Deep Model Based Domain Adaptation for Fault Diagnosis

TLDR

Machine‑learning methods are widely applied to fault diagnosis, yet distribution shifts between source and target domains often degrade performance. This study aims to mitigate such degradation by applying domain adaptation to fault‑diagnosis models. We propose a deep neural network that incorporates domain‑adaptation layers and hyperparameter‑optimization strategies to strengthen feature representation and improve target‑domain accuracy. Experiments on several real‑world datasets confirm that the proposed model and its hyperparameter‑search strategies achieve reliable, high classification accuracy in target domains.

Abstract

In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.