## 1 Introduction
The main faults and their signatures are shown in Table 1.  
As shown in Figure 1, three main types of machine maintenance can be expressed to be applied in practice: corrective, preventive, and predictive maintenance [29]. In the case of corrective maintenance, also known as reactive maintenance, all needed repairs are assumed to be done after the failure has already occurred. However, this solution is appropriate only for small and insignificant workstations, where unexpected failure does not lead to economic or catastrophic consequences. Alternatively, many manufacturers assume preventive maintenance to the machine to avoid fatal outcomes. In this case, the electrical equipment needs to be regularly checked by the manufacturers through scheduled and specified inspections. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fault Signatures** & **Winding Short Circuit** & **Rotor Broken Bar(s)** & **Eccentricity** & **Bearing Faults** \\  & [23; 24] & [25] & [26; 27] & [28] \\ \hline vibration & \(\boldsymbol{\mathsf{\
u}}\) & \(\boldsymbol{\mathsf{\star}}\) \\ \hline current & \(\boldsymbol{\mathsf{\star}}\) & \(\boldsymbol{\mathsf{\star}}\) & \(\boldsymbol{\mathsf{\
u}}\) & \(\boldsymbol{\mathsf{\times}}\) \\ \hline magnetic flux changes & \(\boldsymbol{\mathsf{\star}}\) & \(\boldsymbol{\mathsf{\star}}\) & \(\boldsymbol{\mathsf{\star}}\) & \(\boldsymbol{\mathsf{\
u}}\) \\ \hline chemical analysis & \(\boldsymbol{\mathsf{\
u}}\) & \(\boldsymbol{\mathsf{\times}}\) & \(\boldsymbol{\mathsf{\times}}\) & \(\boldsymbol{\mathsf{\times}}\) \\ \hline torque changes & \(\boldsymbol{\mathsf{\star}}\) & \(\boldsymbol{\mathsf{\star}}\) & \(\boldsymbol{\mathsf{\
u}}\) & \(\boldsymbol{\mathsf{\times}}\) \\ \hline \hline \end{tabular}  
* \(\boldsymbol{\mathsf{\star}}\)—the most preferable parameter for condition monitoring; \(\boldsymbol{\mathsf{\
u}}\)—parameter can be used for condition monitoring; \(\boldsymbol{\mathsf{\chi}}\)—parameter cannot be used for condition monitoring.  
\end{table} 
Table 1: Signatures of the main faults in electrical machines. Although this solution can prolong machine lifespan, this schedule-based condition monitoring approach provides very little information on the remaining useful lifetime (RUL) of the devices and does not allow for their prognostic and full exploitation [30]. Moreover, because of the scheduled controls in production, it usually means a partial or total shutdown of the manufacturing process, leading to inefficient resource usage and extra operating costs. To decrease shutdown costs and minimize downtime, manufacturers switch their production over predictive maintenance [31; 32]. Condition monitoring is an essential component of predictive maintenance that allows forecasting a further failure based on electrical equipment's working conditions. A schematic illustration of the condition monitoring is shown in Figure 2. As can be seen, condition monitoring consists of several stages. The accuracy of measuring systems largely depends on the sensors used for data acquisition. Signal processing is one of the essential stages in condition monitoring. For feature extraction, to predict and teach the system to detect faults in the future, the system needs a more powerful tool. Moreover, as the data amount is increasing worldwide and computer science is rapidly developing, it is reasonable to remake production under advanced approaches using artificial intelligence (AI). There are widely used thermal imaging in industry to monitor the fault at the early stages of development [33]. In this case, as an example, different variants of machine learning (ML) algorithms can be used  
Figure 1: Maintenance types: (**a**) corrective maintenance, (**b**) preventive maintenance, (**c**) predictive maintenance. Figure 2: General diagram of decision models. for fault detection. These algorithms, as well as their comparison, are described in the following chapters.