Reliability of prognostics and health management systems (PHM) relies upon accurate understanding of critical components' degradation process to predict the remaining useful life (RUL). Traditionally, degradation process is represented in the form of data or expert models. Such models require extensive experimentation and verification that are not always feasible. Another approach that builds up knowledge about the system degradation over the time from component sensor data is known as data driven. Data driven models, however, require that sufficient historical data have been collected. In this paper, a two phases data driven method for RUL prediction is presented. In the offline phase, the proposed method builds on finding variables that contain information about the degradation behavior using unsupervised variable selection method. Different health indicators (HI) are constructed from the selected variables, which represent the degradation as a function of time, and saved in the offline database as reference models. In the online phase, the method finds the most similar offline health indicator, to the online health indicator, using k-nearest neighbors (k-NN) classifier to use it as a RUL predictor. The method finally estimates the degradation state using discrete Bayesian

In this paper, a data-driven method for remaining useful life (RUL) prediction is presented. The method learns the relation between acquired sensor data and end of life time (EOL) to predict the RUL. The proposed method extracts monotonic trends from offline sensor signals, which are used to build reference models. From online signals the method represents the uncertainty about the current status, using discrete Bayesian filter. Finally, the method predicts RUL of the monitored component using integrated method based on K-nearest neighbor (k-NN) and Gaussian process regression (GPR). The performance of the algorithm is demonstrated using two real data sets from NASA Ames prognostics data repository. The results show that the algorithm obtain good results for both application.