Selecting the Best Units in a Fleet: Performance Prediction from Equipment Peers

摘要

We focus on the problem of selecting the few vehicles in a fleet that are expected to last the longest without failure. The prediction of each vehicle's remaining life is based on the aggregation of estimates from 'peer' units, i.e. units with similar design, maintenance, and utilization characteristics. Peers are analogous to neighbors in Case-Based Reasoning, except that the states of the peer units are constantly changing with time and usage. We use an evolutionary learning framework to update the similarity criteria for peer identification. Results indicate that learning from peers is a robust and promising approach for the usually data-poor domain of equipment prognostics. The results also highlight the need for model maintenance to keep such a reasoning system vital over time.