## 1 Introduction  
Steel wire ropes play an important load-bearing role in many mechanical systems [1]. The occurrence of broken wires may lead to unexpected security issues [2]. Regular replacement is a commonly used in engineering to avoid wire rope accidents, as such accidents usually cause huge economic waste [3]. However, the rope is still potentially safety hazard before the replacement period [4, 5], therefore, it is necessary to explore an intelligent diagnosis method to safely maintain the operation of steel wire ropes.  
In recent years, many studies have been carried out to realize the quantitative identification of broken wires, which includes signal processing methods, feature extraction, recognition models, and so on [6, 7, 8, 9]. [PERSON] et al. [6] proposed a denoising method based on ensemble empirical mode decomposition (EEMD) to reduce the noise of the remanence signals caused by the defects. A back propagation (BP) neural network was used for the identification of different broken wires. [PERSON] et al. [7] built a two-step neural network to distinguish internal and surface defects of the wire rope, in which the MFL signals of broken wires are received by both the induction coil and Hall sensor. [PERSON] et al. [8] present a surface detection method for steel wire ropes using deep learning and computer vision techniques. [PERSON] et al. [9] combined signal analysis and a convolutional neural network (CNN) for the identification of wire rope defects.  
However, a large number of fault samples are required for the methods mentioned above to find the relationship between damage information and various defects, which is difficult to implement in practical detection. The testing process must be controlled to minimize machine downtime and ensure orderly production. Therefore, intelligent fault diagnosis methods are urgently demanded to monitor the condition of steel wire ropes in the case of small samples of faults.  
Generative adversarial networks (GANs) have been attracting considerable attention since they were proposed in 2014 [10; 11]. New samples, different from the original data but with similar distribution, can be generated through the GANs, which provides a new method for fault diagnosis under the condition of small samples [12]. GANs have been successfully applied in the domain of machine fault diagnosis [13; 14]. [PERSON] et al. [13] proposed an intelligent fault diagnosis for rotating machinery based on wavelet transform (WT), GANs, and a convolutional neural network (CNN). [PERSON] et al. [14] developed an auxiliary classifier GAN framework for machine fault diagnosis, which was verified through induction motor vibration signal datasets. However, there are few related studies in the field of wire rope fault diagnosis. Hence, in this paper, the GANs are first attempted for the wire rope detection, and the effectiveness of the network is evaluated through commonly broken wire experiments.  
The main contributions of this paper can be summarized as follows:  
1. A novel intelligent detection method is presented by combing GANs and CNN, which can be applied to the quantitative identification of surface and inner broken wires of wire ropes in the case of small samples.
2. By using the GANs for the expansion of the fault data set to achieve higher accuracy of quantitative detection, which can adapt to the actual complex conditions of broken wire inspection.  
The remaining part of the paper is organized as follows. The theory of GANs is introduced in Section 2. In Section 3, the detailed description of the whole framework based on GANs is illustrated. Section 4 presents the experimental results of the proposed method. Finally, the conclusions and discussion are drawn in Section 5.