Principal components selection for dimensionality reduction using discriminant information applied to fault diagnosis |
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| Affiliation: | 1. Automatic and Computing Department, CUJAE, Havana, Cuba;2. Systems and Automatic Engineering Department, UPV, Valencia, Spain;1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2. School of Information Science and Engineering, University of Jinan, Jinan 250013, China;3. College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;4. College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China;5. Collaborative Innovation Center of IoT Technology and Intelligent Systems, Minjiang University, Fuzhou 350108, China;1. College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China;2. Research Center of TCM-information Engineering, State Administration of Traditional Chinese Medicine of the People׳s Republic of China, Beijing 100102, China |
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| Abstract: | The Principal Component Analysis is one of most applied dimensionality reduction techniques for process monitoring and fault diagnosis in industrial process. This work proposes a procedure based on the discriminant information contained in the principal components to determine the most significant ones in fault separability. The Tennessee Eastman Process industrial benchmark is used to illustrate the effectiveness of the proposal. The use of statistical hypothesis tests as a separability measure between multiple failures is proposed for the selection of the principal components. The classifier profile concept has been introduced for comparison purposes. Results show an improvement in the classification process when compared with traditional techniques and the StepWise selection. This has resulted in a better classification for a fixed number of components, or a smaller number of required components to obtain a prefixed error rate. In addition, the computational advantage is demonstrated. |
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| Keywords: | Fault diagnosis Feature extraction PCA Dimensionality reduction Discriminant analysis |
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