基于遗传算法和支持矢量机参数优化的制冷机组故障检测与诊断研究  

Chiller Fault Detection and Diagnosis Based on Genetic Algorithm and Support Vector Machine with Parameter Tuning

作　　者：韩华[1] 谷波[1] 康嘉[1] 

机构地区：[1]上海交通大学制冷与低温工程研究所,上海200240

出　　处：《机械工程学报》2011年第16期120-126,共7页Journal of Mechanical Engineering

基　　金：国家自然科学基金资助项目(50876059)

摘　　要：针对制冷机组故障诊断中特征多、诊断准确率低的特点,提出一种复合诊断模型,利用遗传算法搜索特征空间,与带参数优化的支持矢量机(Support vector machine,SVM)结合,同时进行故障特征提取和模型训练。用该模型研究7种典型的制冷机组故障,从64个原始特征中筛选出8个与试验辅助系统关系甚微、均十分靠近核心制冷循环的特征,作为故障指示特征,总体诊断准确率从96.95%提高到99.53%,测试时间下降70%以上。用命中率和虚警率评价模型对各故障的诊断性能,所提复合模型除个别故障外,均优于无特征提取及带主元分析特征提取的SVM模型。复合模型在制冷机组故障诊断中有良好的应用前景。In view of too many features and low accuracy characteristics in chiller fault detection and diagnosis（FDD）,a hybrid FDD model based on genetic algorithm（GA） and SVM with parameter tuning is presented.The GA-SVM wrapper can carry out fault feature extraction and model training simultaneously.Studying on seven typical chiller faults shows that eight indicative features,centered around the core refrigeration cycle,can be selected from the original 64 features,with a rise in FDD accuracy from 96.95% to 99.53% and a sharp drop in FDD time（over 70%）.Using hit rate（HR） and false alarm rate（FAR） to evaluate the model performance on individual fault,the presented hybrid model behaves much better than the SVM model without feature extraction and the PCA-SVM model.It has a promising future in chiller FDD applications.

关 键 词：故障诊断 支持矢量机 遗传算法 制冷 故障特征提取 

分 类 号：TB65[一般工业技术—制冷工程]