An algorithm for sensor fault diagnosis with EEMD-SVM. (April 2018)

• Record Type: Journal Article
• Authors: Ji, Junjie; Qu, Jianfeng; Chai, Yi; Zhou, Yuming; Tang, Qiu; Ren, Hao
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 40:Number 6(2018)
• Page Start: 1746
• Page End: 1756
• Publication Date: 2018-04
• Subjects: Ensemble empirical mode decomposition (EEMD) -- fault diagnosis -- feature vectors -- sensor -- support vector machine (SVM); Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331217690579
• Languages: English
• ISSNs: 0142-3312

Abstract

Based on ensemble empirical mode decomposition (EEMD) and the support vector machine (SVM), an algorithm used in the sensor fault detection and classification is put forward in this paper. Using this method and through EEMD, the sensor signal is decomposed into several segments, including the original signals, several intrinsic mode functions (IMFs) and the residual signals. Moreover, as features of the sensor fault, their variance, mean, entropy and the slope of the original signal are calculated in accordance with the characteristics of different fault types and the inherent physical meanings of each IMF. Subsequently, the feature vectors are inputted into the SVM, which is used to classify the detection and identification of sensor faults. Finally, the simulation results of the fault diagnosis of a carbon dioxide sensor indicate that this method may not only be effectively applied to fault diagnosis of carbon dioxide sensors but also provides a reference for that of other sensors.