An adaptive and tacholess order analysis method based on enhanced empirical wavelet transform for fault detection of bearings with varying speeds
An adaptive and tacholess order analysis method based on enhanced empirical wavelet transform for fault detection of bearings with varying speeds
复制标题
基于增强经验小波变换的自适应变速轴承故障检测无转速阶次分析方法
DOI：
10.1016/j.jsv.2017.08.003
复制
发表时间：
2017-11-24
影响因子：
4.7
通讯作者：
Meng, Guang
中科院分区：
文献类型：
--
作者：
Hu, Yue;Tu, Xiaotong;Meng, Guang
基于时频表示的阶比跟踪方法被认为是检测变转速轴承故障的有效工具。在传统的阶数跟踪方法中，由于技术和经济上的限制，需要一个转速表来获得实际中难以满足的瞬时速度。近年来发展了一些无速度阶数跟踪方法。在这些方法中，瞬时频率脊线提取是最重要的部分之一。然而，目前的脊线提取方法对噪声敏感，容易陷入局部最优。由于信号中噪声和其他无关成分的存在，很难从包络谱或包络阶谱中检测出轴承故障特征。为了克服上述缺点，本文提出了一种自适应无速度阶次分析方法。该方法采用一种新的基于动态路径优化的脊线提取算法来估计瞬时频率。该算法克服了现有脊线提取算法的不足。同时，采用改进的经验小波变换(EEWT)算法提取轴承故障特征。仿真和实验结果表明，该方法对噪声具有较强的鲁棒性，对变速条件下的轴承故障检测具有较好的效果。(C)2017爱思唯尔有限公司。保留所有权利。
The order tracking method based on time-frequency representation is regarded as an effective tool for fault detection of bearings with varying rotating speeds. In the traditional order tracking methods, a tachometer is required to obtain the instantaneous speed which is hardly satisfied in practice due to the technical and economical limitations. Some tacholess order tracking methods have been developed in recent years. In these methods, the instantaneous frequency ridge extraction is one of the most important parts. However, the current ridge extraction methods are sensitive to noise and may easily get trapped in a local optimum. Due to the presence of noise and other unrelated components of the signal, bearing fault features are difficult to be detected from the envelope spectrum or envelope order spectrum. To overcome the abovementioned drawbacks, an adaptive and tacholess order analysis method is proposed in this paper. In this method, a novel ridge extraction algorithm based on dynamic path optimization is adopted to estimate the instantaneous frequency. This algorithm can overcome the shortcomings of the current ridge extraction algorithms. Meanwhile, the enhanced empirical wavelet transform (EEWT) algorithm is applied to extract the bearing fault features. Both simulated and experimental results demonstrate that the proposed method is robust to noise and effective for bearing fault detection under variable speed conditions. (C) 2017 Elsevier Ltd. All rights reserved.