A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System
Joint Authors
Zhou, Fengyu
Li, Yan
Yuan, Xianfeng
Song, Mumin
Chen, Zhumin
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-02
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots.
To improve the safety and reliability of wheeled robots, this paperpresents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion.
Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction.
The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis.
To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well.
The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values.
Eventually, the final fault diagnosis result is archived by the fusion of the BPAs.
Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.
American Psychological Association (APA)
Yuan, Xianfeng& Song, Mumin& Zhou, Fengyu& Chen, Zhumin& Li, Yan. 2015. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1057724
Modern Language Association (MLA)
Yuan, Xianfeng…[et al.]. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1057724
American Medical Association (AMA)
Yuan, Xianfeng& Song, Mumin& Zhou, Fengyu& Chen, Zhumin& Li, Yan. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1057724
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057724