A new chiller fault diagnosis method under the imbalanced data environment via combining an improved generative adversarial network with an enhanced deep extreme learning machine

Abstract

The existing chiller fault diagnosis approaches often ignore the problem of data imbalance of chiller, which leads to low accuracy in diagnosing minority class fault samples. To conquer this issue, this paper proposes an improved generative adversarial network (IGAN) with an enhanced deep extreme learning machine (EDELM) method. Firstly, to better learn the latent structure of chiller fault data, the multi-head attention (MHA) mechanism is integrated into the traditional generative adversarial network (GAN) method to generate new samples that are more in line with the distribution of minority class fault samples for the purpose of obtaining a rebalanced dataset. Secondly, to fully handle the nonlinear features hidden in the massive chiller data, the deep extreme learning machine (DELM) basic classifier is trained on the rebalanced dataset. To enhance more attention to the misclassified samples, the adaptive boosting (AdaBoost) ensemble strategy is employed to train multiple DELM basic classifiers by updating the sample weights following the classification results through the iterative rounds. The voting weight of the current DELM basic classifier is given according to its fault diagnosis accuracy. Finally, multiple DELM basic classifiers are ensembled according to their voting weights to obtain the final ensemble classifier. The pattern of the snapshot sample is determined through the weighted voting strategy. Detailed experimental results based on the research project 1043 (RP-1043) conducted by the American society of heating, refrigeration, and air conditioning engineers (ASHRAE) confirm the effectiveness of the proposed IGAN-EDELM approach under imbalanced data environments.