A novel KICA–PCA fault detection model for condition process of hydroelectric generating unit

Fault detection and diagnosis of hydroelectric generating unit (HGU) have significant importance to the security of hydropower plant and the power system. In recent years, many fault detection methods based on spectral characteristic of vibration signals have been published. However, some faults cannot be effectively recognized just with spectral features for condition process monitoring of HGU. Thus, this study presents a novel fault detection model based on kernel independent component analysis and principal compo- nent analysis (KICA-PCA) monitoring model for condition process of HGU. Each of the condition processes is equivalent to a multivariate statistical process monitoring (MSPM). KICA-PCA model of the specific MSPM is trained by normal condition process data at first. Then, confidence limits of two monitoring indices (Hotelling's T 2 statistic and SPE statistic)

[1]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[2]  Ying-wei Zhang,et al.  Complex process monitoring using modified partial least squares method of independent component regression , 2009 .

[3]  Qing Li,et al.  Digitized Hydroelectric Generating Unit Under the Framework of ICMMS and Its Application , 2009, 2009 Asia-Pacific Power and Energy Engineering Conference.

[4]  Yingwei Zhang,et al.  Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM , 2009 .

[5]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[6]  Andrew Ball,et al.  Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis , 2010 .

[7]  Kai Lei,et al.  Multivariate statistical analysis of heavy metals in street dust of Baoji, NW China. , 2010, Journal of hazardous materials.

[8]  In-Beum Lee,et al.  Fault Detection of Non-Linear Processes Using Kernel Independent Component Analysis , 2008 .

[9]  Yu-Liang Chung,et al.  A looseness identification approach for rotating machinery based on post-processing of ensemble empirical mode decomposition and autoregressive modeling , 2012 .

[10]  Jicong Fan,et al.  Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA , 2014 .

[11]  Bo-Suk Yang,et al.  Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors , 2007, Expert Syst. Appl..

[12]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[13]  Xu Yangwen.  Study on Fault Diagnosis of Rotating Machinery Based on Wavelet Neural Network , 2009, 2009 International Conference on Information Technology and Computer Science.

[14]  S. Shamsuddin,et al.  An optimization technique based on imperialist competition algorithm to measurement of error for solving initial and boundary value problems , 2014 .

[15]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[16]  Xiaoyuan Zhang,et al.  Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines , 2013 .

[17]  Liu Xuemei,et al.  Measurement of soil properties using visible and short wave-near infrared spectroscopy and multivariate calibration , 2013 .

[18]  Li Li,et al.  Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis , 2013 .

[19]  Li Chao-shun.  Vibrant fault diagnosis for hydroelectric generator unit considering overlapping fault patterns , 2012 .

[20]  Sheng Chen,et al.  A process monitoring method based on noisy independent component analysis , 2014, Neurocomputing.

[21]  L. Liu,et al.  Vibration Fault Diagnosis for Hydraulic Generator Units with Pattern Recognition and Cluster Analysis , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[22]  Shuai Li,et al.  Dynamic processes monitoring using recursive kernel principal component analysis , 2012 .

[23]  Jian Xiao,et al.  Hydraulic turbine governing system identification using T-S fuzzy model optimized by chaotic gravitational search algorithm , 2013, Eng. Appl. Artif. Intell..

[24]  In-Beum Lee,et al.  Fault detection and diagnosis based on modified independent component analysis , 2006 .

[25]  Gangshi Hu,et al.  An independent component analysis and mutual information based non-Gaussian pattern matching method for fault detection and diagnosis of complex cryogenic air separation process , 2013, 2013 American Control Conference.

[26]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

[27]  Shaojiang Dong,et al.  Bearing degradation process prediction based on the PCA and optimized LS-SVM model , 2013 .

[28]  Zhaohui Li,et al.  Automated Vibration Analysis in Loaded Condition of Hydro Turbine Generator Sets , 2012, 2012 Asia-Pacific Power and Energy Engineering Conference.

[29]  Bo-Suk Yang,et al.  Intelligent fault diagnosis system of induction motor based on transient current signal , 2009 .

[30]  Manabu Kano,et al.  Monitoring independent components for fault detection , 2003 .

[31]  Yuh-Jye Lee,et al.  Anomaly detection via over-sampling principal component analysis , 2009 .

[32]  Zhiqiang Ge,et al.  Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches , 2010 .

[33]  Philippe Garat,et al.  Blind separation of mixture of independent sources through a quasi-maximum likelihood approach , 1997, IEEE Trans. Signal Process..

[34]  Xuemin Tian,et al.  A new fault detection method for non-Gaussian process based on robust independent component analysis , 2014 .