| Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis |
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| 作者姓名： | WANG Sheng-chun HAN Jie LI Zhi-nong LI Jian-feng |
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| 作者单位： | [1]School of Mechanical Engineering, Shandong University, Jinan 250061, P. R. China [2]Research Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450002, P. R. China |
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| 基金项目： | 国家自然科学基金
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河南省高校杰出科研创新人才工程项目 |
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| 摘 要： | The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.
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| 关 键 词： | 适配时间频率分布 时间变化回归 机械疲劳 诊断方法 |
| 修稿时间： | 2006-07-27 |
Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis |
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| WANG Sheng-chun,HAN Jie,LI Zhi-nong,LI Jian-feng.Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis[J].International Journal of Plant Engineering and Management,2007,12(2):116-120. |
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| Authors: | WANG Sheng-chun HAN Jie LI Zhi-nong LI Jian-feng |
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| Abstract: | The time-varying autoregressive (TVAR) modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing, and the experiment result shows that the proposed method is effective in feature extraction. |
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| Keywords: | time-varying autoregressive modeling parameter estimation time-frequency distribution fault diagnosis |
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