Fault Diagnosis of Robots Based on Multi-Sensor Information Fusion

Abstract

Abstract:

Abstract： A novel multi-sensor information fusion method combined with the support vector machine (SVM) was proposed in diagnosing three types of faults which are collision, front collision and obstruction, as the robot’s arm approaches the grasping place. After fusing the proper number of the data from multisensors and searching the optimal parameters C and γ of the SVM by grid searching, the proposed method can successfully diagnose the faults of obstruction, front collision and collision. Besides, the selection of the number of the features of data to be fused by multisensor information fusion was discussed. The experimental results show that the selection of the proper number of the fusing features of the sampling data influences the number of fusion data obtained and the accuracy of classification.

Key words: multi-sensor information fusion, robot, fault diagnosis, classification； support vector machine (SVM)

CLC Number:

TP 24

WANG Xiuqing1,HOU Zengguang2,ZENG Hui3,L Feng1,PAN Shiying1. Fault Diagnosis of Robots Based on Multi-Sensor Information Fusion[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 793-798.

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