Wang Meng, Yu Jiong, Leng Hongyong, Du Xusheng, Liu Yiran
School of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.
School of Software, Xinjiang University, Urumqi, 830046, China.
Sci Rep. 2024 Mar 3;14(1):5206. doi: 10.1038/s41598-024-55620-6.
The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.
轴承故障诊断技术的研究与应用对于提高设备可靠性、延长轴承寿命以及降低维护成本至关重要。然而，大多数现有方法在区分正常运行和故障状态下机器的信号时面临挑战，导致检测结果不稳定。为解决这一问题，本研究提出了一种基于图神经网络和集成学习的轴承故障检测新方法。我们的主要贡献是一种基于新颖随机性的合成方法，该方法将欧几里得结构数据转换为图形格式，以便由图神经网络进行处理，并进行特征融合，同时还提出了一种专门为轴承故障诊断设计的用于异常检测的新集成学习策略。这种方法在准确识别轴承故障方面取得了重大进展，突出了我们的研究在改进诊断方法中的关键作用。
