Abstract
Abstract To address the challenges of detecting internal damage in steel wire rope core conveyors and the difficulties in quantitative identification, this study proposes an improved backpropagation (BP) neural network damage identification algorithm based on the Grey Wolf Optimization (GWO-BP). The Grey Wolf algorithm is employed to optimize the initial weights and thresholds of the BP neural network, thereby enhancing its performance and stability. A testing apparatus for detecting damage in steel wire rope core conveyors is designed and constructed to evaluate the algorithm's effectiveness and feasibility. First, damage signal data from the steel wire rope are extracted and normalized to facilitate the convergence of the predictive model. Next, the BP neural network algorithm is optimized to address issues such as parameter selection randomness, improving model training speed and identification accuracy. Experimental results indicate that the optimized BP algorithm achieves an average identification accuracy of 96.0%, representing a 5.5% improvement over the unoptimized BP algorithm and significantly enhancing the precision of damage quantitative identification.
Published Version
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