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Towards Predicting the Life of an Engine: A Deep Learning Approach
Abstract
Predictive maintenance has attracted many researchers with the increased growth in the digitization of industrial, locomotive, and aviation fields. Simultaneously, extensive research in deep learning model development to its deployment has made its way to industrial applications with unprecedented accuracy. The most crucial task in predictive maintenance is to predict the machine's remaining useful life, yet the most beneficial one. In this chapter, the authors address the problem of predicting the remaining lifecycle of an engine using its sensor data. The authors provide practical implementation of predicting the RUL of an engine by proposing a deep learning-based framework on the open-source benchmark NASA's Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) engine dataset, which contains sensor information of around 100 engines with 22 sensors. The proposed framework uses the bi-directional long short term memory algorithm. The authors optimize hyperparameters using advanced deep learning frameworks.
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