Study of Noise Reduction and Identification of Internal Damage Signals in Wire Ropes

Abstract

Mining wire rope, a frequently used load-bearing element, suffers various forms of damage over extended periods of operation. Damage occurring within the wire rope, which is not visible to the naked eye and is difficult to detect accurately with current technology, is of particular concern. Consequently, the identification of internal damage assumes paramount importance in ensuring mine safety. This study proposes a wire rope internal damage noise reduction and identification method, first of all, through a three-dimensional magnetic dipole model to achieve the detection and analysis of the internal damage of the wire rope. Simultaneously, a sensor system capable of accurately detecting the internal damage of wire rope is developed and validated through experimentation. A novel approach is proposed to address the noise reduction issue in the design process. This approach utilizes a particle swarm optimization variational modal decomposition method to enhance the signal-to-noise ratio. Additionally, a dual-attention mode, which combines channel attention and spatial attention, is integrated into the CNN-GRU network model. This network model is specifically designed for the detection of internal damage in steel wire ropes. The proposed method successfully achieves quantitative identification of internal damage in steel wire ropes. The experimental findings demonstrate that this approach is capable of efficiently detecting internal damage in wire rope and possesses the capacity to quantitatively identify such damage, enabling adaptive identification of wire rope.