# A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning
## 1 Introduction
proposed a new framework based on small-labeled infrared thermal images and an enhanced CNN for monitoring the vibration of a rotor-bearing system fault diagnosis, which was demonstrated to be superior to the mainstream methods used to date [20]. Some studies also focused on transfer learning (TL) methods to minimize the discrepancy between two datasets of working conditions, which aimed to avoid long time-consuming training and insufficiently labeled data. [PERSON] et al. designed a novel fault diagnosis network that was constructed by a deformable CNN, deep long short-term memory, and dense layers based on transfer learning strategies, and their cross-domain experiments demonstrated its effectiveness in identifying the fault types of bearings in new conditions [21]. [PERSON] et al. proposed a transfer diagnosis framework based on an improved domain adaptation algorithm, and their related comparison of the experimental results demonstrated the applicability and practicability of the proposed method compared with other existing state-of-the-art (SOTA) algorithms [22]. Some studies also used machine learning methods to detect wire rope defects, such as neural networks, support vector machine (SVM) [23], and multi-channel signal fusion, to improve the performance of defect detection activities under conditions of vigorous movement and strand noise [24]. Undoubtedly, the mentioned studies yielded positive effects from different aspects; however, for cross-domain working conditions and those with noise interference, further improvements and increased robustness can be achieved in wire rope defect diagnosis. Aiming to improve wire rope defect diagnosis performance under diverse working conditions from LMA signals, a new CNN-transformer network and TL architecture are proposed in this paper to improve the defect detection accuracy. The main contributions and novelties of this paper are the following:  
(1) A data preprocessing method based on empirical mode decomposition (EMD) is presented to eliminate the adverse interference of various noises. Two-dimensional gray images are processed through matrix reconstruction and data augmentation methods for the preparation of a wire rope training dataset. (2) Through the combination of CNN and a transformer, a novel wire rope defect diagnosis network is proposed and named DCNNT, where the defect information can be effectively extracted, computational complexities are reduced, and forward efficiency is improved. (3) Unlike the existing approaches in the literature, this paper focuses on solving the issues of domain adaptation and wire rope defect data that are insufficient. A new TL architecture is established based on TL techniques and the proposed DCNNT. (4) The effectiveness of the proposed DCNNT and domain adaptation ability of the TL architecture are verified through different comparison experiments with several general and SOTA methods, where the proposed model can balance detection accuracy, forward latency, and network parameters. This paper is organized as follows: The related study is reviewed in Section 2. Section 3 is the methodology of the proposed methods, including original data preprocessing based on EMD, feature image processing based on matrix reconstruction, TL architecture establishment, and DCNNT network structure. In Section 4, case studies are conducted through comparison experiments and visualization analysis. Conclusions are presented in Section 5.