Abstract：Ultrasonic guided wave based structural health monitoring technology will be affected by changing environment and operating conditions in practical engineering applications. Because of the limitations of independent component analysis methods for processing a large number of monitoring guided waves, and the lack of research on different degrees of damage characterization, a guided wave monitoring method based on over-determined independent component analysis is proposed and the damage index based on k-means clustering is improved. The temperature, which has a large influence on the monitoring signal and is widely exist, is taken as an environmental variable. The principal component analysis (PCA) is used to analyze the observation matrix composed of a large number of guided wave signals to determine the number of independent components, and independent component analysis (ICA) is used to decompose the processed guided wave signals into independent components. The influence of damage and that of environmental and operational conditions are separated into different independent components. The guided wave monitoring experiment was carried out on aluminum plates subjected to long-term changes in ambient temperature. The results show that the method can effectively reduce the number of independent components and eliminate the interference from the ambient temperature in a large number of guided signals to identify the damage. Furthermore, the damage identification experiment was carried out on the aluminum plate with intact and different damage degree under the temperature change condition. The effect of the method on charactering the damage degree while eliminating the environmental temperature interference was studied.
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