Statistical Knowledge Integration into Neural Networks: Novel Neuron Units for Bearing Prognostics
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Abstract
Prognostics and Health Management (PHM) is a framework that assesses the health condition of complex engineering assets to ensure proper reliability, availability, and maintenance. PHM can be used to determine how long a machine can function before failure by predicting the Remaining Useful Life (RUL). Neural networks have been used for RUL prediction, but these data-driven models rely solely on data to explicitly integrate knowledge. Recently, authors have proposed physics-informed neural networks (PINNs) to address this limitation. PINNs are neural networks that incorporate expert knowledge and physics in different ways (observational,inductive, and learning bias). Despite their significance, these models tend to be case-dependent and challenging to configure. In this work, we propose statistical neuron units that can be integrated into any neural network. The proposed neuron units extract features from raw data using various statistical functions. Importantly, these modules can be located in different parts of the neural network, and they can be optimized automatically by backpropagating the modules’ weights during training. In a study involving bearing degradation behavior, we compare a classical neural network with our modular
version. Our proposed RUL estimation model outperformed the baseline, with a reduction of 13% in the root mean square
error and a reduction of 7% in the mean absolute error. We also observe an increase of 40% and 21% for the α − λ accuracy metric for an α equal to 0.1 and 0.2 respectively. Our code is available publicly on Github.
How to Cite
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Feature extraction, knowledge integration, opotimization of parameters, interpretability, accuracy, modularity, neural network
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