An algorithm for data-driven prognostics based on statistical analysis of condition monitoring data on a fleet level

The availability of condition monitoring data for large sets of homogeneous products (in the following referred as a fleet) motivates the development of new data-driven prognostic algorithms. In this paper, an intuitive and an innovative data-driven algorithm to predict the health and, consequently, the Residual Useful Lifetime (RUL) of a product are proposed. The algorithm is based on the extraction and exploitation of knowledge at a fleet level. The fleet-specific usage and the degradation profile are extracted by statistically analyzing the condition monitoring data of all the products that´s belongs to the fleet. The extracted knowledge, in terms of statistical distribution of health condition and sampling time, is then exploited to predict the health and RUL of a product in the fleet. The algorithm described in this paper is able to predict the RUL of a product with a good credibility even for observation window lengths that are smaller compared to the lifetime of the product.