Toggle Main Menu Toggle Search

Open Access padlockePrints

Fast classification for rail defect depths using a hybrid intelligent method

Lookup NU author(s): Professor Gui Yun TianORCiD, Qiuji Yi



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


© 2018 Elsevier GmbH In view of the fact that the traditional laser ultrasonic imaging test takes a long time and cannot achieve large area scanning of rail. This paper explores the possibility of combing the laser-ultrasonic technology and a hybrid intelligent method to fast achieve classification and evaluation of artificial rolling contact fatigue (RCF) defect in different depths. The laser ultrasonic scanning detection system is used to collect data samples from different locations of the defects quickly, and the signals are detected by an interferometer. Once the characteristic information of different rail defects is acquired and trained by Support Vector Machine (SVM), the high efficient and high-precision rail detection can be realized through the input of the feature in the detection process. The hybrid method is composed by Wavelet Packet Transform (WPT), Kernel Principal Component Analysis (KPCA) and SVM. The WPT is used to decompose the signal of surface defect in different frequency bands. The KPCA is used to eliminate the redundancy of the original feature set, thereby reducing the correlation among all the defect features. Wavelet packet time-frequency coefficient (X), energy (E) and local entropy (F) are generated and a new feature (Ynew) is created by fusing X, E and F, as a result of WPT and KPCA. Finally, a support vector machine (SVM) method is used to classify RCF defect in different depths. It implements a fast classification of small data. Compared with single features, fusion feature has the highest accuracy rate up to 98.73%.

Publication metadata

Author(s): Jiang Y, Wang H, Tian G, Yi Q, Zhao J, Zhen K

Publication type: Article

Publication status: Published

Journal: Optik

Year: 2019

Volume: 180

Pages: 455-468

Print publication date: 01/02/2019

Online publication date: 20/11/2018

Acceptance date: 15/11/2018

Date deposited: 25/01/2019

ISSN (print): 0030-4026

ISSN (electronic): 1618-1336

Publisher: Elsevier GmbH


DOI: 10.1016/j.ijleo.2018.11.053


Altmetrics provided by Altmetric