Evaluation of fatigue cracks on rail material based on laser nonlinear wave modulation and adaptive particle swarm optimization - support vector machines

ABSTRACTThe evaluation of fatigue cracks is essential for the detection of early damage to railway tracks. Laser nonlinear wave modulation spectroscopy using broadband excitation is an effective method for fatigue crack detection that can solve the problem of frequency combination optimisation. However, due to the complex components of laser nonlinear ultrasonic signals, the evaluation of fatigue cracks using laser nonlinear ultrasonic signals remains a challenge. In this study, wavelet packet decomposition and principal component analysis were used to extract various features of nonlinear ultrasonic signals in the frequency domain, and four new combinations of features were obtained. These new features were used as input for the evaluation model. Finally, a fatigue crack evaluation model based on adaptive particle swarm optimisation-support vector machines was proposed to accurately evaluate fatigue cracks of different lengths. The experiments conducted showed that the fourth new combination of features and evaluation model proposed in this paper can improve the accuracy of evaluating rail fatigue cracks of different lengths, reaching a classification accuracy of 97%.KEYWORDS: Rail fatigue cracksnonlinear laser ultrasonic modulationfeature extractionAPSO-SVM Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Natural Science Research of Jiangsu Higher Education Institutions of China (Grants No. 22KJD140004), the National Natural Science Foundation of China (Grant no. 61903193 and AA202108033), China Postdoctoral Science Foundation (Grant no. 2020M671476), Open Fund of Key Laboratory of Nondestructive Testing (Nanchang Hangkong University) (Grant no. EW201980443 and EW201908008), Key Laboratory of Nondestructive of Ministry of Education.