Industrial fault diagnosis based on active learning and semi-supervised learning using small training set

Abstract

Industrial fault diagnosis has been investigated for many years, and many approaches have been proposed to identify industrial faults. However, the size of the actual training set is usually small, which severely degrades the performance of existing fault diagnostic models. To solve this problem, a new fault diagnosis method was proposed based on active and semi-supervised learning. First, uncertain unlabelled samples were selected by estimating the first two values in the class probability distribution of the samples. They were labelled by experts to update the performance of the models learned from a small training set. Second, heterogeneous classifiers were adopted to increase the diversity of the base classifiers, and noise samples were deleted using a sample pruning operation. The weights of the base classifier were designed for ensemble learning based on the test error rates. An evaluation using the Case Western Reserve University and Intelligent Maintenance Systems data showed that the performance of the proposed method was better than those of the other methods in the experiment. The experimental results showed that this study provided a promising and useful methodology for fault diagnosis under a small training set.