Work

Gear Fault Detection Based on Convolutional Neural Networks and Support Vector Machines

Year: 2024

Type: article

Abstract: As a critical component of mechanical transmission systems, gears play a vital role in ensuring industrial production runs smoothly.Undetected gear failures can lead to mechanical breakdowns, production interruptions, and even safety hazards.Therefore, an efficient gear fault detection method is essential for maintaining industrial continuity and safety.This paper proposes a hybrid model that integrates convolutional neural networks (CNN) and support vector machines (SVM) for gear fault detection.The model leverages CNNs to automatically extract key features from vibration signals, while SVMs enhance classification accuracy, resulting in a high-precision fault diagnosis system.On a publicly available gear fault dataset, the proposed model achieved an impressive accuracy of 0.9922, significantly outperforming single-classifier models.Moreover, the model exhibits a short training time, demonstrating its computational efficiency.This research provides an effective and automated approach to gear fault detection, offering significant potential for industrial applications.

Source: International Journal of Knowledge and Innovation Studies

DOI: 10.56578/ijkis020405

Authors: Yeqi Fei, Ziyang Meng, Tianxi Chen, Pengfei Zhao

Institutions: Nanjing Forestry University, Nanjing University of Science and Technology ZiJin College

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4408462403