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灰色关联理论下的移动机器人故障诊断方法研究 被引量:3

Method of mobile robot fault diagnosis based on grey correlation theory
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摘要 随着计算机理论、电子信息技术、自动控制理论、机械自动化等学科的发展和新型材料的应用,移动机器人技术突飞猛进,对移动机器人进行故障诊断具有重要意义。首先,通过拆解移动机器人的硬件,了解移动机器人各个模块的工作原理并分析其可能出现的故障;接着,对故障状态下的数据和正常运行状态下的数据进行分析比较,利用灰色关联理论诊断故障;最后,利用Matlab仿真对故障诊断方法进行验证。仿真实验和实物试验证明,该方法具有良好的效果。 With the development of computer theory,electronic information technology,automatic control theory and mechanical automation and the application of the new materials,mobile robot technology is advancing by leaps and bounds,which has great significant to the fault diagnosis of mobile robot. The working principle of each module of the mobile robot is understood and the possible faults are analyzed by disassembling the hardware of the mobile robot. The data in the fault state and the data in the normal running state are analyzed and compared,and the faults are diagnosed by means of the grey correlation theory. The fault diagnosis method is verified with Matlab simulation. The simulation experiments and physical test show that the method has a certain effect.
作者 蒋文萍 闵军 吴其鑫 汪晹 JIANG Wenping;MIN Jun;WU Qixin;WANG Yi(School of Electrical and Electronic Engineering,Shanghai Institute of Technology,Shanghai 201418,China)
出处 《现代电子技术》 北大核心 2020年第12期165-169,共5页 Modern Electronics Technique
基金 国家自然科学基金重点项目(61333008) 上海市科委联盟项目(LM201728)。
关键词 故障诊断 移动机器人 灰色关联理论 数据分析 MATLAB仿真 测试分析 fault diagnosis mobile robot grey relational theory data analysis Matlab simulation testing analysis
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  • 1Yu Lingli, Wu Min, Cai Zixing, et al. A particle filter and SVM integration framework for fault-proneness prediction in robot dead reckoning system[J]. WSEAS Transactions on System, 2011, 10(11): 868-875. 被引量:1
  • 2Auger F, Hilairet M, Guerrero J M, et al. Industrial applications of the kalman filter: a review[J]. IEEE Transactions on Industrial Electronics, 2013, 60(12): 5458-5471. 被引量:1
  • 3Ni Jianjun, Zhang Chuanbiao, Yang Simon X. An a daptive approach based on KPCA and SVM for real- time fault diagnosis of HVCBs[J]. IEEE Transactions on Power Delivery, 2011, 26(3): 1960-1971. 被引量:1
  • 4Chang 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. 被引量:1
  • 5Liu Zhiwen, Cao Hongrui, Chen Xuefeng, et al. Multi-fauh classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings[J]. Neuroeomputing, 2013, 99:399- 410. 被引量:1
  • 6Sasiadek J Z. Sensor fusion[J]. Annual Reviews in Control,2002,26(2) :213-228. 被引量:1
  • 7Luo R, Yih C, Su K. Multisensor fusion and integra- tion: Approaches, applications and future research directions[J]. IEEE Sensors Journal, 2002, 2(2): 107-117. 被引量:1
  • 8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. 被引量:1
  • 9Berghbfer E, Schulze D, Rauch C. ART-based fusion of multi-modal perception for robots[J]. Neuroeom- paring, 2013, 107: 11-22. 被引量:1
  • 10Liu S H, Huang T S, Yen J Y. Comparison of sen- sor fusion methods for an SMA-based hexapod biomi- metic robot [J]. Robotics and Autonomous Systems, 2010, 58: 737-744. 被引量:1

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