Kiakojouri Amirmasoud, Wang Ling
National Centre for Advanced Tribology at Southampton (nCATS), School of Engineering, University of Southampton, Southampton SO17 1BJ, UK.
Sensors (Basel). 2025 Apr 9;25(8):2378. doi: 10.3390/s25082378.
Rolling element bearings (REBs) are critical components in rotating machinery and a leading cause of machine failures. Traditional fault detection methods rely on signal processing, but advances in machine learning (ML) and deep learning (DL) have dramatically improved diagnostic accuracy. However, existing DL models struggle with data availability, generalization, and domain adaptation, making industrial applications challenging. This study proposes a convolutional neural network (CNN) model trained on numerically simulated vibration data generated for a wide range of bearing designs. A novel hybrid signal processing method is employed to enhance feature extraction and reduce domain shifts between simulated and real-world data. The optimized CNN model, trained on simulated data, is tested using experimental and real-world vibration signals from laboratory bearings and jet engine components. The results show high classification accuracy using data from the Case Western Reserve University experimental dataset and successful fault detection in real-world Safran jet engine ground tests. The findings demonstrate the effectiveness of the developed CNN-based model for bearing fault classification, tackling training data scarcity and generalizability challenges while contributing to the development of intelligent fault diagnosis models for several industrial applications.
滚动元件轴承（REB）是旋转机械中的关键部件，也是机器故障的主要原因。传统的故障检测方法依赖于信号处理，但机器学习（ML）和深度学习（DL）的进展显著提高了诊断准确性。然而，现有的深度学习模型在数据可用性、泛化能力和领域适应性方面存在困难，这使得工业应用具有挑战性。本研究提出了一种卷积神经网络（CNN）模型，该模型基于为广泛的轴承设计生成的数值模拟振动数据进行训练。采用了一种新颖的混合信号处理方法来增强特征提取，并减少模拟数据与实际数据之间的领域差异。在模拟数据上训练的优化CNN模型，使用来自实验室轴承和喷气发动机部件的实验和实际振动信号进行测试。结果表明，使用凯斯西储大学实验数据集的数据具有很高的分类准确率，并且在赛峰喷气发动机的实际地面测试中成功检测到故障。研究结果证明了所开发的基于CNN的模型在轴承故障分类方面的有效性，解决了训练数据稀缺和泛化能力挑战的问题，同时为多个工业应用的智能故障诊断模型的发展做出了贡献。
