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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Breakdowns and unplanned shutdowns in industrial processes and equipment can lead to significant loss of availability and revenue. It is imperative to perform optimal maintenance of such systems when signs of abnormal behavior are detected and before they propagate and lead to catastrophic failure. This is particularly challenging in systems with interconnected multiple components as it is difficult to isolate the effect of one component on the operation of other components in the system. In this work, an ensemble approach based on Cascaded Convolutional neural network and Long Short-term Memory (CC-LSTM) network models is proposed for detecting and predicting the time of onset of faults in interconnected multicomponent systems. The performance of the ensemble CC-LSTM model was demonstrated on an industrial 4-component system and was found to improve the accuracy of onset time predictions by ~15% compared to individual CC-LSTM models and ~25-40% compared to commonly used deep learning techniques such as dense neural networks, convolutional neural networks and LSTMs. The CC-LSTM and the ensemble models also had the lowest missed detection rates and zero false positive rates making them ideal for real-time monitoring and fault detection in multicomponent systems.