A Restricted-Learning Network With Observation Credibility Inference for Few-Shot Degradation Modeling

Multiple sensors are widely used in the monitoring of the degradation process and prediction of the remaining useful lifetime (RUL) of units in complex engineering systems. However, ensuring the prognostic performance with only a few units available remains difficult. Under a few-shot scenario, the discordant observations that exist in sensor data introduce considerable uncertainty into the degradation model, which leads to an empirical loss far from the expected loss. On the other hand, the learned degradation model tends to be overfitted on the limited available units and results in a biased model parameter distribution, which limits the model generalization capability on unseen units. To address these issues, this paper proposes a restricted-learning network with observation credibility inference (OCI) for few-shot degradation modeling. We initially introduce the OCI to figure out discordant observations from sensor data. Then, OCI is incorporated into restrictive learning through the deletion of discordant observations from sensor data, which enforces a prior distribution constraint on degradation model parameters to prevent overfitting. Finally, a posterior augmented classifier is learned to estimate health status based on the posterior sensor paths, and the RUL can be predicted subsequently. A case study that uses the degradation dataset of aircraft engines demonstrates the superiority of the proposed method over benchmark methods under few-shot scenarios.Note to Practitioners—This paper aims to develop a few-shot degradation modeling method for conducting status monitoring and RUL prediction. Specifically, the developed method addresses two challenging issues in practice: 1) How to figure out discordant observations exist in sensor data; 2) How to prevent overfitting issues under few-shot scenarios. To implement this method, four steps are included as follows: First, collect multiple sensor data and failure time of historical units. Second, construct the degradation model network, and train the network with restricted parameter distribution after deleting discordant observations. Third, construct and learn the classifier for predicting the probability of failure. Fourth, estimate the degradation status of in-service units, and predict the RUL based on the classifier. The proposed method is expected to be able to characterize various degradation processes and be applied to the degradation modeling of engineering systems with limited data available.