Abstract
In order to strengthen the safety and to monitor the working states of numerical control system real time, a framework was developed to diagnose multi-kinds of potential connection-related faults in the system. This framework considered that the number of fault data is small and the map from position to applied torque is a complex nonlinear function, and adopted the method of support vector machine to decide whether fault states had occurred or would occur. Position signal and the torque monitoring one in this system were used to train the parameters of support vector machine where Gaussian function was employed as nonlinear kernel. The faults mentioned in this research were diagnosed from a simple decision function real time, where the parameters were from the trained results. The framework and a trained decision function were applied to an X–Y motion platform, where the process included data acquisition, training of support vector machine and fault diagnosis. Results validate that three working states including two kinds of faults can be diagnosed easily at the same time, and the method can be used to numerical control system feasibly.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Torgny B (2007) Present and future robot control development—an industrial perspective. Annu Rev Control 31:69–79
Isermann R (2005) Model-based fault-detection and diagnosis–status and applications”. Annu Rev Control 29:71–85
Liu H, George M (2005) A model-based approach to robot fault diagnosis. Knowl Based Sys 18:225–233
Vandi V, Reid S (2006) Scalable robot fault detection and identification. Rob Auton Sys 54:184–191
Nalinaksh S, Vyas DS (2001) Artificial neural network design for fault identification in a rotor-bearing system. Mech Mach Theory 36:157–175
Lei Y, He Z, Zi Y (2008) Fault diagnosis based on novel hybrid intelligent mode. Chin J Mech Eng 44(7):112–117
Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cyber 2:107–122
He Q, Wu CX (2011) Separating theorem of samples in Banach space for support vector machine learning. Int J Mach Learn Cyber 2:49–54
Liu Z, Wu QH, Zhang Y et al (2011) Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery. Int J Mach Learn Cyber 2:37–47
Zhang L, Xiong GL, Liu HS et al (2007) TVAR and SVM based fault diagnosis method for rotation machine. Proc CSEE 27(9):99–103
Fang R, Zheng L, Ma HH et al (2007) Fault diagnosis for rotor of induction machine based on MCSA and SVM. Chin J Sci Instrum 28(2):252–257
Xiao JZ, Wang HR, Gao Z (2009) Diagnosis method for connection-related faults in motion system based on SVM. Proc Int Conf Mach Learn Cyber 1:1053–1058
Duan ZH, Cai ZX, Yu JX (2005) Fault diagnosis and fault tolerant control of mobile robots in unknown environments:a survey. Robot 27(4):373–379
Reyes F, Rosado A (2005) Polynomial family of PD-type controllers for robot manipulators. Control Eng Pract 13:441–450
Shigeo A (2005) Support vector machines for pattern classification. Springer, London
Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin
Acknowledgments
This paper is supported by the National Foundation (No.F2007000223) and the High Technical Foundation (No. 07212106D, 07212128) of Hebei province, China. Thank Dr. Jian-hong Mei gave professional help to correct the expression of the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xiao, JZ., Wang, HR., Yang, XC. et al. Multiple faults diagnosis in motion system based on SVM. Int. J. Mach. Learn. & Cyber. 3, 77–82 (2012). https://doi.org/10.1007/s13042-011-0035-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-011-0035-y