| 基于小波包无量纲免疫检测器的故障诊断模型 |
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| 引用本文： | 覃爱淞,张清华,孙国玺,胡勤,邵龙秋. 基于小波包无量纲免疫检测器的故障诊断模型[J]. 上海应用技术学院学报：自然科学版, 2015, 15(2): 114-117 |
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| 作者姓名： | 覃爱淞 张清华 孙国玺 胡勤 邵龙秋 |
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| 作者单位： | 广东石油化工学院广东省石化装备故障诊断重点实验室,广东茂名,525000 |
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| 基金项目： | 国家自然科学基金资助项目，广东省战略性新兴产业核心技术攻关资助项目 |
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| 摘 要： | 为可靠地检出并识别旋转机械设备轴承故障,提出了一种基于小波包分解和无量纲免疫检测器的轴承故障模式识别方法.该方法采用小波包对原始时域信号进行预处理,分别提取原始时域信号和各频带范围内时域信号的无量纲指标,并计算其敏感因子,根据敏感因子选取敏感特征,结合人工免疫阴性选择算法,生成多个敏感特征无量纲免疫检测器,实现对故障进行识别和分类.仿真实验结果表明,所提方法能有效地识别各种轴承故障.
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| 关 键 词： | 小波包 免疫检测器 无量纲指标 故障诊断 |
Model of Wavelet Packet and Dimensionless Immune Detector for Fault Diagnosis |
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| QIN Aisong,ZHANG Qinhu,SUN Guoxi,HU Qin and SHAO Longqiu. Model of Wavelet Packet and Dimensionless Immune Detector for Fault Diagnosis[J]. Journal of Shanghai Institute of Technology: Natural Science, 2015, 15(2): 114-117 |
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| Authors: | QIN Aisong ZHANG Qinhu SUN Guoxi HU Qin SHAO Longqiu |
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| Affiliation: | Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis,Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China;Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis,Guangdong University of Petrochemical Technology, Maoming 525001, Guangdong, China;Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis,Guangdong University of Petrochemical Technology, Maoming 525002, Guangdong, China;Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis,Guangdong University of Petrochemical Technology, Maoming 525003, Guangdong, China;Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis,Guangdong University of Petrochemical Technology, Maoming 525004, Guangdong, China |
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| Abstract: | In order to check and identify bearing fault of rotating machines reliably, a pattern recognition method for bearing fault diagnosis based on wavelet packet decomposition and dimensionless immune detector was presented. Wavelet packet decomposition was presented as a pre-processing tool for the original time domain signal, then the dimensionless indicators of the original time domain signal and the various bands signals were extracted separately, and the sensitivity factor of each indicator was calculated, and the sensitive feature was determined by their value. Combined with artificial immune systems negative selection algorithm, several dimensionless immune detectors of sensitive features were constructed to identify and classify bearing fault. The simulation experiment results showed that the methods were effective to identify various kinds of bearing fault. |
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| Keywords: | wavelet packet immune detector dimensionless indicators fault diagnosis |
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