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化工学报  2015 

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, PP. 1815-1820

Keywords: 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

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Abstract:

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。

References

[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]  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
[10]  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
[11]  Benkedjouh T, Medjaher K, Zerhouni N, et al. Fault prognostic of bearings by using support vector data description [Z]. IEEE, 2012:1-7
[12]  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
[13]  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
[14]  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
[15]  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
[16]  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
[17]  Jackson J E. A User's Guide To Principal Components Analysis[M]. New York: John Wiley & Sons., 1991: 1058-1067
[18]  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
[19]  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
[20]  Tax D M J. Support vector domain description [J]. Pattern Recognition Letters, 1999, 20 (11): 1191-1199
[21]  Tax D M, Duin R P. Support vector data description [J]. Machine Learning, 2004, 54 (1): 45-66
[22]  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
[23]  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

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