Eddy Current Detection of Broken Steel Wires in the Core of the Conveyor Belt Based on CutMix-ReliefF-RF

Abstract

Broken steel wires in the core of the conveyor belt significantly reduce its carrying capacity and overall strength, potentially leading to safety hazards. The eddy current testing (ECT) has significant advantages in terms of cost and portability over other nondestructive testing technologies such as ultrasonic testing (UT) and X-ray. ECT detects changes in the magnetic field, enabling the detection of the broken steel wires. However, the detection of broken wire ropes in conveyor belts has difficulty in obtaining data under actual working conditions, insufficient data diversity, low recognition accuracy, and poor model generalization ability. This article designs a differential probe and introduces a novel approach, the CutMix-ReliefF-RF method, which addresses these challenges based on the differential signal. By applying CutMix, we enhance the diversity of small sample data and construct varied datasets representing different numbers of broken wires, which significantly improves the model’s recognition ability and generalization performance. The fault features of the broken steel wires in the core of the conveyor belt are extracted, and then the ReliefF method is used for dimensionality reduction to obtain an optimal feature subset. The random forest (RF) algorithm is adopted to identify the broken core characteristics from the eddy current (EC) detection signal. The accuracy of the finite element model (FEM) is validated through experimental and simulation signals used to generate training samples. The proposed method can accurately identify broken steel wires in the core of the conveyor belt, achieving a fault diagnosis accuracy of 98.23% under simulation signals and 96.67% under experimental signals, respectively. This provides strong support for the health monitoring of broken steel wires in the core of the conveyor belt.