Remaining Useful Life Prediction for Experimental Filtration System: A Data Challenge



Published Jul 22, 2020
Kürşat Ince Engin Sirkeci Yakup Genç


Maintenance costs of industrial systems often exceed the initial investment cost. Predictive maintenance, which analyzes the health of the system and suggests maintenance planning, is one of the strategies implemented to reduce maintenance costs. Health status and life estimation of the machinery are the most researched topics in this context. In this paper, we present our analysis for Fifth European Conference of the Prognostics and Health Management Society 2020 Data Challenge, which introduces an experimental filtration system for different experiment setups, and asks for remaining useful life predictions. We compared random forest, gradient boosting, and Gaussian process regression algorithms to predict the useful life of the experimental system. With the help of a new fault-based piecewise linear RUL assignment strategy, our gradient boosting based solution has been ranked 3rd in the data challenge.

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