Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis under Different Working Conditions

The demand for transfer learning methods for mechanical fault diagnosis has considerably progressed in recent years. However, the existing methods always depend on the maximum mean discrepancy (MMD) in measuring the domain discrepancy. But MMD can not guarantee the different domain features to be similar enough. Inspired by generative adversarial networks (GAN) and domain adversarial training of neural networks (DANN), this study presents a novel deep adaptive adversarial network (DAAN). The DAAN comprises a condition recognition module and domain adversarial learning module. The condition recognition module is constructed with a generator to extract features and classify the health condition of machinery automatically. The domain adversarial learning module is achieved with a discriminator based on Wasserstein distance to learn domain-invariant features. Then spectral normalization (SN) is employed to accelerate convergence. The effectiveness of DAAN is demonstrated through three transfer fault diagnosis experiments, and the results show that the DAAN can converge to zero after approximately 15 training epochs, and all the average testing accuracies in each case can achieve over 92%. It is expected that the proposed DAAN can effectively learn domain-invariant features to bridge the discrepancy between the data from different working conditions.