The Hybrid Principal Component Analysis Based on Wavelets and Moving Median Filter

  • Authors:

  • Cheng-Lin Wen;Shao-Hui Fan;Zhi-Guo Chen

  • Affiliations:

  • School of Automatic, Hangzhou Dianzi University, Hangzhou 310018, China;School of Computer and Information Engineering, Henan University, Kaifeng 475001, China;School of Computer and Information Engineering, Henan University, Kaifeng 475001, China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
  • Year:
  • 2007

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Abstract

The data obtained from any process may be corrupted with noise and outliers which may lead to false-alarm when applying conventional PCA to process monitoring. To overcome the above mentioned limitations of conventional PCA, an approach is developed by combining the ability of wavelets and moving median filter with PCA. This method utilizes the quality of wavelets and moving median filter to preprocess the data to eliminate noise and outliers. At last, this method is applied to fault detection and has a good effect which proves the method is effective and feasible.