A New Strategy for Rotating Machinery Fault Diagnosis Under Varying Speed Conditions Based on Deep Neural Networks and Order Tracking

Abstract

Rotating machines are widely used in industry and often work under harsh and varying speed conditions. Fault diagnosis under varying speed conditions is needed to prevent major shutdowns. This paper aims to develop an intelligent rotating machinery fault diagnosis strategy based on deep neural networks (DNNs) and order tracking (OT). The developed strategy can automatically conduct rotating machinery fault diagnosis under both constant and varying speed conditions. Case studies on a rolling element bearing dataset and a fixed-shaft gearbox dataset show the superiority in diagnosis accuracy of the proposed strategy over reported approaches.