Sensor Multifault Diagnosis With Improved Support Vector Machines

In this paper, two multifault diagnosis methods based on improved support vector machine (SVM) are proposed for sensor fault detection and identification respectively. First, online sparse least squares support vector machine (OS-LSSVM) is utilized to detect and predict sensor faults. Then, a method...

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Bibliographic Details
Published inIEEE transactions on automation science and engineering Vol. 14; no. 2; pp. 1053 - 1063
Main Authors Deng, Fang, Guo, Su, Zhou, Rui, Chen, Jie
Format Journal Article
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Encoding
Error-correcting output codes (ECOC)
Fault diagnosis
Feature extraction
Frequency-domain analysis
online sparse least squares support vector machine (OS-LSSVM)
sensor fault diagnosis
Sparse matrices
support vector machine (SVM)
Support vector machines
Training
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Summary:In this paper, two multifault diagnosis methods based on improved support vector machine (SVM) are proposed for sensor fault detection and identification respectively. First, online sparse least squares support vector machine (OS-LSSVM) is utilized to detect and predict sensor faults. Then, a method which combines the SVM and error-correcting output codes (ECOC) called ECOC-SVM is proposed to solve the sensor fault feature extraction and online identification problem. We regard nonlinear transformation as the input of classifiers to enhance the separability of initial characteristics. ECOC-SVM is utilized to classify the fault states. Some typical faults are investigated and the experimental results indicate that ECOC-SVM has high identification accuracy and can be implemented in real-time to meet the requirements of online fault identification. This method can also be extended to solve other related problems.
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2015.2487523