Author:
Hong Li,Cai Jianxian,Wu Yanxiong,Yao Zhenjing,Qiu Zhongchao,Teng Yuntian
Abstract
A modelling method integrating principal component analysis (PCA) and the least-squares support vector machine with particle swarm optimisation (PSO-LSSVM) is proposed to address the difficulties in quantitative identification of fatigue cracks. The widths and depths of fatigue cracks
are quantitatively identified by establishing a non-linear mapping relationship between these dimensions and magnetic flux leakage (MFL) signals. A series of fatigue crack samples are prepared through the fatigue tensile test for the MFL detection system. A sample library is established through
MFL experimentation to verify the feasibility of the method for quantitative identification of fatigue cracks based on PSO-LSSVM. The results indicate that the method is in a position to effectively and quantitatively identify the widths and depths of fatigue cracks less than 1 mm in size
with a maximum error of approximately 0.3 mm.
Publisher
British Institute of Non-Destructive Testing (BINDT)
Subject
Materials Chemistry,Metals and Alloys,Mechanical Engineering,Mechanics of Materials
Cited by
5 articles.
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