Paper
30 March 2009 Vibration-based structural health monitoring technique using statistical features for data stability assessment and damage localization
Z. S. Wu, A. P. Adewuyi
Author Affiliations +
Abstract
A statistical vibration-based damage identification algorithm to assess the stability of the measurement data, detect and locate damage in civil structures, where variability in response and modal parameters due to measurement noise and environmental influence is often inevitable, is presented in this paper. The algorithm utilizes the statistical correlation of the magnitudes of frequency response function (FRF) of target sensors relative to a reference sensor to assess the consistency of the data while the slopes of lines of fit are exploited for damage localization. Through numerical simulation and experimental investigation of a flexural structure using accelerometers and "point" strain gauges, and long gauge fiber Bragg gratings (FBG) sensors, the importance of the technique for civil SHM is established and presented in an easy-to-interpret graphical format for effective implementation of results. The proposed method using long-gauge FBG sensors is suitable for practical civil SHM with limited number of sensors and where variability in response and modal parameters due to measurement noise and environmental influence is often inevitable. Also, the ability to effectively manage and make sense of enormous amounts of data collected under continuous monitoring process for an effective diagnostic and/or prognostic system is an added advantage.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Z. S. Wu and A. P. Adewuyi "Vibration-based structural health monitoring technique using statistical features for data stability assessment and damage localization", Proc. SPIE 7292, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2009, 729233 (30 March 2009); https://doi.org/10.1117/12.817518
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Sensors

Fiber Bragg gratings

Structural health monitoring

Environmental sensing

Statistical analysis

Numerical simulations

Data acquisition

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