Li Jingchao, Ying Yulong, Ren Yuan, Xu Siyu, Bi Dongyuan, Chen Xiaoyun, Xu Yufang
College of Electronic and Information Engineering, Shanghai Dianji University, Shanghai, People's Republic of China.
School of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, People's Republic of China.
R Soc Open Sci. 2019 Feb 20;6(2):181488. doi: 10.1098/rsos.181488. eCollection 2019 Feb.
Rolling bearing failure is the main cause of failure of rotating machinery, and leads to huge economic losses. The demand of the technique on rolling bearing fault diagnosis in industrial applications is increasing. With the development of artificial intelligence, the procedure of rolling bearing fault diagnosis is more and more treated as a procedure of pattern recognition, and its effectiveness and reliability mainly depend on the selection of dominant characteristic vector of the fault features. In this paper, a novel diagnostic framework for rolling bearing faults based on multi-dimensional feature extraction and evidence fusion theory is proposed to fulfil the requirements for effective assessment of different fault types and severities with real-time computational performance. Firstly, a multi-dimensional feature extraction strategy on the basis of entropy characteristics, Holder coefficient characteristics and improved generalized box-counting dimension characteristics is executed for extracting health status feature vectors from vibration signals. And, secondly, a grey relation algorithm is used to calculate the basic belief assignments (BBAs) using the extracted feature vectors, and lastly, the BBAs are fused through the Yager algorithm for achieving bearing fault pattern recognition. The related experimental study has illustrated the proposed method can effectively and efficiently recognize various fault types and severities in comparison with the existing intelligent diagnostic methods based on a small number of training samples with good real-time performance, and may be used for online assessment.
滚动轴承故障是旋转机械故障的主要原因，并会导致巨大的经济损失。工业应用中对滚动轴承故障诊断技术的需求日益增加。随着人工智能的发展，滚动轴承故障诊断过程越来越被视为一种模式识别过程，其有效性和可靠性主要取决于故障特征主导特征向量的选择。本文提出了一种基于多维特征提取和证据融合理论的滚动轴承故障诊断新框架，以满足有效评估不同故障类型和严重程度并具有实时计算性能的要求。首先，执行基于熵特征、Holder系数特征和改进的广义盒计数维特征的多维特征提取策略，从振动信号中提取健康状态特征向量。其次，使用灰色关联算法利用提取的特征向量计算基本置信分配（BBA），最后，通过Yager算法融合BBA以实现轴承故障模式识别。相关实验研究表明，与现有的基于少量训练样本的智能诊断方法相比，该方法能够有效且高效地识别各种故障类型和严重程度，具有良好的实时性能，可用于在线评估。
