A comprehensive experimental study is presented to assess the utility of a proposed structural health monitoring and damage detection methodology based on vibration signature analysis of the test article. The approach uses a time-domain least-squares-based method to identify the reduced-order system matrices of an equivalent linear model whose order matches the number of available sensors. A quantification of the level of the system nonlinearity is obtained by determining the residual nonlinear forces involved in the system dynamics. The approach is applied to an intricate mechanical system about which virtually no information was available; i.e., the system was essentially a “black box.” By using similar measurements from a reference version of the test article and two subsequently modified versions, it is shown that through the use of higher-order statistics involving the probability density functions of key system parameters, a reliable measure of the extent of variation of the system influence functions may be obtained. The use of measures of the identified quantities’ dispersion offers a practical method for quantifying the reliability of the estimated changes involved in dealing with real-world (i.e., not noise-free) measurements that result in uncertain estimates of the physical changes in the article being monitored. [S0021-8936(00)03303-1]

Issue Section:
Technical Papers
Keywords:
vibrations, surveillance, structural engineering, least squares approximations, time-domain analysis, matrix algebra, higher order statistics, probability, identification, condition monitoring
Topics:
Damage, Excitation, Noise (Sound), Probability, Statistics, Stiffness, Surveillance, Vibration, Structural health monitoring, Sensors, Density, System dynamics, Nonlinear systems
1.
Braun, S. G., ed., 1986, Mechanical Signature Analysis: Theory and Applications, Academic Press.
2.
Chang, F.-K., ed., 1997, “Structural Health Monitoring, Current Status and Perspectives,” Proceedings of the International Workshop on Structural Health Monitoring, Stanford University, Stanford, CA, Sept. 18–20, Technomic, Lancaster, PA.
3.
Natke, H. G., Tomlinson, G. R., and Yao, J. T. P., eds., 1993, Safety Evaluation Based on Identification Approaches Related to Time-Variant and Nonlinear Structures, Friedr. Vieweg & Sohn.
4.
Doebling, S. W., Farrar, C. H., Prime, M. B., and Shevitz, D. W., 1996, “Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in their Vibration Characteristics: A Literature Review,” Tech. Report LA-13070-MS, Los Alamos National Laboratory Report.
5.
Tomlinson
,
G. R.
, and
Worden
,
K.
,
1994
, “
Modeling and Classification of Non-linear Systems Using Neural Networks. Part I: Simulation
,”
J. Mech. Syst. Signal Process.
,
8
, pp.
319
–
356
.
6.
Housner
,
G. W.
,
Bergman
,
L. A.
,
Caughey
,
T. K.
,
Chassiakos
,
A. G.
,
Claus
,
R. O.
,
Masri
,
S. F.
,
Skelton
,
R. E.
,
Soong
,
T. T.
,
Spencer
,
B. F.
, and
Yao
,
J. T. P.
,
1997
, “
Structural Control: Past, Present and Future
,”
ASCE J. Eng. Mech.
, (Special Issue),
123
, No.
9
, pp.
897
–
971
.
7.
Beck, J. L., and Katafygiotis, L. S., 1992, “Probabilistic System Identification and Health Monitoring of Structures.” Proc. 10th World Conference on Earthquake Engineering, Madrid, Spain.
8.
Beck
,
J. L.
, and
Katafygiotis
,
L. S.
,
1998
, “
Updating Models and their Uncertainties. I: Bayesian Statistical Framework
,”
ASCE J. Eng. Mech.
,
124
, pp.
455
–
461
.
9.
Sohn
,
H.
, and
Law
,
K. H.
,
1997
, “
Bayesian Probabilistic Approach for Structural Damage Detection
,”
J. Earthquake Eng. Struct. Dyn.
,
26
, pp.
1259
–
1281
.
10.
Agbabian
,
M. S.
,
Masri
,
S. F.
,
Miller
,
R. F.
, and
Caughey
,
T. K.
,
1990
, “
System Identification Approach to Detection of Structural Changes
,”
ASCE J. Eng. Mech.
,
117
, No.
2
, pp.
370
–
390
.
11.
Ghanem
,
R.
, and
Shinozuka
,
M.
,
1995
, “
Structural-System Identification. I: Theory
,”
ASCE J. Eng. Mech.
,
121
, pp.
255
–
264
.
12.
Loh
,
C.-H.
, and
Tou
,
I.-C.
,
1995
, “
A System Identification Approach to the Detection of Changes in Both Linear and Nonlinear Structural Parameters
,”
J. Earthquake Eng. Struct. Dyn.
,
24
, p.
85
–
97
.
13.
Juang
,
J. N.
,
Phan
,
M.
,
Horta
,
L. G.
, and
Longman
,
R. W.
,
1993
, “
Identification of Observer/Kalman Filter Markov Parameters: Theory and Experiments
,”
J. Guid. Control Dyn.
,
16
, No.
2
, pp.
320
–
329
.
14.
Farrar, C. R., and Jauregui, D., 1996, “Damage Detection Algorithms Applied to Experimental and Numerical Modal Data from the I-40 Bridge,” Report No. LA-13074-MS, Los Alamos National Laboratory, Los Alamos, NM.
15.
Zimmerman
,
D. C.
,
Smith
,
S. W.
,
Kim
,
H. M.
, and
Bartkowicz
,
T. J.
,
1996
, “
An Experimental Study of Structural Health Monitoring using Incomplete Measurements
,”
ASME J. Vibr. Acoust.
,
118
, pp.
543
–
550
.
16.
Loh, C.-H., and Wu, T.-C., 1999, “System Identification of Fei-Tsui Arch Dam from Forced Vibration and Seismic Response Data,” J. Earthquake Eng. Struct. Dyn., (to appear).
17.
Shinozuka
,
M.
, and
Ghanem
,
R.
,
1995
, “
Structural System Identification. II: Experimental Verification
,”
ASCE J. Eng. Mech.
,
121
, p.
265
–
273
.
18.
Loh
,
C.-H.
, and
Lin
,
H.-M.
,
1996
, “
Application of Off-Line and On-Line Identification Techniques to Building Seismic Response Data
,”
J. Earthquake Eng. Struct. Dyn.
,
25
, pp.
269
–
90
.
19.
Smyth
,
A. W.
,
Masri
,
S. F.
,
Chassiakos
,
A. G.
, and
Caughey
,
T. K.
,
1999
, “
On-Line Parametric Identification of MDOF Nonlinear Hysteretic Systems
,”
ASCE J. Eng. Mech.
,
125
, No.
2
, pp.
133
–
142
.
20.
Sato
,
T.
, and
Qi
,
K.
,
1998
, “
Adaptive H∞ Filter: Its Applications to Structural Identification
,”
ASCE J. Eng. Mech.
,
124
, No.
11
, pp.
1233
–
1240
.
21.
Golub, G. H., and Van Loan, C. F., 1983, Matrix Computations, Johns Hopkins University Press, Baltimore, MD.
22.
Brogan, W. L., 1985, Modern Control Theory, 2nd Ed., Quantum Publishers, New York.
23.
Smyth, A. W., 1998, “Experimental and Analytical Studies in Nonlinear System Identification and Modeling for Structural Control,” Ph.D. thesis, University of Southern California, Los Angeles, CA.
24.
Masri
,
S. F.
,
Smyth
,
A. W.
,
Chassiakos
,
A. G.
,
Nakamura
,
M.
, and
Caughey
,
T. K.
,
1999
, “
Training Neural Networks by Adaptive Random Search Techniques
,”
ASCE J. Eng. Mech.
,
125
, No.
2
, pp.
123
–
132
.
25.
Masri
,
S. F.
,
Miller
,
R. W.
,
Saud
,
A. F.
, and
Caughey
,
T. K.
,
1987
, “
Identification of Nonlinear Vibrating Structures
,”
ASME J. Appl. Mech.
,
54
, pp.
918
–
929
.
26.
Worden, K., 1989, “Parametric and Nonparametric Identification of Nonlinearity in Structural Dynamics,” Ph.D. thesis, Department of Mechanical Engineering, Heriot-Watt University, Edinburgh.
27.
Loh, C.-H., and Duh, G.-Y., 1996, “Frequency Domain and Time Domain Identification on Nonlinear Structural System,” Proceedings of the 11th World Conference on Earthquake Engineering, Acapulco, Mexico, Pergamon, Oxford, UK, p. 653.
28.
IMAC, 1998, Proceedings of the 16th International Modal Analysis Conference, Santa Barbara, CA, Society of Experimental Mechanics.
29.
IMAC, 1999, Proceedings of the 17th International Modal Analysis Conference, Kissimmee, FL, Society of Experimental Mechanics.
Copyright © 2000
 by ASME
You do not currently have access to this content.