红外与毫米波学报, 2019, 38 (5): 627, 网络出版: 2019-11-19
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
Static and dynamic rubbing positions identification of Cryocooler based on wavelet packet analysis and support vector machine
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 vibration signal wavelet packet support vector machine genetic algorithm rubbing fault Patternrecognition
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.
Abstract
Cryocoolerplaysanextremelyimportantrolein thefield ofinfraredremote sensing. Thenor-maloperationandperformanceofthedetectorwillbeaffectedifthecryocoolerbreaksdown. Anewin-telligent 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 sig-nal,and the vibration signal is extracted in time domain. The evaluation factors of the combined fea-ture set are calculatedby using the distance evaluation technique,and the corresponding sensitive fea-tures are selected. Then,the parameters of SVM are optimized by genetic algorithm. Finally,the se-lected sensitive features are inputinto the optimizedSVM to identifydifferent 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 accu-rately,andtheaccuracyis95%.
高升, 吴亦农, 蒋珍华. 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[J]. 红外与毫米波学报, 2019, 38(5): 627. GAO Sheng, WU Yi-Nong, JIANG Zhen-Hua. Static and dynamic rubbing positions identification of Cryocooler based on wavelet packet analysis and support vector machine[J]. Journal of Infrared and Millimeter Waves, 2019, 38(5): 627.
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