A One-Class Generative Adversarial Detection Framework for Multifunctional Fault Diagnoses

In this article, fault diagnosis is of great significance for system health maintenance. For real applications, diagnosis accuracy suffers from unbalanced data patterns, where normal data are usually abundant than anomaly ones, leading to tremendous diagnosis obstacles. Therefore, it is challenging to use only normal data for fault diagnosis under this imbalanced condition. In addition, a single fault diagnosis model can only conduct one fault diagnosis task in most of cases. Accordingly, a one-class generative adversarial detection (OCGAD) framework based on semisupervised learning is proposed to learn one-class latent knowledge for dealing with multiple semisupervised fault diagnosis tasks, i.e., fault detection using only normal knowledge learning, novelty detection from unknown conditional data, and fault classification with unlabeled data. A bi-directional generative adversarial network (Bi-GAN) is first trained with only normal data. A one-class support vector machine is then established using features exacted by Bi-GAN from signals acquired from an attitude sensor for multifunctional fault detection. The presented OCGAD model is validated using an industrial robot with experiments of three fault detection tasks. The results demonstrate that the present model has good performance for dealing with multiple semisupervised diagnosis problems.

I. Introduction

As a branch of the fault diagnosis, anomaly detection [1] has been applied to handle the problem of finding outliers in data that do not conform to the expected behavior [2]. However, these outliers or anomalies are common to cause damage in the manufacturing systems, such as industrial robots. Condition monitoring systems are helpful in detecting anomalies and reducing maintenance costs [3]. Therefore, anomaly detection plays an important role in the industrial machinery fault diagnosis.