A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation

Data driven fault diagnosis has attracted a lot of attention in recent years owing to its intelligent and accurate detection of fault categories. However, it is a challenge for its applications in real world. The abundant labeled data is extremely necessary for data driven fault diagnosis to train a favorable model. Even though enough labeled data is prepared for training a model, we still cannot ensure the data used for training and testing draw from identical distribution. In other words, the labeled source domain has different distribution compared with the unlabeled target domain. In this paper, we introduce the domain adaptation strategy into deep neural networks to propose a deep domain adaptation architecture, which realizes to learn knowledge from the labeled source domain to facilitate the target classification. In the proposed model, the conditional and marginal distribution are adapted together in multiple layers of neural network, which uses MMD to measure the distribution discrepancy. Besides, the relative importance between marginal and conditional distributions is explored, and an adaptively weighted strategy is further introduced to learn the relative importance of the two distributions. To evaluate the proposed method, we conduct the simulations on different workloads, sensor deployment locations, and even different platforms. The results show the superiority of the proposed model to other intelligent fault diagnosis methods, meanwhile verify the necessity of marginal and conditional distribution adaptation and adaptive weighted strategy. (c) 2020 Elsevier B.V. All rights reserved.