## 1 Introduction
The crack location detection was realized by transforming the basic vibration mode into the wavelet space. Experimental research is the most direct verification method for fault detection, and it is also an important step before the fault detection algorithm is applied to the actual industrial field. In 2000, [PERSON] et al. [12] used the fault detection method based on a numerical model to diagnose the crack location and depth and verified it on an experimental platform. In this study, the location information was obtained by the least square method in the frequency domain, and the depth information was obtained by the ratio of equivalent bending moment and static bending moment. In 2005, [PERSON] et al. [13] proposed a crack location detection method based on a numerical model in the frequency domain. The location diagnosis of shallow cracks and deep cracks was carried out on a large experimental platform. In 2006, [PERSON] et al. [14] used a fault detection method based on a numerical model to detect crack existence and location in a rotor system with both rub-impact and crack faults and carried out experimental verification. In 2009, [PERSON] et al. [15] used the empirical mode decomposition (EMD) method to decompose a non-coupled signal and used Laplace wavelet to extract the modal parameters, detected the crack location and depth, and carried out experiments to verify the method. The above literature review, shows that the research algorithms mainly include model-based and signal-based detection algorithms. The application requires a strong theoretical level, different research requires different algorithms, and there is still a certain distance from the actual industrial field application. Although most rotors used in the experiment are single-span rotors, there may be complex structures such as multi-span rotor systems in the actual industrial field. For example, generator rotors and cracks will bring high-order harmonic vibration to the shafting, will easily lead to subcritical resonance peaks and increase the risk of resonance of the system. Therefore, studying the fault detection method of asymmetric shafting with cracks and detecting the location of cracks has great scientific research value and practical application value. In order to overcome the problem that traditional fault diagnosis algorithms rely on artificial experience, some scholars have gradually adopted a deep learning algorithm to study the fault diagnosis method, among which the convolutional neural network (CNN) has received the most attention. In 2016, [PERSON] et al. [16] first applied CNN to gearbox fault classification. Compared with the traditional artificial feature extraction machine learning method, the accuracy rate increased by about 6%. [PERSON] et al. [17,18] studied bearing fault transfer learning under different working conditions based on a convolution neural network and a transfer learning method, which have better adaptability than traditional methods. [PERSON] et al. [19] realized rotor fault classification based on a convolution neural network and multi-source data. In the fault diagnosis experiment of a rotor system, the accuracy rate reached 98.97%. [PERSON] et al. [20] adopted a domain adaptive method of joint adaptive distribution to study the problem of fault transfer diagnosis on the three data sets of fan, bearing and gearbox and verified the applicability of the proposed method. [PERSON] et al. [21] adopted a multi-scale domain adaptive method to study the transfer of bearing experimental fault data to real bearing fault data for diagnosis, which achieved higher accuracy than the traditional method. [PERSON] et al. [22] proposed a new transfer diagnosis method combining sparse autoencoder and transfer learning methods and tested it on a bearing data set. Compared with the traditional algorithm, the accuracy reached 99.82%. [PERSON] et al. [23] proposed a new transfer diagnosis method by combining a residual network and local adaptation and studied the domain adaptation between different bearing fault data sets.