Domain distribution variation learning via adversarial adaption for helicopter transmission system fault diagnosis

Deep Learning-based fault diagnosis has aroused widespread attention in machine fault diagnosis. Helicopter is an important transport for its special purpose. How to ensure its normal operation is a challenging task. Nevertheless, the existing research mainly focuses on single bearing or gear of gearbox, while there are few reports about intelligent fault diagnosis of bearing and shaft in helicopter transmission system. Furthermore, traditional domain adaption-based fault diagnosis methods assume that the source machine and target machine have the same class distribution. Besides, the latent distributed feature of target domain data is rarely developed, and the distributional discrepancy of shared-class samples during domain adaption with outlier class is rarely considered. To address these issues, we propose a domain distribution variation learning (DDVL) via adversarial adaption for helicopter transmission system fault diagnosis in this paper. Hereinto, the distributional discrepancy of partial shared-class is measured by adversarial training during open-set domain adaption. Especially, a self-supervised learning framework via the pseudo-label and weight normalization is proposed to develop the latent distribution feature of target data with unknown labels. The case study from a simulated helicopter transmission system is used to verify the effectiveness of DDVL. Our method outperforms other comparison methods in different case studies for helicopter transmission system fault diagnosis.