Yang Chuanlei, Wang Hechun, Gao Zhanbin, Cui Xinjie
College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, People's Republic of China.
R Soc Open Sci. 2018 May 23;5(5):180066. doi: 10.1098/rsos.180066. eCollection 2018 May.
As the main cause of failure and damage to rotating machinery, rolling bearing failure can result in huge economic losses. As the rolling bearing vibration signal is nonlinear and has non-stationary characteristics, the health status information distributed in the rolling bearing vibration signal is complex. Using common time-domain or frequency-domain approaches cannot easily enable an accurate assessment of rolling bearing health. In this paper, a novel rolling bearing fault diagnostic method based on multi-dimensional characteristics was developed to meet the requirements for accurate diagnosis of different fault types and severities with real-time computational performance. First, a multi-dimensional feature extraction algorithm based on entropy characteristics, Holder coefficient characteristics and improved generalized fractal box-counting dimension characteristics was performed to extract the health status feature vectors from the bearing vibration signals. Second, a grey relation algorithm was employed to achieve bearing fault pattern recognition intelligently using the extracted multi-dimensional feature vector. This experimental study has illustrated that the proposed method can effectively recognize different fault types and severities after integration of the improved fractal box-counting dimension into the multi-dimensional characteristics, in comparison with existing pattern recognition methods.
作为旋转机械故障和损坏的主要原因，滚动轴承故障会导致巨大的经济损失。由于滚动轴承振动信号具有非线性和非平稳特性，分布在滚动轴承振动信号中的健康状态信息十分复杂。使用常见的时域或频域方法难以轻松实现对滚动轴承健康状况的准确评估。本文开发了一种基于多维特征的新型滚动轴承故障诊断方法，以满足对不同故障类型和严重程度进行准确诊断并具备实时计算性能的要求。首先，执行一种基于熵特征、赫尔德系数特征和改进的广义分形盒计数维特征的多维特征提取算法，从轴承振动信号中提取健康状态特征向量。其次，采用灰色关联算法，利用提取的多维特征向量智能地实现轴承故障模式识别。该实验研究表明，与现有模式识别方法相比，所提出的方法在将改进的分形盒计数维纳入多维特征后，能够有效地识别不同的故障类型和严重程度。
