| 基于信号奇异性分析的轴承故障检测方法 |
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| 引用本文： | 徐晶,陶新民,张鸿艳. 基于信号奇异性分析的轴承故障检测方法[J]. 机械传动, 2010, 34(1) |
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| 作者姓名： | 徐晶 陶新民 张鸿艳 |
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| 作者单位： | 1. 黑龙江科技学院,数力系,黑龙江,哈尔滨,150027
2. 哈尔滨工程大学,信息与通信工程学院,黑龙江,哈尔滨,150001 |
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| 基金项目： | 黑龙江省教育厅科学技术研究项目(11544049) |
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| 摘 要： | 针对轴承振动信号利用小波单奇异点检测无法克服噪声影响的不足,提出利用小波模极大值分析信号奇异性变化进而进行轴承故障检测的方法。实验中对信号的模极大分形指数、模极大分形指数熵、Lipschitz指数以及Lipschitz指数熵等奇异特征进行分析比较,实验结果表明这些特征都能有效克服噪声影响实现故障检测,但模极大曲线数最能体现故障特征且检测效果最好。最后将本方法同基于小波包能量谱特征和小波单奇异点检测的方法进行比较,实验结果表明本文中建议的方法在检测时间及检测率上都有显著提高。
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| 关 键 词： | 故障检测 小波变换 小波模极大值 熵 |
Bearings Fault Detection based on Signal Wavelet Singular Analysis |
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| Xu Jing,Tao Xinmin,Zhang Hongyan. Bearings Fault Detection based on Signal Wavelet Singular Analysis[J]. Journal of Mechanical Transmission, 2010, 34(1) |
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| Authors: | Xu Jing Tao Xinmin Zhang Hongyan |
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| Affiliation: | 1 Department of Mathematics and Mechanics/a>;Heilongjiang Institute of Science and Technology/a>;Harbin 150027/a>;China;2 College of Information and Communication Engineering/a>;Harbin Engineering University/a>;Harbin 150001/a>;China |
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| Abstract: | To solve the problem that the wavelet-singular point detection is more sensitive to the noise,the scheme of bearings fault detection based on the number of singularity point using wavelet transform modulus maximum with the constant length is proposed.Wavelet transform modulus maximum fractal spectrum,Wavelet transform modulus maximum fractal spectrum entropy,Wavelet transform modulus maximum Lipschitz spectrum and Wavelet transform modulus maximum Lipschitz spectrum entropy are analyzed and compared,results... |
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| Keywords: | Fault Detection Wavelet transform Wavelet transform modulus maximum Entropy |
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