Research On Environmental Compensation Technology Of Lamb Wave

With the rapid development of aircraft technology,the safety of aircraft structures is getting more and more attention.Potential damage will occur during the operation of the aircraft structure,but the existing non-destructive testing methods are greatly limited due to technical reasons in practical applications.Structure health monitoring technology is the key technology of security which can monitor the status of the aircraft structure in real time online.And Lamb wave monitoring technology has gained popularity among researchers because its mature and effective.However,the key issue of Lamb wave monitoring technology is that it usually requires the comparison between the data in the current state and the baseline data without damage.But the Lamb wave data in both states is easily affected by environmental factors such as external load or temperature,which leads to a decrease in the accuracy of its damage monitoring.Therefore,the research on the environmental compensation method of Lamb wave monitoring technology is of great significance to the development of structural health monitoring technology.For the two typical applications of Lamb wave technology—large-area structural damage monitoring and hot-spot area damage monitoring,this paper develops corresponding environmental compensation methods,which mainly include:A temperature compensation technique for large-area structural damage monitoring based on a data-driven model is proposed.First,a temperature change data model is established for the structure with no damage,for each excitation-sense path,the amplitude and phase of the Lamb wave signal are obtained through a Hilbert transform,and signals at different temperatures are obtained using Levenberg-Marquardt algorithm.With respect to the estimated parameters of the signal at the reference temperature,the least squares regression model is used to establish the Lamb wave amplitude and phase versus temperature curve for each path.When performing damage monitoring,calculate the amplitude and phase changes of the relative reference temperature for each path of the sensor network in the current state,and use the established regression model to obtain the temperature estimate of each path in the sensor network,assuming that the actual temperature field is uniformly changing.Abnormal determination criteria can be used to determine and reject abnormal temperature for establishing the actual temperature field.The least squares regression model is used to estimate the amplitude and phase of the Lamb wave signal in the actual temperature field by increasing/decreasing the reference temperature.The experiment was carried out by establishing a sensor network on the composite material board.The experimental results show that the method has good compensation accuracy in the range of large temperature range(the errors are all less than-25db).An environmental compensation technique for crack propagation monitoring in hot-spot area based on probability statistical model is proposed.Crack propagation in hot-spot area is a slow process.Monitoring crack propagation requires acquiring Lamb wave data at a large number of points in the process.During the actual service of the aircraft,the environmental parameters such as temperature and load corresponding to each time point have randomness.In this paper,a Gaussian mixture model and a fuzzy C means model are used to establish a statistical model by multiple adjacent time-point data.The statistical model's offset index is used to eliminate the uncertainties caused by random environmental factors to Lamb,and to establish an offset index.The quantitative relationship between crack lengths is used to determine the early warning criteria for cracks.The crack propagation monitoring experiments of the random load state and the random temperature state of the temperature control box in the fatigue test verify the feasibility and effectiveness of the method to eliminate the effects of random load and random temperature when used in the hot spot crack propagation monitoring.