Bearing Fault Detection Using Higher-Order Statistics Based ARMA Model
Bearing Fault Detection Using Higher-Order Statistics Based ARMA Model
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DOI：
10.4028/www.scientific.net/kem.347.271
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Fucai Li;L. Ye;G. Zhang;G. Meng
中科院分区：
文献类型：
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作者：
Fucai Li;L. Ye;G. Zhang;G. Meng
脉冲响应提供了机械系统缺陷的重要信息。反褶积是一种用于故障检测的系统识别技术，当从有缺陷和无缺陷的轴承捕获信号同时可用时。然而，测量系统和噪声的影响是该技术的障碍。在本研究中，采用自回归移动平均（ARMA）模型来估计滚动轴承的振动模式，用于故障检测。常用的ARMA估计器不能完全表征非高斯噪声。针对基于二阶统计量的ARMA估计器效率低下的问题，在ARMA估计器中引入高阶统计量（HOS），极大地消除了噪声的影响，从而提高了对系统的估计精度。此外，利用估计的基于hos的ARMA模型的双谱，得到更清晰的信息。为了进行故障检测，对无缺陷和有缺陷轴承信号的脉冲响应及其双谱进行了比较。结果表明，该方法具有较好的振动信号处理和故障检测能力。
Impulse response provides important information about flaws in mechanical system. Deconvolution is one system identification technique for fault detection when signals captured from bearings with and without flaw are both available. However effects of measurement systems and noise are obstacles to the technique. In the present study, a model, namely autoregressive-moving average (ARMA), is used to estimate vibration pattern of rolling element bearings for fault detection. The frequently used ARMA estimator cannot characterize non-Gaussian noise completely. Aimed at circumventing the inefficiency of the second-order statistics-based ARMA estimator, higher-order statistics (HOS) was introduced to ARMA estimator, which eliminates the effect of noise greatly and, therefore, offers more accurate estimation of the system. Furthermore, bispectrums of the estimated HOS-based ARMA models were subsequently applied to get clearer information. Impulse responses of signals captured from the test bearings without and with flaws and their bispectra were compared for the purpose of fault detection. The results demonstrated the excellent capability of this method in vibration signal processing and fault detection.