Delamination in composite structures is characterized by a resonant cavity wherein a fraction of an ultrasonic guided wave may be trapped. Based on this wave trapping phenomenon, we propose a baseline-free statistical approach for the identification and localization of delamination using sparse sampling and density-based spatial clustering of applications with noise (DBSCAN) technique. The proposed technique can be deployed for rapid inspection with minimal human intervention. The Performance of the proposed technique in terms of its ability to determine the precise location of such defects is quantified through the probability of detection measurements. The robustness of the proposed technique is tested through extensive simulations consisting of different random locations of defects on flat plate structures with different sizes and orientation as well as different values of signal to noise ratio of the simulated data. The simulation results are also validated using experimental data and the results are found to be in good agreement.

Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves

The development in materials technology has produced stronger, lighter, stiffer, and more durable electrically insulating composites which are replacing metals in many applications. These composites require alternative inspection techniques because the conventional nondestructive testing (NDT) techniques such as thermography, eddy currents, ultrasonic, X-ray and magnetic particles have limitations of inspecting them. Microwave NDT technique employing open-ended rectangular waveguides (OERW) has emerged as a promising approach to detect the defects in both metal and composite materials. Despite its promising results over conventional NDT techniques, OERW microwave NDT technique has shown numerous limitations in terms of poor spatial resolution due to the stand-off distance variations, inspection area irregularities and quantitative estimation in imaging the size of defects. Microwave NDT employing OERW in conjunction with robust artificial intelligence approaches have tremendous potential and viability for evaluating composite structures for the purpose mentioned here. Artificial intelligence techniques with signal processing techniques are highly possible to enhance the efficiency and resolution of microwave NDT technique because the impact of artificial intelligence approaches is proven in various conventional NDT techniques. This paper provides a comprehensive review of NDT techniques as well as the prospect of using artificial intelligence approaches in microwave NDT technique with regards to other conventional NDT techniques.

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Prospect of Using Artificial Intelligence for Microwave Nondestructive Testing Technique: A Review

https://iopscience.iop.org/article/10.1088/1361-665X/ab6ce7/pdf

A parameter estimation based sparse representation approach for mode separation and dispersion compensation of Lamb waves in isotropic plate

Estimation of the Lamb wave phase velocity dispersion curves using only two adjacent signals

Various traffic-sensing technologies have been employed to facilitate traffic control. Due to certain factors, e.g., malfunctioning devices and artificial mistakes, missing values typically occur in the Intelligent Transportation System (ITS) sensing datasets, resulting in a decrease in the data quality. In this study, an integrated imputation algorithm based on fuzzy C-means (FCM) and the genetic algorithm (GA) is proposed to improve the accuracy of the estimated values. The GA is applied to optimize the parameter of the membership degree and the number of cluster centroids in the FCM model. An experimental test of the taxi global positioning system (GPS) data in Manhattan, New York City, is employed to demonstrate the effectiveness of the integrated imputation approach. Three evaluation criteria, the root mean squared error (RMSE), correlation coefficient (R), and relative accuracy (RA), are used to verify the experimental results. Under the ±5% and ±10% thresholds, the average RAs obtained by the integrated imputation method are 0.576 and 0.785, which remain the highest among different methods, indicating that the integrated imputation method outperforms the history imputation method and the conventional FCM method. On the other hand, the clustering imputation performance with the Euclidean distance is better than that with the Manhattan distance. Thus, our proposed integrated imputation method can be employed to estimate the missing values in the daily traffic management.

An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data.

An approach based on expectation-maximization algorithm for parameter estimation of Lamb wave signals

A baseline-free approach of locating defect based on mode conversion and the reciprocity principle of Lamb waves.

A modified composite fatigue damage model considering stiffness evolution for wind turbine blades

A novel method to predict the stiffness evolution of in-service wind turbine blades based on deep learning models

Measured by distributed fiber Bragg grating (FBG) sensors, an application and strain analysis of real-time structural health monitoring system for muti-MW scale wind blades during the service process has been established and investigated. The experiments of the multi-MW scale wind turbine were performed at the top of the hill, near the sea and the FBG sensors were mounted at various locations on the internal surfaces of the rotating blades. The feasibility and effectiveness of the system were validated by continuously transmitting the optical signals between the FBG demodulator and the sensors. The internal dynamic strain of the blades during the environmental fatigue loadings were monitored, valuated and given crash alert with FBG sensors through the results of preliminary field tests. Through Hilbert-Huang transform (HHT), the strain data were decomposed into a series of intrinsic mode functions (IMF) and residual component by the empirical mode decomposition (EMD) method under different frequencies.

Hongwei Liu

Papers

An approach based on expectation-maximization algorithm for parameter estimation of Lamb wave signals

A baseline-free approach of locating defect based on mode conversion and the reciprocity principle of Lamb waves.

A modified composite fatigue damage model considering stiffness evolution for wind turbine blades

A novel method to predict the stiffness evolution of in-service wind turbine blades based on deep learning models

Real-time monitoring system for multi-MW scale wind blades using FBG sensors