Snake Skeleton Graph: A New Method for Analyzing Signals That Contain Spatial Information
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Published:2003-09-01
Issue:3
Volume:125
Page:294-302
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ISSN:0022-0434
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Container-title:Journal of Dynamic Systems, Measurement, and Control
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language:en
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Short-container-title:
Author:
Du Ruxu1, Guo W. Z.1, Xu Diana2, Liasi Evangelos2
Affiliation:
1. Department of Automation and Computer Aided Engineering, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong, China 2. Press-Room Technical Support, Stamping Business Unit, Vehicle Operation, Ford Motor Company, Dearborn, MI 48121
Abstract
Many modern engineering systems use multiple sensors for monitoring, diagnosis and control. Some of these sensors contain not only time domain and frequency domain information, but also valuable spatial domain information to which little attention has been paid. This paper presents a new method for capturing the spatial characteristics of the sensor signals. The basic idea is to model the spatial information of the signals using a Be´zier surface. For example, given m one-dimensional force signals: X1t,X2t,…,Xmt, at each time instance t, a Be´zier surface can be constructed, which describes the distribution of the force. Furthermore, lining up the surfaces at different time t1,t2,…,tn, will show how the force changes as a function of time. Since the graph looks like a snake skeleton, the new method is called the snake skeleton graph. The paper first describes how the snake skeleton graph is constructed using a demonstration example: a foot walking on a plate. Then it presents an application for fault diagnosis in sheet metal stamping operation. Future research topics are also discussed.
Publisher
ASME International
Subject
Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering
Reference22 articles.
1. Du, R., 2000, “Fault Diagnosis,” Encyclopedia of Electrical and Electronics Engineering, edited by John G. Webster, Article No. 2608, John Wiley, New York. 2. Qu, L. et al., 1989, “The Holospectrum: A New Method for Rotor Surveillance and Diagnosis,” Mech. Syst. Signal Process., 3, pp. 255–267. 3. Du, R., Chen, Y. D., and Chen, Y. B., 1996, “Four Dimensional Holospectrum—A New Method for Analyzing Force Distributions,” ASME J. Manuf. Sci. Eng.,, 119, pp. 95–104. 4. Choi, H., and Williams, W. J., 1989, “Improved Time-Frequency Representation of Multicomponent Signals Using Exponential Kernels,” IEEE Trans. Acoust., Speech, Signal Process., 37, pp. 862–871. 5. Mallat, S. G.
, 1989, “Multifrequency Channel Decomposition of Images and Wavelet Models,” IEEE Trans. Acoust., Speech, Signal Process., 37, pp. 2091–2110.
Cited by
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