Deep Semisupervised Domain Generalization Network for Rotary Machinery Fault Diagnosis Under Variable Speed

In recent years, deep learning has become a promising tool for rotary machinery fault diagnosis, but it works well only when testing samples and training samples are independent and identically distributed. In practice, rotary machinery usually works under variable speed. The change of speed leads to the variation of samples' distribution, which can significantly decrease the performance of the deep learning model. Scholars try to utilize transfer learning techniques for solving this problem. However, most exiting methods can just work well under target speed instead of all speed, while the target samples are always required in model training. In this article, a deep semisupervised domain generalization network (DSDGN) is proposed for rotary machinery fault diagnosis under variable speed, which can generalize the model to the fault diagnosis task under unseen speed. Under the setting of semisupervised domain generalization, only one fully labeled source (LS) domain data set and one totally unlabeled source (US) domain data set are available during training. To make full use of these data, the proposed method simultaneously utilizes Wasserstein generative adversarial network with gradient penalty (WGAN-GP)-based adversarial learning and pseudolabel-based semisupervised learning for training. The transmission and bearing fault diagnosis cases are utilized for evaluation. The comparative experiments indicate that the proposed method has a better performance than other state-of-the-art methods.

I. Introduction

Modern equipment is becoming more and more complex, precise, intelligent, and automated [1]. As a critical component of modern equipment, rotary machinery always works under harsh environment and varying duty, which leads to a great risk of failures [2]. The sudden breakdown of rotary machinery may cause enormous economic loss or even severe safety accident. Therefore, it is crucial to develop rotary machinery fault diagnosis technique for the operating reliability and the safety of equipment [3], [4]. To effectively diagnose the failure mode of rotary machinery, scholars have conducted many researches. The mainstream fault diagnosis methods can be roughly separated into four categories: signal processing-based method [5], [6], feature extraction-based method [7]–[9], traditional machine learning method [10]–[12], and deep learning method [13], [14]. Among these fault diagnosis methods, deep learning-based intelligent fault diagnosis methods have shown outstanding performance in recent years.