Quantitative Identification of Internal and External Wire Rope Damage Based on VMD-AWT Noise Reduction and PSO-SVM.

Area of Science:

• Materials Science
• Mechanical Engineering
• Non-Destructive Testing

Background:

• Mining wire ropes are critical load-bearing components prone to internal damage, often undetectable by visual inspection.
• Existing technologies struggle with accurate detection of internal wire rope defects, posing significant safety risks.

Purpose of the Study:

• To design and validate a non-destructive testing (NDT) device for effectively detecting internal wire rope damage.
• To develop advanced signal processing techniques for noise reduction and feature extraction in NDT data.
• To implement a robust algorithm for quantitative identification and classification of wire rope defects.

Main Methods:

• A leakage magnetism-based non-destructive testing device was designed and simulated.
• A variational mode decomposition-adaptive wavelet thresholding method was employed for signal noise reduction.
• Multi-dimensional feature vectors, including wavelet energy entropy, were constructed.
• A particle swarm optimization-support vector machine (PSO-SVM) algorithm was utilized for damage classification.

Main Results:

• The developed NDT device demonstrated effectiveness in identifying internal damage defects in wire ropes.
• The proposed noise reduction method significantly improved the signal-to-noise ratio.
• Seven distinct feature vectors were determined, aiding in defect characterization.
• The PSO-SVM algorithm showed superiority in reducing system noise and classifying internal and external defects.

Conclusions:

• The leakage magnetism NDT device offers a viable solution for detecting internal wire rope damage.
• Advanced signal processing and machine learning algorithms enhance the accuracy and reliability of defect detection.
• This technology contributes to improved safety and maintenance strategies for critical mining infrastructure.