Sequential Domain Adaptation for Fault Diagnosis in Rotating Machinery

Abstract

Fault diagnosis of the machinery system is essential to minimize the damage to the industrial field. Recently, with the development of computer and IoT technology, deep learning-based fault diagnosis has been widely researched. However, due to the domain shift, which changes the distribution of data under different operating conditions of the machinery system, the performance of the deep learning-based fault diagnosis algorithm decreases. This paper proposes a sequential domain adaptation to alleviate different distributions between different operating conditions. The proposed method has been validated in open-source datasets and shows a high performance compared to the other models.