Fault Detection Based on Improved Wavelet Kernel Principal Component Analysis
[1]ZHANG Duan-jin,WANG Ai-juan.Fault Detection Based on Improved Wavelet Kernel Principal Component Analysis[J].Journal of Zhengzhou University (Engineering Science),2015,36(01):97-100.[doi:10.3969/j.issn.1671-6833.2015.01.023]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
36
Number of periods:
2015 01
Page number:
97-100
Column:
Public date:
2015-01-10
- Title:
- Fault Detection Based on Improved Wavelet Kernel Principal Component Analysis
- Keywords:
- kernel principal component analysis ; wavelet kernel function; wavelet denoising; fault detection
- CLC:
- TP273
- Abstract:
- The problem of fault detection for a class of nonlinear systems based on kernel principal componentanalysis is studied. The improved wavelet kernel principal component analysis is proposed. Firstly,the pro-posed method is applied to denose the data. Then,the preprocessed data is transformed by wavelet kernelfunction to map the nonlinear input space into linear characterization space. In the feature space,principalcomponent analysis is applied to detect faults for nonlinear system,in combination with SPE statistic and T’statistic. Simulation results show that the method can improve the fault detection performance.
- References:
- -
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