A fine-tuning framework for cross-domain RUL prediction based on relevant health indicators

Bearings are crucial elements in mechanical equipment, and their deterioration can result in equipment malfunction or failure. Therefore, precise analysis of the degradation process and proactive prediction of the remaining useful life are essential measures to prevent significant accidents. Practically, the intricacy and fluctuation of operational circumstances result in cross-domain issues in the training and test datasets, meaning that the source and target domains have inconsistent distributions. The disparities from data collection to feature extraction stages might exacerbate the discrepancies between the two domains. Additionally, selecting metrics can restrict the effectiveness of cross-domain prediction. To solve the above problems, this paper proposes a cross-domain transfer prediction method based on generalized HI for fine-tuning. Initially, multidimensional characteristics are retrieved, followed by acquiring HI metrics using a secondary optimization indicator and correlation extraction. Ultimately, the original dataset from the source domain is expanded to enhance the accuracy of prediction through time-series data augmentation. The effectiveness and practicality of the suggested strategy are convincingly showcased through experiments conducted on the PHM2012 dataset.