The 2018 PHM Data Challenge posed the problem of estimating Remaining Useful Life (RUL) for multiple faults in ion etch mills. As with any industrial system, run-to-failure data for the mills is not directly available and the mills experience more than one fault at the same time. We propose a novel data-driven methodology to address these challenges and develop a workflow that can be used for concurrent estimation of RUL for multiple faults in ion etch mills in real time. In the proposed approach, operational data of the ion etch mill is used to build a machine learning model for predicting a health score of the mill and to create a library of truncated degradation curves for each fault. These are then utilized for RUL predictions using Dynamic Time Warping (DTW) curve matching. Application of the proposed approach to test and validation datasets provided during the data challenge showed reasonable agreement between RUL predictions and the ground truth. The approach proposed here can be extended to other industrial systems and equipment for which historical operational data and failure information is available. This framework will help optimize health management and pave the way for predictive maintenance of industrial equipment.
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