Damage identification for mining wire rope based on continuous wavelet transform and convolutional neural network

As a vital component of mining hoisting equipment, mining wire rope (MWR) is critical in the operation of the mine. Once damaged, it is very easy to cause the loss of life and property. Therefore, it is meaningful to identify the damage to MWR, especially the weak damage identification. This paper proposes a method based on continuous wavelet transform (CWT) and convolutional neural network (CNN) model for MWR damage degree identification. Firstly, the MWR signals with different damage degrees were acquired. Secondly, CWT and data augmentation were performed on the original signals to obtain a time-frequency image dataset of MWR damage degree. Then, a deep learning (DL) model is built for identification and compared with traditional machine learning and some classical CNN models. The results show that the model proposed in this paper is better than the traditional machine learning models. It has the highest accuracy of 91.58% in MWR damage degree identification with classical CNN models. The method focuses on the weak damage of MWR and combines CWT with CNN model to meet the requirements of accuracy and efficiency, detecting the early damage as soon as possible and ensuring intelligent, safe and stable operation in the mining industry.