Minimax entropy-based co-training for fault diagnosis of blast furnace

Abstract

Due to the problems of few fault samples and large data fluctuations in the blast furnace (BF) ironmaking process, some transfer learning-based fault diagnosis methods are proposed. The vast majority of such methods perform distribution adaptation by reducing the distance between data distributions and applying a classifier to generate pseudo-labels for self-training. However, since the training data is dominated by labeled source domain data, such classifiers tend to be weak classifiers in the target domain. In addition, the features generated after domain adaptation are likely to be at the decision boundary, resulting in a loss of classification performance. Hence, we propose a novel method called minimax entropy-based co-training (MMEC) that adversarially optimizes a transferable fault diagnosis model for the BF. The structure of MMEC includes a dual-view feature extractor, followed by two classifiers that compute the feature’s cosine similarity to representative vector of each class. Knowledge transfer is achieved by alternately increasing and decreasing the entropy of unlabeled target samples with the classifier and the feature extractor, respectively. Transfer BF fault diagnosis experiments show that our method improves accuracy by about 5% over state-of-the-art methods.