Fault Diagnosis for Refrigeration Systems Based on Principal Component Analysis and Support Vector Machine

Abstract

Abstract:Principal component analysis (PCA) was employed to make feature extraction from the vast data pool, and the PCA+SVM (support vector machine) model was established for the fault detection and diagnosis (FDD) of refrigeration systems. Considering that SVM can not be used to solve multiclass classification problems directly, several classical multiSVMs algorithms were analyzed and compared, and the “One vs others” algorithm was adopted. The hybrid PCASVM FDD model was presented and validated by the historical data from specially designed experiments. The results show that it can isolate normal from faulty modes (detection) and has a diagnostic rate of no less than 98.57%, which is better than the SVM model without PCA; model training is about 130—350 times faster than the latter; it also has better performance on dealing with small sample problem than BP neural network with higher diagnostic rate and much less training time (about 1/240).

Key words:refrigeration system,fault diagnosis (FD),principal component analysis (PCA),support vector machine (SVM)

CLC Number:

TB66

HAN Hua-1, GU Bo-1, REN Neng-2. Fault Diagnosis for Refrigeration Systems Based on Principal Component Analysis and Support Vector Machine[J]. Journal of Shanghai Jiaotong University, 2011, 45(09): 1355-1361.

References

［1］Qin S J, Yue H Y, Dunia R. Selfvalidating inferential sensors with application to air emission monitoring ［J］. Ind Eng Chen Res, 1997, 36(5): 16751685.

［2］邱天，丁艳军，吴占松. 基于主元分析的故障可检测性的统计指标比较［J］. 开云网页登录学报，2006, 40(8): 14471450.

QIU Tian, DING Yanjun, WU Zhansong. Sensor fault detection statistics based on principal component analysis ［J］. Journal of Shanghai Jiaotong University， 2006, 40(8): 14471450.

［3］高隽. 人工神经网络原理及仿真实例［M］. 北京: 机械工业出版社, 2003.

［4］Vapnik V N. 统计学习理论［M］. 许建华译.北京: 电子工业出版社, 2004: 293323.

［5］Choi K, Namburu S M, Azam M, et al. Fault diagnosis in HVAC chillers: Adaptability of a datadriven fault detection and isolation approach ［J］. IEEE Instrum Meas Mag, 2005, 8(3): 2432.

［6］Cherkassky V, Ma Y Q. Practical selection of SVM parameters and noise estimation for SVM regression ［J］. Neural Networks, 2004, 17(1): 113126.

［7］Lee J M, Yoo C, Choi S W, et al. Nonlinear process monitoring using kernel principle component analysis ［J］. Chemical Engineering Science, 2004, 59(1):223234.

［8］Valle S, Li W, Qin S J. Selection of the number of principal components: The variance of the reconstruction error criterion with a comparison to other methods［J］. Industrial and Engineering Chemistry Research, 1999, 38(11): 43894401.

［9］Cui J T. A robust fault detection and diagnosis strategy for centrifugal chillers ［D］. Hong Kong: Department of Building Services Engineering, Hong Kong Polytechnic University, 2005.

［10］Fletcher R. Practical methods of optimization ［M］. New York: John Wiley and Sons, 1987.

［11］Hsu C W, Lin C J. A comparison of methods for multiclass support vector machines ［J］. IEEE Transaction on Neural Networks, 2002, 13(2): 415425.

［12］Liu Y G, You Z S, Cao L P. A novel and quick SVMbased multiclass classifier ［J］. Pattern Recognition, 2006, 39(11): 22582264.

［13］Weston J, Watkins C. Support vector machines for multiclass pattern recognition ［C］// Proc ESANN’99. Brussels: Facto Press, 1999: 219224.

［14］Platt J C, Cristianini N, Shawe T J. Large margin DAGs for multiclass classification ［C］//Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2000: 547553.

［15］Moreira M, Mayoraz E. Improved pairwise coupling classification with correcting classifiers ［C］// Euro Conf Machine Learning. London: SpringerVerlag, 1998: 160171.

［16］Yuan S F, Chu F L. Support vector machinesbased fault diagnosis for turbopump rotor ［J］. Mechanical Systems and Signal Processing, 2006, 20(4): 939952.

［17］Lin H T, Lin C J. A practical guide to support vector classification ［EB/OL］.［20091030］.http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf.

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