Partial Transfer Fault Diagnosis by Multiscale Weight-Selection Adversarial Network
Quan Qian, Yi Qin, Jun Luo, Shilong Wang
Abstract
Most of domain adaptation (DA) methods just focus on the case that the label spaces of two domains are identical. However, it is more valuable for studying partial DA in practical engineering. Whereupon, a novel multiscale weight-selection adversarial network (MWSAN) is proposed to enhance the effect of partial DA. MWSAN is constructed via the designed multiscale domain adversarial network (MDAN) and the multiscale weight-selection mechanism of instance and class. MDAN is innovatively built to avoid the overfitting of a single classifier and strengthen the domain confusion. For the weight-selection of instance, the instance weights are directly obtained by the probability output of MDAN. For the weight-selection of class, the possible Gaussian distributions of target-domain samples is estimated by Gaussian mixture model, and the Gaussian distribution of each class of source-domain samples is computed by maximum likelihood estimation, then the Wasserstein distance is employed to computed the class weights. With the multiscale backbone network and weighting mechanism, MWSAN can achieve the partial DA to the larger degree. The proposed method is applied to the partial transfer fault diagnosis of planetary gearboxes with the unlabeled target-domain samples, and the experimental results indicate that MWSAN outperforms other typical DA methods.