for Remaining Useful Life Estimation via Data-Driven Models

Abstract

Today, due to the growing complexity of engineered systems, it is crucial to develop technologies able to deal with the systems' behavior to maintain a high degree of safety, reliability, and efficiency while reducing operating expenses such as maintenance costs. The idea is to develop a Prognostics and Health Management (PHM) framework to monitor these complex systems' behavior using sensory data and then apply machine learning models to infer the current health state. One important goal is to estimate the Remaining Useful Life (RUL) essential for optimizing maintenance processes and sustainable practices in industrial settings. This work presents an empirical analysis for RUL estimation via a model degradation built using condition monitoring data. A support vector machine regression (SVR) model and a similarity measure algorithm are employed to extract degradation trends and compute the RUL. We evaluate the prognostics performance and compare the results with reported benchmarks from publicized works.