Fault Detection in Ball Bearings

2022/09/19 by Joshua Pickard, Pickard, Joshua, Sarah Moll +1
Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Gear and Bearing Dynamics Analysis #Industrial Vision Systems and Defect Detection #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2209.11041

openalex publication_date 2022/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machines. This paper explores the construction of vibration images, a preprocessing technique that has been previously used to train convolutional neural networks for ball bearing joint IFD. The main results demonstrate the robustness of this technique by applying it to a larger dataset than previously used and exploring the hyperparameters used in constructing the vibration images.

Related

• FaultNet: A Deep Convolutional Neural Network for bearing fault\n classification
• A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals
• FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN) based Ball-Bearing Failure Detection Method
• Deep learning-based fault identification in condition monitoring
• Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data