title: A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.
abstract: 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 paper presents 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.
authors.affiliation: School of Control Science and Engineering, Shandong University, Jinan 250061, China.
authors.forename: Xianfeng
authors.initials: X
authors.lastname: Yuan
authors.affiliation: School of Control Science and Engineering, Shandong University, Jinan 250061, China.
authors.forename: Mumin
authors.initials: M
authors.lastname: Song
authors.affiliation: School of Control Science and Engineering, Shandong University, Jinan 250061, China.
authors.forename: Fengyu
authors.initials: F
authors.lastname: Zhou
authors.affiliation: School of Computer Science and Technology, Shandong University, Jinan 250101, China.
authors.forename: Zhumin
authors.initials: Z
authors.lastname: Chen
authors.affiliation: School of Control Science and Engineering, Shandong University, Jinan 250061, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
mesh_terms: D008962:Models, Theoretical; D025341:Principal Component Analysis / Q000379:methods*; D012371:Robotics / Q000295:instrumentation* / Q000379:methods*; D060388:Support Vector Machine / Q000706:statistics & numerical data*