Nondestructive Evaluation of Aging Failure in Insulation Coatings by Ultrasonic Guided Wave Based on Signal Processing and Machine Learning

Abstract

In the field of nondestructive evaluation (NDE) using ultrasonic guided waves, accurately assessing the aging failure of insulation coatings remains a challenging and prominent research topic. While the application of ultrasonic guided waves in material testing has been extensively explored in the existing literature, there is still a significant gap in quantitatively evaluating the aging failure of insulation coatings. This study innovatively proposes an NDE method for assessing insulation coating aging failure by integrating signal processing and machine learning technologies, thereby effectively addressing both theoretical and practical gaps in this domain. The proposed method not only enhances the accuracy of detecting insulation coating aging failure but also introduces new approaches to non-destructive testing technology in related fields. To achieve this, an accelerated aging experiment was conducted to construct a cable database encompassing various degrees of damage. The effects of aging time, temperature, mechanical stress, and preset defects on coating degradation were systematically investigated. Experimental results indicate that aging time exhibits a three-stage nonlinear evolution pattern, with 50 days marking the critical inflection point for damage accumulation. Temperature significantly influences coating damage, with 130 °C identified as the critical threshold for performance mutation. Aging at 160 °C for 100 days conforms to the time-temperature superposition principle. Additionally, mechanical stress concentration accelerates coating failure when the bending angle is ≥90°. Among preset defects, cut defects were most destructive, increasing crack density by 5.8 times compared to defect-free samples and reducing cable life to 40% of its original value. This study employs Hilbert–Huang Transform (HHT) for noise reduction in ultrasonic guided wave signals. Compared to Fast Fourier Transform (FFT), HHT demonstrates superior performance in feature extraction from ultrasonic guided wave signals. By combining HHT with machine learning techniques, we developed a hybrid prediction model—HHT-LightGBM-PSO-SVM. The model achieved prediction accuracies of 94.05% on the training set and 88.36% on the test set, significantly outperforming models constructed with unclassified data. The LightGBM classification model exhibited the highest classification accuracy and AUC value (0.94), highlighting its effectiveness in predicting coating aging damage. This research not only improves the accuracy of detecting insulation coating aging failure but also provides a novel technical means for aviation cable health monitoring. Furthermore, it offers theoretical support and practical references for nondestructive testing and life prediction of complex systems. Future studies will focus on optimizing model parameters, incorporating additional environmental factors such as humidity and vibration to enhance prediction accuracy, and exploring lightweight algorithms for real-time monitoring.