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.

References

［1］李圣怡，吴学忠，范大鹏. 多传感器融合理论及在智能制造系统中的应用［M］. 长沙：国防科技大学出版社, 1998.

［2］Sasiadek J Z.Sensor fusion［J］．Annual Reviews in Control，2002，26(2)：213228.

［3］Luo R, Yih C, Su K. Multisensor fusion and integration: Approaches, applications and future research directions［J］. IEEE Sensors Journal, 2002, 2(2): 107117.

［4］王秀青, 侯增广, 谭民. 多传感器数据融合技术在移动机器人中应用的新进展［J］. 哈尔滨工业大学学报, 2006, 38(S): 10301034.

WANG Xiuqing, HOU Zengguang, TAN Min. Recent progress on multisensor data fusion techniques for mobile robots［J］. Journal of Harbin Institute of Technology, 2006，38(S): 10301034.

［5］赵小川,罗庆生,韩宝玲. 机器人多传感器信息融合研究综述［J］. 传感器与微系统, 2008, 27(8):14,11.

ZHAO Xiaochuan, LUO Qingsheng, HAN Baoling. Review of robot multisensor information fusion［J］. Transducer and Microsystem Technologies, 2008, 27(8):14, 11.

［6］Berghfer E, Schulze D, Rauch C. ARTbased fusion of multimodal perception for robots［J］. Neurocomputing, 2013, 107: 1122.

［7］Liu S H, Huang T S, Yen J Y. Comparison of sensor fusion methods for an SMAbased hexapod biomimetic robot［J］. Robotics and Autonomous Systems, 2010, 58: 737744.

［8］Pateraki M, Baltzakis H, Trahanias P. Visual estimation of pointed targets for robot guidance via fusion of face pose and hand orientation［J］. Computer Vision and Image Understanding, 2014, 120: 113.

［9］Vapnik V. The nature of statistical learning theory［M］. New York: SpringVerlag, 1995.

［10］Boser B E, Guyon I, Vapnik V. A training algorithm for optimal margin classifiers［C］∥Proceedings of the Fifth Annual Workshop on Computational Learning Theory. Pittsburgh: ACM Press, 1992: 144152.

［11］Kohonen T. The selforganizing map ［J］. Proceedings of the IEEE, 1990, 78(9): 14641480.

［12］Robert E S. The strength of weak learn ability［J］. Machine Learning, 1990, 5 (2): 197227.

［13］Seabra L L. Robot learning at the task level: A study in the assembly domain［D］. Portugal: University Date Nova de Lisboa, 1997.

［14］Friedman J H，Trevor R T．Additive logistic regression：A statistical view of boosting［J］．The Annals of Statistics，2000，38(2)：337374.

［15］Bache K, Lichman M. UCI machine learning repository ［DB/OL］.［20150214］.http:∥www.archive.ics.uci.edu/ml.

［16］Bouckaert R R, Frank E, Hall M, et al. Weka ［CP/OL］.［20150214］.http:∥www.cs.waikato.ac.nz.

［17］Chang C C，Lin C J．LIBSVM：A library for support vector machines［J］． ACM Transactions on Intelligent Systems and Technology (TIST), 2011，2(3)：127.

［18］Faruto, Li Y. Libsvmfaruto ultimate 3.0［CP/OL］.［20150214］. http://www.ilovematlab.cn/forum.php?mod=viewthread&tid=35262.

［19］CamarinhaMatos L M, Seabra L L, Barata J. Integration and learning in supervision of flexible assembly systems［J］. IEEE Transactions on Robotics and Automation, 1996, 12(2): 202219.

[1]	PAN Meiqi, HE Xing. A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning [J]. Journal of Shanghai Jiao Tong University, 2025, 59(5): 561-568.
[2]	Wang Wei, Zhou Cheng, Jiang Jinlei, Cui Xinyuan, Yan Guozheng, Cui Daxiang. Optimization of Wireless Power Receiving Coil for Near-Infrared Capsule Robot [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 425-432.
[3]	Li Tao, Zhao Zhigang, Zhu Mingtong, Zhao Xiangtang. Cable Vector Collision Detection Algorithm for Multi-Robot Collaborative Towing System [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 319-329.
[4]	Nie Wei, Liang Xinwu. Efficient Fully Convolutional Network and Optimization Approach for Robotic Grasping Detection Based on RGB-D Images [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 399-416.
[5]	ZHAO Yanfei^1，2，3(赵艳飞), XIAO Peng⁴ (肖鹏), WANG Jingchuan^1，2，3* (王景川), GUO Rui^4*(郭锐）. Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 27-33.
[6]	XUE Guiting, LIU Zhe, HAN Zhaoru, SHI Fang, WANG Ti, WANG Xiao. Switching Modeling and Application in Fault Diagnosis Algorithm Testing of Distribution Network [J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1381-1389.
[7]	WANG Qi, LI Lüzhou, DONG Xu, YUAN Ningyi, DING Jianning. Development of a Small Jellyfish Robot with Controllable Trajectory [J]. Journal of Shanghai Jiao Tong University, 2024, 58(8): 1240-1248.
[8]	ZHAO Wei, YUAN Shaoke, LI Yinan, FEI Yanqiong. Design and Motion Analysis of Bionic Jaw Mechanism for Humanoid Expression Robot [J]. Journal of Shanghai Jiao Tong University, 2024, 58(8): 1231-1239.
[9]	LIU Yuesheng (刘月笙), HE Ning^∗(贺宁), HE Lile (贺利乐), ZHANG Yiwen (张译文), XI Kun (习坤), ZHANG Mengrui (张梦芮). Self-Tuning of MPC Controller for Mobile Robot Path Tracking Based on Machine Learning [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(6): 1028-1036.
[10]	ZHANG Chunmei, XU Xingque, LIU Silin. Distribution Network Fault Diagnosis Technology Based on Multi-Source Data Fusion [J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 739-746.
[11]	MU Jianbin^∗ (穆建彬), YANG Haili (杨海丽), HE Defeng (何德峰). CBF-Based Distributed Model Predictive Control for Safe Formation of Autonomous Mobile Robots [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 678-688.
[12]	LV Feng(吕峰), WANG Xinyan^* (王新彦), LI Lei(李磊), JIANG Quan(江泉), YI Zhengyang(易政洋). Tree Detection Algorithm Based on Embedded YOLO Lightweight Network [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 518-527.
[13]	XU Yaru(徐亚茹), LI Kehong(李克鸿), SHANG Xinna（商新娜）, JIN Xiaoming(金晓明), LIU Rong(刘荣), ZHANG Jiancheng（张建成）. Establishment of Constraint Relation of Robot Dynamics Equation Based on Kinematic Influence Coefficients Method [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 450-456.
[14]	HUANG Shan(黄山), HUANG Hongzhong(黄洪钟), ZENG Qi(曾奇). Fast Four-Stage Local Motion Planning Method for Mobile Robot [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 428-435.
[15]	MIAO Zhenhua(苗镇华), HUANG Wentao(黄文焘), ZHANG Yilian(张依恋), FAN Qinqin(范勤勤). Multi-Robot Task Allocation Using Multimodal Multi-Objective Evolutionary Algorithm Based on Deep Reinforcement Learning [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 377-387.