Event log modeling and analysis for system failure prediction

Abstract

Event logs, commonly available in modern mechatronic systems, contain rich information on the operating status and working conditions of the system. This article proposes a method to build a statistical model using event logs for system failure prediction. To achieve the best prediction performance, prescreening and statistical variable selection are adopted to select the best set of predictor events, coded as covariates in the statistical model. In-depth discussion of the prediction power of the model in terms of false alarm and misdetection probability is presented. Using a real-world example, the effectiveness of the proposed method is further confirmed.