Classification with imperfect labels for fault prediction

Classification techniques have been widely used in fault prediction for industrial systems. However, an inherent issue with this approach is label imperfections in training data, since the line of demarcation between classes is determined based on field expert experience and maintenance capability. To address this issue we propose a noisy-label model in which the labeling noise function is derived from a point of view motivated by reliability analysis. We also present a novel label bootstrapping method that can better reflect the true uncertainty of the labeling process than the standard approach for addressing label imperfections. The proposed technique gives encouraging results on two industrial fault-prediction data sets.