Fault Diagnosis of Rotating Machinery under Complex Conditions Based on Multi-Scale Convolutional Neural Networks

Abstract

In complex operational scenarios involving variable speeds, burdens, and noise, rotating machinery necessitates extended maintenance. Extracting stable and effective fault-sensitive features under such intricate conditions presents a significant challenge. To tackle this issue, the paper introduces the Multi-Scale Convolutional Neural Network (MSCNN) model tailored specifically for such complexities. The approach in this paper simultaneously captures multi-scale vibration signal features using the innovative Multi-Scale Bifurcation (MSB) module and subsequently aggregates them through a multi-scale fusion layer. This model effectively addresses the common problem of low CNN accuracy in fault diagnosis. The paper validates methodology using a bearing dataset provided by Case Western Reserve University (CWRU) and demonstrates superior performance compared to classical CNN models and other alternatives, achieving an impressive 99.56% classification accuracy for normal signals.