Wavelet-fuzzy logic approach to structural health monitoring

In this work a novel wavelet-fuzzy logic approach to structural health monitoring is proposed based on wavelet transform theory and fuzzy logic technology. The proposed method combines the effectiveness of the Wavelet Packet Transform (WPT) as a tool for feature extraction and the capabilities of fuzzy sets to model vagueness and uncertainty. Two stages of operation are considered: pattern training and health monitoring. Pattern training is concerned with the determination of fuzzy sets based baseline patterns representing health condition states for which training data are available. Health monitoring is concerned with the classification of new data into the different structural health states. This classification problem is solved based on determining degrees of membership values to each one of the previously defined fuzzy patterns. In order to demonstrate the effectiveness and viability of the proposed approach, the method was applied to data collected from an experiment involving repeatedly impact excitations of an aluminum cantilever beam. Different damage cases in the beam where emulated by adding a lumped mass at different locations. The measured vibration response data provided by six accelerometers were analyzed. Results show that the method is effective in classifying the different damage cases.