Uncertainty Quantification and Interval Prediction of Equipment Remaining Useful Life Based on Semisupervised Learning

With the rapid development of sensing technology, deep learning (DL) methods have gained great popularity in remaining useful life (RUL) prediction of equipment. However, DL-based prediction methods generally cannot quantify uncertainties or provide reliable prediction intervals (PIs) for RUL. To tackle this problem, this article proposes an ensemble framework for uncertainty quantification and interval prediction of RUL based on semisupervised learning. First, the prediction variance is used to represent the aleatoric uncertainty, and a weighted negative log-likelihood loss function is designed to help the DL model to provide both the prediction value and the variance, thereby quantifying the aleatoric uncertainty in RUL prediction. For the issue that variance labels are difficult to obtain, the DL model is trained in a semisupervised manner to learn the prediction variance caused by data noise. Next, the Bootstrap method is applied to sample the training data with replacement to obtain several subsets, and several DL models are trained by these subsets, thereby quantifying the epistemic uncertainty in RUL prediction. Furthermore, PIs with 95% confidence are calculated based on these uncertainties to achieve RUL interval prediction of equipment. Finally, experiments are conducted on the turbofan engine dataset and CNC machine tool dataset, and the validity of the proposed method is confirmed.