Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure

• Published in: Journal of sound and vibration Vol. 291; no. 1; pp. 349 - 368
• Main Authors: Nair, K. Krishnan, Kiremidjian, Anne S., Law, Kincho H.
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 21.03.2006; Elsevier
• Subjects: Exact sciences and technology; Fundamental areas of phenomenology (including applications); Measurement and testing methods; Physics; Solid mechanics; Structural and continuum mechanics; Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...); ARMA model; Statistical analysis; Vibration; Time series; Autoregressive model; Regression analysis; Experimental study; Vibrometer; Stationary condition; Vibration transducer; Transducer position; Classification; Signal processing; System identification; Localization; Defect detection; Damaging
• ISSN: 0022-460X; 1095-8568
• DOI: 10.1016/j.jsv.2005.06.016

In this paper, a time series algorithm is presented for damage identification and localization. The vibration signals obtained from sensors are modeled as autoregressive moving average (ARMA) time series. A new damage-sensitive feature, DSF, is defined as a function of the first three auto regressive (AR) components. It is found that the mean values of the DSF for the damaged and undamaged signals are different. Thus, a hypothesis test involving the t-test is used to obtain a damage decision. Two damage localization indices LI 1 and LI 2, are introduced based on the AR coefficients. At the sensor locations where damage is introduced, the values of LI 1 and LI 2 appear to increase from their values obtained at the undamaged baseline state. The damage detection and localization algorithms are valid for stationary signals obtained from linear systems. To test the efficacy of the damage detection and localization methodologies, the algorithm has been tested on the analytical and experimental results of the ASCE benchmark structure. In contrast to prior pattern classification and statistical signal processing algorithms that have been able to identify primarily severe damage and have not been able to localize the damage effectively, the proposed algorithm is able to identify and localize minor to severe damage as defined for the benchmark structure.