A multi-indicator modeling method for similarity-based residual useful life estimation with two selection processes

The similarity-based residual useful life prediction (SbRLP) approach, a burgeoning technique, plays an increasingly important role in the residual useful life (RUL) prediction. Currently, studies on taking (a) the selection of degradation indicatorsf; and (b) the selection of reference samples into account are rare. While they have positive influence on improving the performance of the SbRLP method with multiple degradation indicator (MSbRLP). Driven by the abovementioned gap, two novel selection processes are advanced by this article and thus an improved MSbRLP method is developed. Then, in case study of the gyroscope RUL estimation, two selection processes and the improved MSbRLP method are illustrated to be reasonable and effective by comparison with the classical MSbRLP method. Furthermore, implementing results reveal that the existence of degradation indicator having bad reflection effect will truly increase the recognition error of the degradation state and hence decrease the accuracy of RUL estimation. © 2018, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.