Considering the strain characteristics of the composite structures changing with fatigue loading, so that this paper attempts to monitor the strain characteristics in real time during the fatigue cycle and assesses the fatigue life through the strain signals. However, from the published literatures, resistance strain gauge often suffered from early fatigue failure in the long-time dynamic testing, which is not suitable for strain signal acquisition during the full fatigue cycle. Therefore, the novel polyvinylidene fluoride piezoelectric film (PVDF) with high fatigue resistance is used to acquire fatigue characteristic signals of composite structures. The piezoelectric signals during the fatigue process of the composite laminates are obtained by pasting PVDF on the surface of carbon fiber reinforced plastic (CFRP) plates (T700/9A16). Based on the piezoelectric effect, the strain information during the fatigue process is converted into the piezoelectric signal from PVDF. Then, a Random Forest Regression (RFR) algorithm is trained based on the database generated from the experimental tests to efficiently establish the correlation between the piezoelectric signals and fatigue cycles of composite laminates. Through the trained RFR network, the actual fatigue cycles of the test pieces can be accurately predicted based on the piezoelectric signals, in which the maximum percentage error of logarithms of fatigue cycles is controlled within 5%. This paper provides a new research idea and technical support for the fatigue life assessment of composite materials.
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