Paper
11 April 2007 On piezoelectric Lamb wave-based structural health monitoring using instantaneous baseline measurements
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Abstract
A critical aspect of existing Structural Health Monitoring (SHM) systems is the ability to compare current data obtained from a structure to a prerecorded baseline measurement taken for an undamaged case. Several Lamb wave-based SHM techniques have been successfully developed that use baseline measurements to identify damage in structures. The method developed in this study aims to instantaneously obtain baseline measurements in order to eliminate any complications associated with archiving baseline data and with the effects of varying environmental conditions on the baseline data. The proposed technique accomplishes instantaneous baseline measurements by deploying an array of piezoelectric sensors/actuators used for Lamb wave propagation-based SHM such that data recorded for equidistant sensor-actuator path lengths can be used to instantaneously identify several common features of undamaged paths. Once identified, data from these undamaged paths can be used as a baseline for near real-time damage detection. This method is made possible by utilizing sensor diagnostics, a recently developed technique which minimizes false damage identification and measurement distortion caused by faulty sensors. Several aspects of the instantaneous baseline damage detection method are detailed in this paper including determination of the features best used to identify damage, development of signal processing algorithms used to analyze data, and a comparison of two sensor/actuator deployment schemes.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Steven R. Anton, Gyuhae Park, Charles R. Farrar, and Daniel J. Inman "On piezoelectric Lamb wave-based structural health monitoring using instantaneous baseline measurements", Proc. SPIE 6532, Health Monitoring of Structural and Biological Systems 2007, 65320B (11 April 2007); https://doi.org/10.1117/12.715854
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Cited by 11 scholarly publications.
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KEYWORDS
Corrosion

Sensors

Structural health monitoring

Ferroelectric materials

Aluminum

Data analysis

Diagnostics

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