## 1 Introduction  
Industry 4.0 has transformed the company environment, from a wide variety of sensors and control systems, to the development of new maintenance strategies. One of the most popular maintenance strategies is based on decision making, which seeks to optimize performance times by detecting, replacing or repairing machine components before severe and costly problems [1]. By way of illustration, the main components of rotating machines are the bearings, and most failures in industrial machinery occur due to their malfunction. So, if the quality of the bearings is guaranteed, the safety and economic operation can be guaranteed as well [2]. Bearings are the main non-linear components of rotating machines, whose malfunctioning severely affects system operation. Therefore, several monitoring and preventive maintenance strategies have been proposed to guarantee the efficient operation of bearings. In many applications, such as turbines, aircraft engines and others, minor faults can cause dangerous and expensive side effects [3]. Bearing diagnostics and diagnostics of rotational equipment have been widely used for several decades owing to the availability of data monitoring systems. Bearing diagnostic systems based on Deep-Learning (DL) networks have yielded promising results [4]. In particular, Deep Neural Networks (DNN), including, perceptron multi-layer networks, auto-encoder networks and Convolutional Neural Networks (CNN), have reached high levels of accuracy, robustness and reliability [5, 6]. Technological advances have led to a rapid evolution of autonomous and highly effective manufacturing systems. Maintenance is an essential machine process. The acquisition and installation costs represent less than half the cost during the lifetime of a machine. On the contrary, the maintenance value is between 15% and 40% of the total value. Severaltechnological solutions have been proposed, but industries have refused to implement them, since they demand complex data analysis and elaborated result interpretation [7]. However, a few industry applications, such as the early detection and accurate diagnosis of bearing failures in Computer Numerical Control (CNC) rotatory, save both human and financial resources. On this basis, it seems that intelligent systems for detection and diagnosis of faults are necessary [8]. Computational tools, such as Neural Networks (NN), support vector machines and logistic regression have limitations owing to nature, size and diversity of data [9]. In comparison, Deep-Learning Neural Networks (DLNNs) are new tools with great power of representation. DLNNs identify relevant data information without pre-processing, transformation or domain mapping through complex non-linear functions. Some of the most efficient DLNN models are: Deep Belief Network (DBN), DNN and CNN. In addition, there are hybrid methods that combine the best features of traditional methods with DL networks to increase performance and efficiency. When comparing the three types of networks, it has been identified that DNN and DBN are used to extract typical features, while CNN identifies very distinguishing and essential ones [10]. However, the CNN algorithm is more complex and needs longer training periods [11; 12]. The factors that determine the effectiveness of a diagnostic system are the extraction of relevant information and selection of valuable information [8]. DLNNs have optimized this process, but complex machining faults are still difficult to identify [12]. DLNNs are capable of dealing with a large amount of information and monitoring the machine condition. DLNNs have been successfully applied to computer vision, automatic voice recognition, audio recognition and bioinformatics [13]. The most remarkable advances of DLNN applications in the monitoring and diagnosis of bearing machinery failures are summarized in Table 1.  
Several bearing failures arise from spindle rotation speed. Furthermore, rotation speed does not only cause machinery failure, but it also generates noise.