Predicting the Best Units within a Fleet: Prognostic Capabilities Enabled by Peer Learning, Fuzzy Similarity, and Evolutionary Design Process

We analyze the task of selecting the most reliable units within a fleet of vehicles and formulate it as a prediction and classification problem. The prediction of each unit\´s remaining life is based on the identification of "peer" units, i.e. vehicles with similar utilization and maintenance records that are expected to behave similarly to the unit under consideration. With these peers, we construct local predictive models to estimate the unit\´s remaining life. We use evolutionary algorithms (EAs) to develop the criteria for defining peers and the relevance of each criterion in evaluating similarity with the unit. Each individual in the EAs population fully characterizes an instance-based fuzzy model that is used to predict the unit\´s remaining life. The precision of the selection of units with best-expected life provides the fitness value. We analyzed the performance of the evolutionary approach over two years of operation and maintenance data for a fleet of 1100 locomotives. The results illustrate the high accuracy and robustness of this approach. In the conclusion, we highlight the implications of this approach for supporting the lifecycle of the fuzzy models