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Recognition of broken wire rope based on adaptive parameterless empirical wavelet transform and random forest

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Magnetic flux leakage (MFL) detection is one of the most effective methods for non-destructive testing (NDT) of wire ropes. In view of the disadvantages of a magnetic flux leakage signal having a low signal-to-noise ratio (SNR) and the poor performance of the filtering algorithm, the use of an adaptive parameterless empirical wavelet transform (APEWT) and wavelet filtering algorithm is proposed to denoise the leakage magnetic field signal collected on the surface of the wire rope. The improved local modulus maxima method is used to locate and segment the magnetic field image of broken wires and the random forest (RF) classifier is designed to realise the quantitative identification of broken wires. Five texture features and four invariant moments totalled nine features of the magnetic flux leakage image that were trained as the input of the RF classifier and good results were obtained. Compared with the most commonly used backpropagation (BP) neural network, the feasibility and effectiveness of the RF classifier in the identification of broken wires of steel wire rope is demonstrated. The experimental results show that the filtering algorithm can effectively remove noise and the RF classifier that was designed has good recognition of this type of defect.
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Document Type: Research Article

Publication date: July 1, 2019

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