An efficient signal processing tool for impedance-based structural health monitoring

Megan K. O'Brien; Stuart G. Taylor; Kevin M. Farinholt; Gyuhae Park; Charles R. Farrar

doi:10.1117/12.815532

9 April 2009 An efficient signal processing tool for impedance-based structural health monitoring

Megan K. O'Brien, Stuart G. Taylor, Kevin M. Farinholt, Gyuhae Park, Charles R. Farrar

Proceedings Volume 7295, Health Monitoring of Structural and Biological Systems 2009; 72951K (2009) https://doi.org/10.1117/12.815532
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2009, San Diego, California, United States

Abstract

Various experimental studies have demonstrated that an impedance-based approach to structural health monitoring can be an effective means of damage detection. Using the self-sensing and active-sensing capabilities of piezoelectric materials, the electromechanical impedance response can be monitored to provide a qualitative indication of the overall health of a structure. Although impedance analyzers are commonly used to collect such data, they are bulky and impractical for long-term field implementation, so a smaller and more portable device is desired. However, impedance measurements often contain a sizeable number of data points, and a smaller device may not possess enough memory to store the required information, particularly for real-time analysis. Therefore, the amount of data used to assess the integrity of a structure must be significantly reduced. A new type of cross correlation analysis, for which impedance data is instantaneously correlated between different sensor sets and different frequency ranges, as opposed to be correlated to pre-stored baseline data, is proposed to drastically reduce the amount of data to a single correlation coefficient and provide a quantitative means of detecting damage relative to the sensor positions. The proposed analysis is carried out on a 3-story representative structure and its efficiency is demonstrated.

Citation Download Citation

Megan K. O'Brien, Stuart G. Taylor, Kevin M. Farinholt, Gyuhae Park, and Charles R. Farrar "An efficient signal processing tool for impedance-based structural health monitoring", Proc. SPIE 7295, Health Monitoring of Structural and Biological Systems 2009, 72951K (9 April 2009); https://doi.org/10.1117/12.815532

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