In modern industry, the accurate prediction of remaining useful life(RUL) contributes to the equipment safety and economic effectiveness. Aiming at the RUL prediction, this paper proposed a Bayesian temporal convoluti...
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For remaining useful lifetime (RUL) prediction with multi-channel sensory data, long-term prediction has more uncertainty than short-term prediction. In this paper, the ratio of mean to variance was considered to meas...
ISBN:
(数字)9781509066315
ISBN:
(纸本)9781509066322
For remaining useful lifetime (RUL) prediction with multi-channel sensory data, long-term prediction has more uncertainty than short-term prediction. In this paper, the ratio of mean to variance was considered to measure the uncertainty propagation rate (UPR) of RUL prediction over time. Furthermore, we use a recurrent neural network (RNN) as the linking function for the mean of inverse Gaussian distributed RUL to construct a two-stage hybrid model. Later the RNN and the UPR are jointly trained with sensory data and failure records via alternating minimization. Proposed algorithms are validated in a simulation test.
In modern industry, the accurate prediction of remaining useful life(RUL) contributes to the equipment safety and economic effectiveness. Aiming at the RUL prediction, this paper proposed a Bayesian temporal convoluti...
详细信息
ISBN:
(纸本)9781665424509
In modern industry, the accurate prediction of remaining useful life(RUL) contributes to the equipment safety and economic effectiveness. Aiming at the RUL prediction, this paper proposed a Bayesian temporal convolutional network (BayesianTCN) under the Bayesian deep learning framework. BayesianTCN outputs not only the RUL prediction, but also the associated confidence interval by Monte-Carlo simulation. This quantifies the RUL prediction uncertainty. Experimental results on CMAPSS datasets show that our model has higher fitting degree and lower uncertainty than BayesianLSTM, and performs well whether in simple or complex conditions.
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