Research on sensor fault identification based on improved 1-v-r SVM classification method
by Chun-ying Jiang; Li-cai Li; Chang-long Ye; Su-yang Yu
International Journal of Advanced Media and Communication (IJAMC), Vol. 6, No. 2/3/4, 2016

Abstract: The feature identification method based on artificial intelligence can significantly improve accuracy and effectiveness of sensor fault diagnosis. An improved support vector machine (SVM)-K-nearest neighbour (KNN) classification method that combines one-verse-rest (1-v-r) SVM and KNN was brought for sensor fault recognition. The method firstly constructs 1-v-r SVM training set by primary selection on training samples, and then classifies it using 1-v-r method. It re-classifies indivisible samples with KNN algorithm. Fault diagnosis experiment on photoelectric encoder sensor verifies that it can determine current fault belongs to which type of common sensor faults. The experiment also compared SVM-KNN with one-verse-one (1-v-1) SVM and bintree SVM. Results show that it has better classification accuracy and classification speed.

Online publication date: Tue, 13-Dec-2016

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