A Self-Supervised Contrastive Learning Framework Based on Multi-Scale Convolution and Informer for Quantitative Identification of Mining Wire Rope Damage

As a critical part of hoisting equipment in mines, the damage of mining wire rope (MWR) directly poses a threat to production safety and economic efficiency within the coal industry. Therefore, developing efficient and reliable methods for MWR damage identification is of great significance. However, acquiring substantial labeled damage data in real industrial scenarios is often impractical, and accurate damage identification with limited labeled data remains an urgent challenge. To address this, this paper proposes a self-supervised contrastive learning method based on multi-scale convolution and Informer (MS-Informer) for the quantitative damage identification of MWR. This method first generates positive and negative sample pairs through a combined data augmentation strategy to increase data diversity and enhance feature generalization. Subsequently, multi-scale convolution is combined with Informer to simultaneously capture local details and global features. Finally, limited labeled data are incorporated during the fine-tuning phase to accurately identify MWR damage. The experimental results on multiple MWR damage datasets demonstrate that the proposed method achieves superior identification performance under conditions of limited labeled data. This framework not only enhances the accuracy and reliability of MWR damage detection but also provides crucial guidance for the safe operation and subsequent maintenance of mine hoisting equipment.