基于小波包变换和支持向量机的制冷机动静碰摩故障部位识别研究
投稿时间:2019-01-29  修订日期:2019-03-19  点此下载全文
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作者单位E-mail
高升 中国科学院上海技术物理研究所 gaosheng@mail.ustc.edu.cn 
吴亦农 中国科学院上海技术物理研究所 wyn@mail.sitp.ac.cn 
蒋珍华 中国科学院上海技术物理研究所 jiangzhenhua@mail.sitp.ac.cn 
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
中文摘要:制冷机在红外遥感领域发挥着极其重要的作用,如果出现故障直接影响探测器的正常工作以及性能,因此,制冷机智能故障诊断具有重要的意义。针对制冷机出现的碰摩故障,提出了一种基于小波包变换、遗传算法、支持向量机的智能故障诊断方法。首先对振动信号做小波变换及时域特征提取组成特征集。利用距离评价技术从特征集中选择敏感特征。利用遗传算法优化支持向量机参数。将特征值输入到优化好的支持向量机中,自动识别机器运行状态。开展制冷机故障模拟实验,结果表明,该方法最终识别准确率达95%,能有效识别制冷机碰摩故障部位。
中文关键词:振动信号;小波包;支持向量机;  距离评估技术;遗传算法;碰摩故障;模式识别
 
Static and dynamic rubbing positions identification of ryocooler based on wavelet packet analysis and support vector machine
Abstract:Cryocooler plays an extremely important role in the field of infrared remote sensing. The normal operation and performance of the detector will be affected if the cryocooler breaks down. A new intelligent fault diagnosis method for cryocooler has been proposed based on wavelet packet transform, genetic algorithm and SVM for rubbing fault. First, wavelet transform is applied to the vibration signal, and the vibration signal is extracted in time domain. The evaluation factors of the combined feature set are calculated by using the distance evaluation technique, and the corresponding sensitive features are selected. Then, the parameters of SVM are optimized by genetic algorithm. Finally, the selected sensitive features are input into the optimized SVM to identify different machine operation states automatically. The effectiveness of the method is verified by the fault simulation test of the cryocooler. Experimental results show that this method can identify and locate the cryocooler rubbing fault accurately, and the accuracy is 95%.
keywords:vibration signal  wavelet packet  support vector machine  genetic algorithm  rubbing fault  Pattern recognition
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