## 1 Introduction  
As one of the important load-bearing tools in industrial production, steel wire ropes have the advantages of high strength and stable and reliable use and have important applications in engineering fields such as coal mines, offshore oil development, and bridge traction [1; 2; 3]. However, due to prolonged exposure to harsh working environments and long-term high-load conditions, steel wire ropes inevitably suffer from various damages such as wire breakage, wear, and corrosion, which emphasizes the need for effective monitoring and maintenance methods. Unfortunately, the timely evaluation of the safety status of in-service wire ropes is hindered by the lack of effective wire rope damage detection methods and rigorous safety assessment techniques [4; 5]. This leads to potential safety hazards going undetected. Hence, there exists an urgent imperative to cultivate a scientifically robust quantitative identification methodology for detecting broken wires. Such an approach would not only yield economic advantages but also harbor substantial societal significance. Therefore, developing a scientifically effective quantitative identification method for broken wires has high economic benefits and social value. At present, the detection technology for steel wire ropes mainly includes destructive testing and non-destructive testing. Destructive testing may cause damage to the steel wire rope due to the need for tensile or fatigue testing, so non-destructive testing technology has been widely applied. The commonly used non-destructive testing methods currently include ultrasonic testing [6], thermal imaging testing [7], infrared testing, and electromagnetic testing [8]. Due to its simple principle and high reliability, the electromagnetic detection method is highly suitable for the detection of damage in steel wire ropes with good magnetic conductivity [9]. In recent years, in order to accurately and quantitatively evaluate the defect information of steel wire ropes, experts and scholars have proposed various signal processing and fault recognition technologies. The initial signal of detected wire breakage from sensors typically involves a significant amount of noise [10]. To distill the distinctive features of the damaged signal, preprocessing of the signal is essential [11]. [PERSON] et al. use an envelope process based on Hilbert transform to reduce noise and improve the resolution of magnetic flux leakage (MFL) signals [12]. [PERSON] et al. use wavelet transform decomposition and reconstruction techniques to denoise the original damage signal [13]. Early quantitative recognition primarily relies on the manual extraction of signal features, which are used to form feature vectors based on extracted characteristics such as peak, width, and peak-to-peak. The feature vectors are then fed into the network for training, ultimately allowing the classification and recognition of broken wires to be enabled. [PERSON] et al., who used a wavelet decomposition and reconstruction algorithm to process magnetic leakage signals, designed an SVM classifier and achieved quantitative identification of broken wires, thereby proving the effectiveness of SVM classification [14]. [PERSON] et al. proposed a time-frequency domain coupling method to address the issues of low strength and weak lifting effect of steel wire ropes and constructed a BP neural network to extract multiple sets of time-domain feature values as inputs for feature recognition [15]. Nevertheless, conventional manual feature extraction methods necessitate a substantial amount of prior expertise to process and analyze the original signal, and the extraction and selection of signal features are subject to limitations. As the advent of deep learning has occurred, artificial intelligence-based deep learning theories have begun to be utilized for the non-destructive detection of broken wire damage in steel wire ropes.