摘要
本文提出了一种基于支持向量数据描述(Support Vector Data Description,SVDD)算法的变频空调系统制冷剂泄漏故障检测和诊断方法。首先利用主成分分析算法将数据进行降维处理,并在 3,000 r/min和 5,000 r/min 转速下分别构建 SVDD 模型。诊断结果表明,SVDD 模型依赖于训练数据量的大小,训练数据越丰富,模型准确率越高。转速为 5,000 r/min 模型训练数据为 1,800 组,约为3,000 r/min 测试模型训练数据量的 9 倍,15%制冷剂泄漏数据的准确率由 61.29%提高为 73.16%。但数据丰富后,模型求解时间长,难以收敛。最后通过先网格搜索、再使用遗传算法优化的方法改进 SVDD 模型的求解过程。模型优化后,5,000 r/min 转速下无故障数据诊断准确率由 75.06%提高为 93.43%,模型对其他故障水平的数据诊断准确率可达 100%,准确率得到大幅度提升。
A fault diagnosis method based on support vector data description (SVDD) for the refrigerant leakage fault detection and diagnosis of the inverter air-conditioning system is proposed. The principal component analysis method is used to reduce the dimensions of the data from the air-conditioning system, and then the SVDD model is built with 3,000 r/min and 5,000 r/min speed respectively. With the model, the results show that the performance of the model depends on the amount of training data. The larger the amount of data are, the more accurate the model is. The training data of 5,000 r/min model are 1,800 sets, which is about 9 times than those of the training data of 3,000 r/min model. The accuracy of 15% refrigerant leakage data increases from 61.29% to 73.16%. However, it takes a long time to solve the model with a large amount of data, so that the solution is difficult to converge. Finally, the SVDD model is improved to solve the model by grid search and then genetic algorithm. After the optimization, the diagnostic accuracy of fault-free data increases from 75.06 % to 93.43%, and the accuracy of other fault data is 100%, so the accuracy has been greatly improved.
作者
徐廷喜
杜志敏
吴斌
黄小清
晋欣桥
XU Tingxi;DU Zhimin;WU Bin;HUANG Xiaoqing;JIN Xinqiao(School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
出处
《制冷技术》
2019年第4期25-31,共7页
Chinese Journal of Refrigeration Technology
关键词
机器学习
变频空调系统
故障诊断
制冷剂泄漏
Machine learning
Inverter air conditioner, Fault diagnosis
Refrigerant leakage
