A data-driven prognostics method for explicit health index assessment and improved remaining useful life prediction...

Although bearings offer a broad extent of applications and rank among the most-used elements in rotating machinery they also are the most vulnerable to failure. Consequently, "prognostics and health management (PHM)" of bearings has gained awareness in both academia and industry. As it aims to predict future failure events, "remaining useful life (RUL)" prediction is an important process to ensure a reliable and safe operation of bearings in the course of their degradation. However, accurate RUL prediction can hardly be carried out without an explicit health index that fully reflects the bearing's dynamic performance degradation process. Thus, obtaining an explicit health index is a major concern. This paper advocates a novel method to solve this issue. The "proposed method" is based on the ensemble of "deep autoencoder (DAE)" and "locally linear embedding (LLE)". To begin with, secondary features are extracted from the original unprocessed data obtained from sensors. These secondary features are used as inputs to the DAE where they become compressed to a more compact, lower-dimension form. Accordingly, the dimensionally reduced features are evaluated based on a trend factor with which higher-trend features are selected to enhance the accuracy and computational efficiency of the subsequent RUL prediction. The selected features are used as inputs for the LLE algorithm to determine a truly representative explicit health index which fully reflects the bearing's dynamic performance degradation. Having obtained the health index by the "proposed method", the RUL is finally predicted by employing the "long short-term memory (LSTM)" neural network. The obtained results from the experiment, authenticates the "effectiveness and superiority" of the "proposed method".