Subspace-Based Algorithms for Structural Identification, Damage Detection, and Sensor Data Fusion
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Research Article Subspace-Based Algorithms for Structural Identification, Damage Detection, and Sensor Data Fusion ` Basseville,1, 2 Albert Benveniste,1, 3 Maurice Goursat,4 and Laurent Mevel1, 3 Michele 1 IRISA,
Campus Universitaire de Beaulieu, 35042 Rennes Cedex, France Campus Universitaire de Beaulieu, 35042 Rennes Cedex, France 3 INRIA, Campus Universitaire de Beaulieu, 35042 Rennes Cedex, France 4 INRIA, Domaine de Voluceau Rocquencourt, BP 105, 78153 Le Chesnay Cedex, France 2 CNRS,
Received 2 February 2006; Revised 3 March 2006; Accepted 27 May 2006 Recommended by George Moustakides This paper reports on the theory and practice of covariance-driven output-only and input/output subspace-based identification and detection algorithms. The motivating and investigated application domain is vibration-based structural analysis and health monitoring of mechanical, civil, and aeronautic structures. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Framework Detecting and localizing damages for monitoring the integrity of structural and mechanical systems is a topic of growing interest, due to the aging of many engineering constructions and machines and to increased safety norms. Many current approaches still rely on visual inspections or local nondestructive evaluations performed manually, for example acoustic, ultrasonic, radiographic or eddy-current methods. These experimental approaches assume an a priori knowledge and the accessibility of a neighborhood of the damage location. Automatic global vibration-based monitoring techniques have been recognized to be useful alternatives to those local evaluations [1–5]. Many structures to be monitored (e.g., civil engineering structures subject to wind and earthquakes, aircrafts subject to turbulence) are subject to both fast and unmeasured variations in their environment and small slow variations in their modal (vibrating) properties. While any change in the excitation is meaningless, damages or fatigues on the structure are of interest. But the available measurements (e.g., from accelerometers) do not separate the effects of the external forces from the effect of the structure. Moreover the changes of interest (1% in eigenfrequencies) neither are visible on the signals nor on their spectra. A global health monitoring method must rather rely on a model which will help in discriminating
between the two mixed causes of the changes that are contained in the data. Most classical modal analysis and vibration monitoring methods basically process data registered either on test beds or under specific excitation or rotation speed conditions. However a need has been recognized for vibration monitoring algorithms devoted to the processing of data recorded inoperation, namely, during the actual functioning of the considered structure or machine, without artificial excitation, speeding down or stopping [6, 7]. In this framework, covariance-driven input/output and output-only subspace-based algorithms have been developed for the purpose of struc
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