Probabilistic Deep Learning Based on Bayes by Backprop for Remaining Useful Life Prognostics of Consumer Electronics

As a medium of information exchange between the network world and the physical world, the reliability of consumer electronics has been widely concerned by researchers. Maintenance support based on remaining useful life (RUL) prediction is an important means to protect consumer electronics. However, most existing deep learning-based RUL prognostic methods can only perform point prediction of RUL by simply establishing a regression mapping between monitoring data and RUL. The lack of quantifying the uncertainty of prediction and measuring the confidence of the prediction model in decision-making makes these existing methods unreliable to maintenance activities. To this end, this paper proposes a probabilistic deep learning-based RUL prediction method via Bayes by Backprop. In this method, a deep convolutional neural network is integrated with a bidirectional gated recurrent network to explore long-term dependence and nonlinear mapping relationship in degraded time-sequence data. A reparameterization strategy is derived to endow the neural network with varying weights based on Bayesian variational inference to capture epistemic uncertainty in prediction. In addition, l1 norm penalty is used as a constraint of variational loss function to make the network sparse and reduce the computational cost. The effectiveness of the proposed method is verified on hard disk datasets.