Data-driven Approach for RUL Prediction of an Experimental Filtration System

Abstract

This paper illustrates a data-driven approach adopted to address the PHME2020 Data Challenge competition. The aim of the challenge was to estimate the Remaining Useful Lifetime (RUL) of an experimental filtration device analyzing its clogging status by means of static (e.g. data sheets, fluid type, sensors) and dynamic (e.g. sensing data) information. We address the problem employing different state-of-the-art feature extraction, feature selection, and machine learning techniques. In this paper, we describe the approach followed to assess the problem and to generate robust and adaptable prediction models together with a corresponding performance assessment and robustness evaluation. The performance of the proposed solution is calculated in term of penalty score. The final penalty score 57.24 ranked 2nd in the above-mentioned data challenge competition.