# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 1 Introduction  
With the rise of global industrialization and the widespread use of machinery, fault data have become increasingly dense and unrelated. The goal of fault diagnosis technology for instruments and equipment is to monitor the operational state of machines and determine their overall or partial functionality. By employing data mining and machine learning techniques, faults and their underlying causes can be identified, and future fault developments can be predicted. The technical characteristics of fault type identification are as follows: reducing the impact of environmental noise or abnormal data on original data, extracting reliable waveform feature criteria, and selecting or improving existing machine learning methods. The work of [1; 2; 3] has received long-term attention in the industry [4]. As a key component of machinery, bearings can fix objects and provide loads [5]. If the bearing fails, it will lead to a decline in the quality of the product and cause serious economic losses [6]. So bearing fault detection is the focus and difficulty of current research [7]. [PERSON] et al. summarizerecently published work on the application of artificial intelligence to bearing fault detection in rotating machinery [8; 9]. Scholars have proposed many intelligent fault detection methods for the pulse vibration signals generated by bearings. Among these methods, the bearing detection method of deep learning is used the most. Compared with the traditional bearing detection method, deep learning can accurately identify fault information in the context of big data, and reduces the requirement for professional knowledge [10]. [PERSON] et al. improved the feature extraction method of the convolutional neural network and performed fault diagnosis on bearings. Although the above methods have achieved great success, there is a strong dependence on the fault data, and more labels are required when training the model. Such research is contrary to the real industrial environment: in the real industrial environment, there are far more normal data than fault data. Such a dataset will greatly reduce the training effect of the deep learning model. Ref. [11] has attracted widespread attention in the academic community. In order to solve the above-mentioned problems in the industry, Ref. [12] [PERSON] et al. used traditional machine learning, an artificial neural network, deep learning and transfer learning to carry out a typical fault diagnosis method of a wind power system based on the ML method from the theoretical basis and industrial application. An overview is given and its advantages and disadvantages are discussed [13]. [PERSON] et al. worked on improving the model structure and classification loss function, proposing to optimize support vector machines with dynamic penalty factors [14]. [PERSON] et al. used unsupervised machine learning and the DBSCAN algorithm to detect defects on rail fixtures [15]. [PERSON] et al. diagnosed defects using supervised machine learning. The learning data are acquired by sensors, and six machine learning models are used. The result values are best when using the the AdaBoost model [16]. [PERSON] et al. found unexpected test accuracy in cross-validation for machine learning [17]. [PERSON] et al. proposed a new machine learning model for automatic labeling, feature extraction, model building, model fusion, and evaluation of railway data [18]. [PERSON] et al. described a stationary wavelet transform and used it in astronomy as well as veterinary anatomy [19]. [PERSON] et al. used wavelet transform to compare acoustic signals and vibration signals, and the results showed that acoustic signals have a strong influence on early fault detection [20]. [PERSON] et al. used an optimized stationary wavelet packet transform to provide identification for detecting IM bearing faults at an early stage and verified the effectiveness of the method experimentally [21]. [PERSON] et al.