This thesis develops and implements a novel deep learning framework for health state estimation of physical assets based on big machinery data. The framework integrates convolutional and recurrent neural networks to extract temporal and spatial characteristics from sensor data. The framework is validated on two benchmark datasets: a C-MAPSS turbofan engine dataset and a lithium-ion battery dataset. Models trained on the datasets provide state-of-the-art results for metrics like root mean square error while maintaining low uncertainty. The results demonstrate that the deep learning framework can successfully obtain relationships from sensor data for reliability assessment of different systems.

A Deep Learning Based Framework For Physical Assets Health Prognostics Under Uncertainty For Big Machinery Data PDF

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