| 摘要: |
| 冷水机组系统中，温度传感器出现故障会严重影响机组工作效率及使用寿命。针对冷水机组温度传感器偏差故障，本文提出一种基于单类支持向量机(one-class support vector machine, OC-SVM)的故障检测方法，采用冷水机组正常数据建立OC-SVM模型，通过十折交叉验证法获得模型优化参数。分别采用工程实测数据、实验数据(共4组)对该方法进行了验证，结果表明：基于OC-SVM的方法能有效检测出4组冷水机组的温度传感器偏差故障。其中对于螺杆式冷水机组（数据集Ⅰ)的故障检测效果明显，当冷冻水侧温度传感器偏差故障幅值绝对值大于1 ℃时，检测效率达到100%。 |
| 关键词: 冷水机组 传感器 故障检测 单类支持向量机 算法 |
| DOI： |
| 投稿时间：2018-07-05 修订日期：2019-01-02
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| 基金项目:湖北省教育厅科学研究计划青年人才项目(Q20181110)，湖北省高等学校优秀中青年科技创新团队项目(T201829)，湖北省自然科学基金项目(2016CFB472)和武汉科技大学博士科研基金项目(100234)资助。 |
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| Temperature Sensor Fault Detection in Chiller Based on One-class Support Vector Machine Algorithm |
| Mao Qianjun,Liang Zhiyuan,Liu Donghua,Hu Yunpeng,Li Guannan,Fang Xi |
| (School of Urban Construction, Wuhan University of Science and Technology;Wuhan Business University) |
| Abstract: |
| Temperature sensor faults may lead to abnormal system operations that can damage the chiller system and reduce its life span. Herein, a fault detection method based on the one-class support vector machine (OC-SVM) algorithm has been proposed. Fault-free data were used to train the OC-SVM model for detection of temperature sensor biases. The optimized model parameters were obtained by the 10-fold cross validation method. Four chiller datasets, including in-site and laboratory data, were used to validate the proposed method. Results showed that the OC-SVM showed good fault detection performance on the four chiller datasets, with the effect of fault detection being especially obvious for the screw chiller (dataset I). The detection efficiency reached 100%, when the absolute value of temperature sensor fault biases at chilled-water side was greater than 1 ℃. |
| Key words: chiller sensor fault detection one-class support vector machine algorithm |
