A multi-source information fusion approach for crack detection of 42CrMo steel during fatigue test

Different number of broken wires produce different grooves on the surface of steel wire rope. Based on the local structural features of these grooves, a new broken wire identification method is proposed. By comparing the processing effects of various image enhancement methods, a processing method called adaptive histogram equalization is selected to process the broken wire image. Aiming at a large amount of useless information in structural features extracted by HOG algorithm, a encoder-decoder neural network is designed to reduce the dimension of features. In addition, to effectively avoid information loss caused by the output layer of the BP neural network, a joint algorithm of the BP neural network and the support vector machine is proposed. The experimental results show that using image enhancement technology to process broken wire images can effectively improve the recognition rate of broken wires; The structural features extracted by HOG algorithm are more beneficial to the quantitative recognition of broken wires than the texture features extracted by LBP operator; Compared with various dimensionality reduction methods, neural network can retain more effective information; The joint algorithm can improve the recognition rate of broken wire by at least 0.25% on the basis of BP neural network.

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A multi-source information fusion approach for crack detection of 42CrMo steel during fatigue test

Cited by 3 publications

References 9 publications

Wire Rope Damage Detection Based on Magnetic Leakage and Visible Light

Wire Rope Damage Detection Based on Magnetic Leakage and Visible Light

Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Multi-Image Fusion

Quantitative detection of wire rope damage based on local structural characteristics

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