Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis
Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis
复制标题
盲振动分量分离和非线性特征提取应用于齿轮箱多故障诊断的非平稳振动信号
DOI：
10.1016/j.measurement.2012.06.013
复制
发表时间：
2013-01-01
期刊：
影响因子：
5.6
通讯作者：
Li, Li
中科院分区：
文献类型：
--
作者：
Li, Zhixiong;Yan, Xinping;Li, Li
齿轮箱尤其是齿轮和轴承的故障诊断对于长期安全运行至关重要。齿轮箱的意外损坏可能会使整个输电线路中断。因此，工程师和研究人员必须及时监测齿轮箱的健康状况，以消除即将发生的故障。然而，有用的故障检测信息往往淹没在沉重的背景噪声。因此，本文提出了一种基于盲源分离和非线性特征提取技术的齿轮箱故障检测方法。通过对非平稳振动信号的分析，揭示齿轮箱的工作状态。采用核独立分量分析（KICA）算法对齿轮箱振动混合观测信号进行盲源分离，以发现与齿轮箱故障相关的特征振动源。然后采用小波包变换和经验模态分解的非线性分析方法对非平稳振动信号进行处理，提取原始故障特征向量。此外，局部线性嵌入（LLE）算法被执行作为非线性特征约简技术，以获得不同的特征从特征向量。最后，将模糊k-近邻法（FKNN）应用于齿轮箱故障模式识别。进行了两个案例研究，以评估所提出的诊断方法的有效性。一是用于齿轮故障诊断，二是用于诊断变速箱滚动轴承故障。分别从齿轮和滚动轴承故障试验台上采集了非平稳振动数据。实验测试结果表明，KICA处理后能提取出敏感的故障特征，该诊断系统对齿轮和滚动轴承的多故障诊断是有效的。此外，所提出的方法可以实现更高的性能比没有KICA处理的分类率。(C)2012爱思唯尔有限公司版权所有。
Fault diagnosis of gearboxes, especially the gears and bearings, is of great importance to the long-term safe operation. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the health condition of the gearbox in a timely manner to eliminate the impending faults. However, useful fault detection information is often submerged in heavy background noise. Thereby, a new fault detection method for gearboxes using the blind source separation (BSS) and nonlinear feature extraction techniques is presented in this paper. The nonstationary vibration signals were analyzed to reveal the operation state of the gearbox. The kernel independent component analysis (KICA) algorithm was used hereby as the BSS approach for the mixed observation signals of the gearbox vibration to discover the characteristic vibration source associated with the gearbox faults. Then the wavelet packet transform (WPT) and empirical mode decomposition (EMD) nonlinear analysis methods were employed to deal with the nonstationary vibrations to extract the original fault feature vector. Moreover, the locally linear embedding (LLE) algorithm was performed as the nonlinear feature reduction technique to attain distinct features from the feature vector. Lastly, the fuzzy k-nearest neighbor (FKNN) was applied to the fault pattern identification of the gearbox. Two case studies were carried out to evaluate the effectiveness of the proposed diagnostic approach. One is for the gear fault diagnosis, and the other is to diagnose the rolling bearing faults of the gearbox. The nonstationary vibration data was acquired from the gear and rolling bearing fault test-beds, respectively. The experimental test results show that sensitive fault features can be extracted after the KICA processing, and the proposed diagnostic system is effective for the multi-fault diagnosis of the gears and rolling bearings. In addition, the proposed method can achieve higher performance than that without KICA processing with respect to the classification rate. (C) 2012 Elsevier Ltd. All rights reserved.