Detection of Internal Wire Broken in Mining Wire Ropes Based on WOA–VMD and PSO–LSSVM Algorithms

• Published in: Axioms Vol. 12; no. 10; p. 995
• Main Authors: Li, Pengbo, Tian, Jie, Zhou, Zeyang, Wang, Wei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Accuracy; Algorithms; Breakage; Damage detection; Entropy; Fault diagnosis; Fault location (Engineering); Identification; Identification methods; internal damage of wire rope; Magnetic fields; Mechanical properties; Methods; Neural networks; Noise reduction; Parameters; Particle swarm optimization; Permeability; PSO–LSSVM axiom; Signal processing; Signal to noise ratio; Support vector machines; Wavelet transforms; Wire rope; WOA–VMD axiom; China
• ISSN: 2075-1680; 2075-1680
• DOI: 10.3390/axioms12100995

To quantitatively identify internal wire breakage damage in mining wire ropes, a wire rope internal wire breakage signal identification method is proposed. First, the whale optimization algorithm is used to find the optimal value of the variational mode decomposition parameter [K,α] to obtain the optimal combination of the parameters, which reduces the signal noise with a signal-to-noise ratio of 29.29 dB. Second, the minimum envelope entropy of the noise reduction signal is extracted and combined with the time-domain features (maximum and minimum) and frequency-domain features (frequency–amplitude average, average frequency, average power) to form a fusion feature set. Finally, we use a particle swarm optimization–least squares support vector machine model to identify the internal wire breakage of wire ropes. The experimental results show that the method can effectively identify the internal wire rope breakage damage, and the average recognition rate is as high as 99.32%, so the algorithm can greatly reduce the system noise and effectively identify the internal damage signal of the wire rope, which is superior to a certain extent.