Cross-Domain Adaptation using Domain Interpolation for Rotating Machinery Fault Diagnosis

In recent years, deep neural networks have been successfully used for machine failure diagnosis. However, changes in data distribution related to variations in diverse working environments adversely impact the troubleshooting performance. Consequently, cross-domain adaptation, which minimizes inconsistencies in data distribution, is an important issue. We propose an adversarial domain adaptation-based interpolation method that minimizes domain discrepancy by mixing the data and creating a continuous space between different distributed domains using two regularizations. Data interpolation algorithms generate data that interpolate regions through mutual and coherent reconstructions between labeled and target data. This ensures data transformation diversity and creates a continuous latent space. The generated data achieve the two goals of domain invariance and improved class classification performance, through categorical and domain-based regularization. Extensive experiments upon public and real-world equipment datasets demonstrate that the proposed approach achieves an approximately 10-15% improvement in classification performance when compared to conventional methods at various levels of domain portability and data complexity. Experimental results show that the proposed method is an effective solution that can be applied to actual industrial sites.