A Novel Semi-Supervised Fault Diagnosis Method for Unbalanced Data

In modern industrial processes, class imbalance occurs when there is a significant disparity in the number of instances between different classes. Current approaches for handling this problem cannot work effectively due to the invalid instance replenishment strategy for rare categories and even exacerbate class imbalance issues. Therefore, this work presents a novel semi-supervised fault diagnosis (FD) method to address imbalances in FD data by leveraging extensive unlabeled samples. Inspired by adversarial discriminative domain adaptation learning, the proposed approach includes a distribution alignment model for extracting domain-invariant fault features from unlabeled data. Additionally, a soft threshold selection strategy is introduced to strategically select unlabeled fault samples, ensuring an abundance of samples for rare categories and enriching their distribution. Extensive experiments on the two industrial process datasets, including a real-world hot rolling of steel process and a well-established public Tennessee Eastman process, demonstrate the effectiveness of the proposed method in alleviating imbalances and utilizing unlabeled samples, establishing its superiority over existing methods. The code is publicly available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Ticuby/SFDM</uri>.