Generalized Transfer Extreme Learning Machine for Unsupervised Cross-Domain Fault Diagnosis With Small and Imbalanced Samples

In most engineering scenarios, the collected datasets typically have small and imbalanced characteristics and usually possess different data distributions between variable working conditions. Thus, several existing diagnosis models trained by small and imbalanced samples under one working condition may suffer from low accuracy for fault data under another working condition. To effectively alleviate such a problem, a novel domain adaptation method, referred to as a generalized transfer extreme learning machine (G-TELM), is proposed in this study. First, in a random feature space, marginal and conditional distribution discrepancy between source and target domains is minimized based on projected maximum mean discrepancy (MMD). Second, in order to give varied attention to different faulty samples, a cost-sensitive source domain classifier is utilized to reduce the difference between types of misclassification loss for small and imbalanced source data. Third, misclassification loss of target data is used to minimize the difference between the network's outputs and prediction labels of target data. Finally, L2-norm regularization is leveraged to constrain network output weights, and they are iteratively updated to optimize the transferability of model G-TELM. Experimental results of 168 transfer tasks with varying difficulty verify that the proposed model G-TELM can achieve an accurate diagnosis and outperform several state-of-the-art domain adaptation methods.