## 1 Introduction  
Fault diagnosis is one of the significant tasks covered by PHM (Prognostics and Health Management). It includes the process of monitoring mechanical equipment to determine the current health state and predict when and what failures occur. It enables decision-making for the maintenance and efficient management of the production process. Various techniques have been developed to increase the reliability of fault diagnosis. Among them, fault diagnosis methods that use machine learning are primarily used. Intelligent fault diagnosis refers to the use of machine learning to detect and diagnose faults, and to manage health information in mechanical equipment [1]. Performing intelligent fault diagnosis effectively is one of the foundations for smart manufacturing [2, 3]. Traditional fault diagnosis using machine learning consists of three stages: data acquisition, artificial feature extraction and selection, and fault classification. In the data acquisition step, sensor signal data are collected following the attachment of various sensors to mechanical equipment to diagnose problems. In general, signals such as acoustic, vibration, current, and thermal are collected for fault diagnosis. Each signal has advantages and disadvantages and is chosen to be appropriate for the subject being diagnosed. Acoustic emission data can detect incipient and hidden faults, and are used to diagnose bearings [4], gears [5], and induction motors [6]. It is particularly useful for low speed operation conditions or low-frequency noise environments. The thermal signal is collected in several forms, and there is a possibility that it can be applied noninvasively without contacting the driving part, such as a thermal image. These signals are difficult to process and are used for fault diagnosis in induction motors [7,8] and electric impact drills [9]. In the case of electric current signal, it can be easily collected from the current transformer and has a great advantage in diagnosing electric-driven machines [10,11]. Among these signals, the vibration signal is a useful and effective tool widely used for fault diagnosis of various kinds of machines. In order to remove the influence of external interference and noise in the operating environments and to be used as a low noise level, various signal processing is performed [12,13]. It is used to diagnose mechanical and electrical failures of various equipment such as bearings [14], gearboxes [15], commutator motors [16]. In the next step, various features, including the time domain, frequency domain, and time-frequency domain, are artificially extracted from the collected signals through signal processing methods. Then, specific feature selection methods are used to select only important features with information about health states. Subsequently, health states, such as failure, are predicted using the conventional machine learning. A number of machine learning techniques were used for fault diagnosis such as artificial neural network (ANN) [17], support vector machine (SVM) [18], and classification and regression trees (CART) ensemble [19]. Traditional fault diagnosis has the following problems. First, the feature extraction and selection processes are passive and inefficient. Feature extraction relies on the choice of methods based on experience or knowledge. Each handcrafted feature tends to be suitable only for fault diagnosis under certain circumstances, requiring the operator's judgment in many situations. Additionally, a specialized extraction method should be used according to the shape and characteristics of the sensor signal. As the type of collected sensor signal diversifies and the volume increases, it becomes impossible. As a result, the process reliability reduces, and traditional techniques become inappropriate for the fault diagnosis task. Second, these problems make it difficult to generalize and automate the fault diagnosis procedure.