基于SVDD的冷水机组传感器故障检测及效率分析

doi:10.11949/j.issn.0438-1157.20141585

化工学报 ›› 2015, Vol. 66 ›› Issue (5): 1815-1820.DOI: 10.11949/j.issn.0438-1157.20141585

基于SVDD的冷水机组传感器故障检测及效率分析

李冠男¹, 胡云鹏¹, 陈焕新¹, 黎浩荣², 李炅³, 胡文举⁴

1 华中科技大学能源与动力工程学院, 湖北武汉 430074;
2 University of Nebraska-Lincoln, PKI-101 F, 1110 S, 67th street, Lincoln, NE USA 68182;
3 合肥通用机械研究院压缩机技术国家重点实验室, 安徽合肥 230031;
4 北京建筑大学供热供燃气通风及空调工程北京市重点实验室, 北京 100044

收稿日期:2014-10-22 修回日期:2015-01-30 出版日期:2015-05-05 发布日期:2015-05-05
通讯作者: 陈焕新
基金资助:
国家自然科学基金项目(51328602);2013年压缩机技术国家重点实验室开放基金项目;供热供燃气通风及空调工程北京市重点实验室研究基金资助课题(NR2013K02)。

SVDD-based chiller sensor fault detection method and its detection efficiency

LI Guannan¹, HU Yunpeng¹, CHEN Huanxin¹, LI Haorong², LI Jiong³, HU Wenju⁴

1 School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;
2 University of Nebraska-Lincoln, Lincoln 68182, NE, USA;
3 State Key Laboratory of Compressor Technology, Hefei General Machinery Research Institute, Hefei 230031, Anhui, China;
4 Beijing Municipality Key Laboratory of HVAC&R, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Received:2014-10-22 Revised:2015-01-30 Online:2015-05-05 Published:2015-05-05
Supported by:
supported by the National Natural Science Foundation of China (51328602), 2013 State Key Laboratory of Compressor Technology and 2013 Beijing Municipality Key Laboratory of HVAC&R (NR2013K02).

摘要/Abstract

摘要： 传感器是制冷空调系统的重要组成部分,起着测量数据和监控状态的作用。传感器故障,尤其是输出偏差会引起测量值不准,影响控制策略,导致系统能耗增加。依据模式识别理论,故障检测可处理为一种单分类问题。据此采用一种单分类模式识别工具——支持向量数据描述(SVDD),针对冷水机组进行了偏差故障条件下的传感器故障检测工作。收集冷水机组实测正常运行数据,基于训练集建立SVDD模型,进行冷水机组传感器故障检测;在测试集中引入不同幅值水平的偏差故障,分析检测效率。结果表明:基于SVDD的冷水机组传感器故障检测效果明显,但对于不同传感器的不同幅值偏差故障,故障识别程度并不一致。

关键词: 冷水机组, 过程控制, 故障检测, 支持向量数据描述, 算法, 模型简化

Abstract: In the refrigeration and air conditioning system, sensors are independent component for physical data measuring and operating state monitoring. Sensor faults, especially sensor biases output will lead to incorrect measurement, inappropriate controlling strategy and further energy consumption rise. Based on the pattern recognition theory, the fault detection task could be considered as a one-class classification problem. Therefore, a powerful pattern recognition-based one-class classification algorithm, Support Vector Data Description (SVDD) was used to detect the sensor biases occurring in a chiller system. The practical fault-free data were used as training dataset to develop the SVDD model so as to detect the sensor faults. The method and its fault detection efficiency were validated by test dataset with different artificially introduced levels of sensor biases. The SVDD-based fault detection method worked well with chiller practical operating measurements, but the fault detection efficiencies of different sensors with different level faults were inconsistent.

Key words: chiller, process control, fault detection, support vector data description, algorithm, model reduction

中图分类号:

TB65

李冠男, 胡云鹏, 陈焕新, 黎浩荣, 李炅, 胡文举. 基于SVDD的冷水机组传感器故障检测及效率分析[J]. 化工学报, 2015, 66(5): 1815-1820.

LI Guannan, HU Yunpeng, CHEN Huanxin, LI Haorong, LI Jiong, HU Wenju. SVDD-based chiller sensor fault detection method and its detection efficiency[J]. CIESC Journal, 2015, 66(5): 1815-1820.

参考文献 23

[1]	Katipamula S, Brambley M R. Review article: methods for fault detection, diagnostics, and prognostics for building systems—a review (Ⅰ) [J]. HVAC&R Research, 2005, 11 (1): 3-25
[2]	Ms L H C, Phd E L R, Phd R D B. Fault Detection and Diagnosis in Industrial Systems [M]. London: Springer, 2001
[3]	Wang S, Chen Y. Sensor validation and reconstruction for building central chilling systems based on principal component analysis [J]. Energy Conversion and Management, 2004, 45 (5): 673-695
[4]	Wang S, Cui J. Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method [J]. Applied Energy, 2005, 82 (3): 197-213
[5]	Du Z M, Jin X Q, Wu L Z. PCA-FDA-based fault diagnosis for sensors in VAV systems [J]. HVAC&R Research, 2007, 13 (2): 349-367
[6]	Du Z M, Jin X Q, Wu L Z. Fault detection and diagnosis based on improved PCA with JAA method in VAV systems [J]. Building and Environment, 2007, 42 (9): 3221-3232
[7]	Xu X, Xiao F, Wang S. Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods [J]. Applied Thermal Engineering, 2008, 28 (2): 226-237
[8]	Hu Yunpeng (胡云鹏), Chen Huanxin (陈焕新), Zhou Cheng (周诚), et al. Chiller sensor fault detection using wavelet de-noising [J]. J. Huazhong Univ. of Sci. & Tech.: Natural Science Edition (华中科技大学学报: 自然科学版), 2013 (3): 16-19
[9]	Dunia R, Joe Qin S. A unified geometric approach to process and sensor fault identification and reconstruction: the unidimensional fault case [J]. Computers & Chemical Engineering, 1998, 22 (7): 927-943
[10]	Jackson J E. A User's Guide To Principal Components Analysis[M]. New York: John Wiley & Sons., 1991: 1058-1067
[11]	Han H, Gu B, Wang T, et al. Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning [J]. International Journal of Refrigeration, 2011, 34 (2): 586-599
[12]	Han H, Gu B, Kang J, et al. Study on a hybrid SVM model for chiller FDD applications [J]. Applied Thermal Engineering, 2011, 31 (4): 582-592
[13]	Tax D M J. Support vector domain description [J]. Pattern Recognition Letters, 1999, 20 (11): 1191-1199
[14]	Tax D M, Duin R P. Support vector data description [J]. Machine Learning, 2004, 54 (1): 45-66
[15]	Liu X, Li K, Mcafee M, et al. Improved nonlinear PCA for process monitoring using support vector data description [J]. Journal of Process Control, 2011, 21 (9): 1306-1317
[16]	Ge Z, Gao F, Song Z. Batch process monitoring based on support vector data description method [J]. Journal of Process Control, 2011, 21 (6): 949-959
[17]	Liu Y, Ting Y, Shyu S, et al. A support vector data description committee for face detection [J]. Mathematical Problems in Engineering, 2014, 2014: 1-9
[18]	Cho H, Jeong M K, Kwon Y. Support vector data description for calibration monitoring of remotely located microrobotic system [J]. Journal of Manufacturing Systems, 2006, 25 (3): 196-208
[19]	Benkedjouh T, Medjaher K, Zerhouni N, et al. Fault prognostic of bearings by using support vector data description [Z]. IEEE, 2012:1-7
[20]	Tax D M, Ypma A, Duin R P. Pump failure detection using support vector data descriptions//Advances in Intelligent Data Analysis [M]. Springer, 1999: 415-425
[21]	Muller K R, Mika S, Ratsch G, et al. An introduction to kernel-based learning algorithms [J]. IEEE Trans. Neural Netw., 2001, 12 (2): 181-201
[22]	Hu Yunpeng (胡云鹏), Chen Huanxin (陈焕新), Zhou Cheng (周诚), et al. Analysis of sensor fault detection in chiller based on PCA method [J]. CIESC Journal (化工学报), 2012, 63 (S2): 85-88
[23]	Hu Y, Chen H, Xie J, et al. Chiller sensor fault detection using a self-adaptive principal component analysis method [J]. Energy and Buildings, 2012, 54: 252-258

基于SVDD的冷水机组传感器故障检测及效率分析

SVDD-based chiller sensor fault detection method and its detection efficiency

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 23

相关文章 15

编辑推荐

Metrics

本文评价

[1]	康飞, 吕伟光, 巨锋, 孙峙. 废锂离子电池放电路径与评价研究[J]. 化工学报, 2023, 74(9): 3903-3911.
[2]	王浩, 王振雷. 基于自适应谱方法的裂解炉烧焦模型化简策略[J]. 化工学报, 2023, 74(9): 3855-3864.
[3]	曹跃, 余冲, 李智, 杨明磊. 工业数据驱动的加氢裂化装置多工况切换过渡状态检测[J]. 化工学报, 2023, 74(9): 3841-3854.
[4]	张逸豪, 王振雷. 基于最大信息系数的分组支持向量数据描述故障检测[J]. 化工学报, 2023, 74(9): 3865-3878.
[5]	尹刚, 李伊惠, 何飞, 曹文琦, 王民, 颜非亚, 向禹, 卢剑, 罗斌, 卢润廷. 基于KPCA和SVM的铝电解槽漏槽事故预警方法[J]. 化工学报, 2023, 74(8): 3419-3428.
[6]	诸程瑛, 王振雷. 基于改进深度强化学习的乙烯裂解炉操作优化[J]. 化工学报, 2023, 74(8): 3429-3437.
[7]	周继鹏, 何文军, 李涛. 异形催化剂上乙烯催化氧化失活动力学反应工程计算[J]. 化工学报, 2023, 74(6): 2416-2426.
[8]	邵远哲, 赵忠盖, 刘飞. 基于共同趋势模型的非平稳过程质量相关故障检测方法[J]. 化工学报, 2023, 74(6): 2522-2537.
[9]	高学金, 姚玉卓, 韩华云, 齐咏生. 基于注意力动态卷积自编码器的发酵过程故障监测[J]. 化工学报, 2023, 74(6): 2503-2521.
[10]	苏晓丹, 朱干宇, 李会泉, 郑光明, 孟子衡, 李防, 杨云瑞, 习本军, 崔玉. 湿法磷酸半水工艺考察与石膏结晶过程研究[J]. 化工学报, 2023, 74(4): 1805-1817.
[11]	宋冰, 郑城风, 侍洪波, 陶阳, 谭帅. 基于VAE-OCCA的质量相关故障检测方法研究[J]. 化工学报, 2023, 74(4): 1630-1638.
[12]	郑书闽, 郭鹏程, 颜建国, 王帅, 李文博, 周淇. 微小通道内过冷流动沸腾阻力特性实验及预测研究[J]. 化工学报, 2023, 74(4): 1549-1560.
[13]	张中秋, 李宏光, 石逸林. 基于人工预测调控策略的复杂化工过程多任务学习方法[J]. 化工学报, 2023, 74(3): 1195-1204.
[14]	张江淮, 赵众. 碳三加氢装置鲁棒最小协方差约束控制及应用[J]. 化工学报, 2023, 74(3): 1216-1227.
[15]	顾学荣, 刘硕士, 杨思宇. 基于并行EGO和代理模型辅助的多参数优化方法研究[J]. 化工学报, 2023, 74(3): 1205-1215.