An end-to-end quantitative identification method for mining wire rope damage based on time series classification and deep learning

Abstract

Mining wire rope (MWR) is an important part of mine hoisting equipment and plays a key role in mining operations. Damage to these ropes can significantly reduce production efficiency and pose serious safety risks to workers. Therefore, quantitatively identifying damage in MWR is of great importance. Traditional methods for damage signal identification rely on manual feature extraction (MFE), which depends heavily on experience and lacks stability and flexibility. This paper proposes an end-to-end (E2E) quantitative identification model for MWR damage based on time series classification (TSC) and deep learning (DL). Unlike traditional methods, the E2E model learns features directly from the one-dimensional raw signals of MWR damage and does not require MFE. In order to test its validity and versatility, experiments were conducted on three different datasets. The results show that the E2E method performs well in quantitatively identifying MWR damage compared to other methods and this method meets the requirements of the mining industry in terms of precision and efficiency to ensure safe and reliable operation of mining work.