Statistical Wavelet-Based Method for Structural Health Monitoring

Abstract

A statistical pattern classification method based on wavelet packet transform (WPT) is developed in this study for structural health monitoring. The core of this method is the WPT that has the ability of extracting minute abnormality from vibration signals. The vibration signals of a structure excited by a pulse load are first decomposed into wavelet packet components. Signal energies of these wavelet packet components are then calculated and sorted according to their magnitudes. Those components that are small in signal energy are discarded since they are easily contaminated by measurement noise. The remaining dominant component energies are defined as a novel condition index, the wavelet packet signature (WPS). Two damage indicators are then formulated to lump the discriminate information from the extracted WPS. Thresholds for damage alarming are established using the statistical properties and the one-sided confidence limit of the damage indicators from successive measurements. For demonstration, an experimental study on the health monitoring of a steel cantilever I beam is performed. Four damage cases, involving line cuts of different severities in the flanges at one cross section, are introduced. The vibration signals are obtained from an accelerometer installed at the free end of the beam. Results show that the health condition of the beam can be accurately monitored by the proposed method even when the signals are highly contaminated with noise. The proposed method does not require any prior knowledge of the structure being monitored and is very suitable for on-line continuous monitoring of structural health condition.