An Outlier Analysis Framework for Impedance-based Structural Health Monitoring

Abstract This paper presents the use of statistically rigorous algorithms combined with active-sensing impedance methods for damage identification in engineering systems. In particular, we propose to use statistical pattern recognition methods to address damage classification and data mining issues associated with the examination of large numbers of impedance signals for health monitoring applications. In this paper, in order to diagnose damage with statistical confidence, the impedance-based monitoring is cast in the context of an outlier detection framework. A modified auto-regressive model with exogenous inputs (ARX) in the frequency domain is developed and the damage-sensitive feature is obtained by quantifying the differences between the measured impedance and the output of the ARX model. Furthermore, because of the non-Gaussian nature of the feature distribution tails, extreme value statistics is employed to develop a robust damage classifier. This paper concludes with a numerical example on a 5 degree-of-freedom system and an experimental investigation on a multi-story building model to demonstrate the performance of the proposed concept.

[1]  Daniel J. Inman,et al.  IMPEDANCE-BASED HEALTH MONITORING OF CIVIL STRUCTURAL COMPONENTS , 2000 .

[2]  C. R. Farrar,et al.  A STATISTICAL PATTERN RECOGNITION PARADIGM FOR VIBRATION-BASED STRUCTURAL HEALTH MONITORING , 2000 .

[3]  Hoon Sohn,et al.  Structural damage classification using extreme value statistics , 2005 .

[4]  Keith Worden,et al.  DAMAGE DETECTION USING OUTLIER ANALYSIS , 2000 .

[5]  C. Liang,et al.  Truss Structure Integrity Identification Using PZT Sensor-Actuator , 1995 .

[6]  L. Haan,et al.  Extreme value theory , 2006 .

[7]  Charles R. Farrar,et al.  Classifying Linear and Nonlinear Structural Damage Using Frequency Domain ARX Models , 2002 .

[8]  Victor Giurgiutiu,et al.  Electro-Mechanical Impedance Method for Crack Detection in Thin Plates , 2001 .

[9]  Randall J. Allemang,et al.  Discrete Frequency Models: A New Approach to Temporal Analysis , 2001 .

[10]  Douglas E. Adams.  FREQUENCY DOMAIN ARX MODEL AND MULTI-HARMONIC FRF ESTIMATORS FOR NON-LINEAR DYNAMIC SYSTEMS , 2002 .

[11]  Enrique Castillo.  Extreme value theory in engineering , 1988 .

[12]  C. Liang,et al.  Coupled Electro-Mechanical Analysis of Adaptive Material Systems-Determination of the Actuator Power Consumption and System Energy Transfer , 1997 .

[13]  C. Liang,et al.  Structural frequency response function acquisition via electric impedance measurement of surface-bonded piezoelectric sensor /actuator , 1995 .

[14]  Hoon Sohn,et al.  Overview of Piezoelectric Impedance-Based Health Monitoring and Path Forward , 2003 .

[15]  Gary Cloud,et al.  The application of interferometric techniques to the nondestructive inspection of fiber-reinforced materials , 1993 .

[16]  Hoon Sohn,et al.  VIBRATION-BASED DAMAGE DETECTION USING STATISTICAL PROCESS CONTROL , 2001 .

[17]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[18]  Hoon Sohn,et al.  Damage diagnosis using time series analysis of vibration signals , 2001 .

[19]  Charles R. Farrar,et al.  Structural Health Monitoring Using Statistical Pattern Recognition Techniques , 2001 .

[20]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[21]  Charles R. Farrar,et al.  A statistical pattern recognition paradigm for structural health monitoring , 2004 .

[22]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.