Abstract
Magnetic flux leakage (MFL) detection is one of the most widely used and best performing wire rope nondestructive testing (NDT) methods for more than a decade. However, the traditional MFL detection has the disadvantages of single source of information, low precision, easy to miss detection, and false detection. To solve these problems, we propose a method of fusion recognition of magnetic image features and infrared image features. A denoising algorithm based on Hilbert vibration decomposition (HVD) and wavelet transform is proposed to denoise the MFL signal, and the modulus maxima method is used to locate and segment the defect. An infrared image acquisition system was designed to collect the infrared image of the surface of the wire rope. Digital image processing techniques are used to segment infrared defect images. The features of the MFL image and the infrared image are extracted separately for fusion. The fusion feature is input into the nearest neighbor (NN) algorithm for quantitative identification, and the same data are input into the backpropagation (BP) neural network for comparison verification. The experimental results show that the fusion of MFL features and infrared features effectively improves the recognition rate of wire rope defects and reduces the recognition error.
Highlights
Wire ropes play an indispensable role in industrial production, commercial services, and high-tech industries in the modern world. e safety of the wire rope during these production processes is very important because it is usually related to the safety of life and property
Xiucheng et al [15] designed a circular sensor array based on a tunnel magnetoresistive (TMR) sensor to detect slight defects on the surface of the wire rope and judge the defect position based on the Magnetic flux leakage (MFL) information
In order to solve the problem that the wire rope defect information source is single and the recognition accuracy is not high, this paper proposes a method of fusion recognition of magnetic image features and infrared image features
Summary
Wire ropes play an indispensable role in industrial production, commercial services, and high-tech industries in the modern world. e safety of the wire rope during these production processes is very important because it is usually related to the safety of life and property. Pham et al [14] designed a MFL testing device based on a planar Hall magnetoresistive sensor, which is used to detect shallow defects on the lower surface of oil and gas pipelines due to its high sensitivity and low thermal drift. Xiucheng et al [15] designed a circular sensor array based on a tunnel magnetoresistive (TMR) sensor to detect slight defects on the surface of the wire rope and judge the defect position based on the MFL information It only verifies the effect of the TMR sensor, and there is no signal for accurate identification. In order to solve the shortcomings of traditional MFL detection information, such as single source, low precision, and easy to be interfered, we propose a fusion recognition method of magnetic image features and infrared image features. MFL signal effectively improves the recognition rate of wire rope defects and reduces the recognition error
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