Normalized Recurrent Dynamic Adaption Network: A New Framework With Dynamic Alignment for Intelligent Fault Diagnosis

Abstract

In the field of intelligent fault diagnosis, distribution divergence always exists between the training and testing sets (which could be considered as a source domain with known labels and a target domain without labels), which will lead to a significant degradation in the diagnosis performance of deep network. Generally, this problem is solved by transfer learning. Specifically, adapt the marginal distribution or jointly align the marginal and conditional distributions of two domains so that the classifier trained by labeled source data merely can correctly classify target data. However, when aligning the marginal and conditional distributions simultaneously, people usually gives them the equal weight while it is not in accordance with the general situations. In this paper, we propose a new framework called normalized recurrent dynamic adaption network (NRDAN) for intelligent fault diagnosis which not only adapts the marginal and conditional distributions of two domains simultaneously but also estimates the relative importance of two distributions dynamically and quantitatively. This framework adopts long short-term memory (LSTM) as the base network combined with layer normalization (LN) and mainly consists of a feature extractor, a dynamic adaption module, and a classifier. Finally, extensive experiments including transfer tasks between not only various operating conditions but also different machines are conducted to comprehensively evaluate the proposed method.

Keywords

• Intelligent fault diagnosis
• deep learning
• transfer learning
• dynamic adaption
• long short-term memory