On-Line Structural Damage Feature Extraction Based on Autoregressive Statistical Pattern of Time Series

The main aim of this paper is to demonstrate an autoregressive statistical pattern analysis method for the on-line structural health monitoring based on the damage feature extraction. The strain signals obtained from sensors are modeled as autoregressive moving average (ARMA) time series to extract the damage sensitive features (DSF) to monitor the variations of the selected features. One algebra combination of the first three AR coefficients is defined as damage sensitive feature. Using simple theory of polynomial roots, the relationship between the first three AR coefficient and the roots of the characteristic equation of the transfer function is deduced. Structural damage detection is conducted by comparing the DSF values of the inspected structure. The corresponding damage identification experiment was investigated in X12CrMoWVNbN steel commonly used for rotor of steam turbine in power plants. The feasibility and validity of the proposed method are shown.