Twin Broad Learning System for Fault Diagnosis of Rotating Machinery

Abstract

As rotating machines are more and more widely used in modem equipment, their fault diagnosis is important to guarantee the instrument’s reliability and safety. Although intelligent fault diagnosis based on deep learning (DL) has achieved great performance in many fault diagnosis tasks, these models are highly dependent on the large-scale training dataset and can be time-consuming during training and testing. These limitations largely affect the efficiency of diagnosis in real-world applications. An effective alternative way is to develop the single-layer feedforward networks (SLFNs) based Broad Learning System (BLS) for fault diagnosis tasks, which enjoy fast training speed as well as strong generalization ability. However, we found that the least square classifier used in classical BLS can have a problem distinguishing the possibly overlapping fault patterns. In this paper, we propose a novel twin broad learning system (TBLS) for fault diagnosis of rotating machinery. Rather than using the classical least square method, the proposed TBLS learns to find two nonparallel hyper-planes to deal with the classification problem, which shows stronger generalization ability in fault diagnosis problems. Experimental results on two fault diagnosis benchmark datasets for typical rotating machinery further illustrate the effectiveness of the TBLS methods, which offer a high-efficiency solution for rotating machinery fault diagnosis.