# FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization
## 1 Introduction  
The complexity of machinery has been increasing over the past few years, and the traditional methods of mechanical fault detection cannot keep up with the level of automation of machinery. This paper focuses on exploring the mechanical faults in rotating machinery, and bearing failure detection is the primary focus of research on real-time rotating machinery detection. Deep learning is undoubtedly the most widely used method for data-driven fault diagnosis. Deep neural network algorithms have achieved remarkable success in a variety of fields since the theory of deep learning was established [1]. Although it is typically assumed that the training set and the testing set share the same fault pattern space while training neural networks, this simply means that the feature data used for training contains all potential operating conditions. Despite our greatest efforts in dataset collecting, there are still undiscovered faults in actual industrial manufacturing. Models trained on existing operating conditions may not necessarily perform well in diagnosing faults in different operating conditions [2]. To address the issue of real-world operating conditions that may not be represented in the training set, this study incorporates domain generalization into the fault diagnosis model and improves on previously used adversarial domain generalization methods. In genuine industrial manufacturing, fault diagnosis is a major challenge. After more than 60 years of development, there are many mature methods for mechanical fault diagnosis thanks to the establishment of numerous international research facilities in the 1960s and beyond. Domain generalization has become one of the current research hotspots in the field of deep learning in order to enhance the generalization capability of fault diagnosis models on datasets with unknown working conditions. [PERSON] et al. used traditional methods such as the seagull optimization algorithm (SOA) to determine the system parameters [3] and input the signal into their proposed first-order multistable stochastic resonance system (CMSR) and then used Fourier transform on the output and obtained the fault diagnosis results. Video methods mainly include wavelet transform [4; 5], short-time Fourier transform [6; 7], and other methods. Based on the Case Western Reserve bearing data set with Gaussian noise, [PERSON] et al. organized its data points into grayscale images [8] and then used wavelet transform to process and input them into the neural network with Average Row Pooling (ARP) to obtain classification information and achieved a good anti-noise effect. Traditional fault diagnosis methods require not only manual feature extraction but also specific professional knowledge, so the methods are not adaptable and robust. Machine-learning algorithms can automatically generate method models through iteration based on selected features, which greatly reduces labor costs. For example, Support Vector Machine (SVM) is a supervised learning method [9]. In classification, the dimension of the classification vector does not affect the performance of the model classification, which makes the support vector machine better at generalization in the domain of fault diagnosis, so SVM is one of the important tools for fault diagnosis. [PERSON] et al. input the vibration fault features extracted by the envelope spectrum into the support vector machine to classify the health data and fault data [10] and then perform fault diagnosis on the fault data to determine the fault mode. [PERSON] and [PERSON] used various machine-learning techniques (like SVM, K-nearest neighbors (k-NN), ML perceptron (MLP), Random Forest (RF), Decision Tree (DT), etc.) to implement a fault detection strategy in the designed induction motors under variable load conditions [11]. [PERSON] et al. present a case study of a machine-learning (ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive [12].