## 2 Review of Issue Contents  
This SI compiles \(11\) papers from authors and research groups active in the field of machine fault diagnosis. As the topic itself is interdisciplinary, the papers presented in this SI have different viewpoints. Some look at the electrical side of machines, while others concentrate on the mechanical issues. However, most of the papers cover the integral part of measurement technology and related signal processing, essential for successful prognostic and diagnostic procedures. One of the papers is a comprehensive review paper, while the other ten focus on research aspects of machine fault diagnosis. The only review paper of the SI [1] provides an overview of the trends and challenges in intelligent condition monitoring of electrical machines using machine learning. As the world is moving toward Industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. Nevertheless, the collection of big data is a challenging task for the industry and laboratory because of limited resources. Some promising machine learning-based diagnostic techniques are presented in terms of their attributes. The authors of [2] analyzed the frequency interaction of the turbine block in a stand with respect to the magnitude of the error in measuring the turbine's power. They proposed an algorithm for constructing a dynamic analysis during the formation of a wave field of a stand for testing turbines. The research algorithm involved the use of theoretical solutions of nonlinear wave processes using linear oscillations, refined by experiments. The diagnostic model can determine the technical condition of the stand's elements and determine the causes of the discrepancies between the calculated and measured turbine power values. To clarify the stiffness coefficients between the stand's elements, a modal analysis was used to obtain the range of their changes depending on the external dynamic load, which made it possible to assess the impact of changes in the frequency interaction conditions on the turbine power measurement for different test modes. The conditions for amplifying the amplitude of oscillations at their eigenfrequencies were obtained, and the value of the possible deviation of the expected power value at its measurement for specific modes of the turbine was calculated. Condition monitoring of induction motors using transient modeling and recovery of a nonstationary fault signature was analyzed in [3]. The authors presented the modeling and the broken rotor bar fault diagnostics using time-frequency analysis of the motor current under an extended startup transient time. The transient current-based nonstationary signal was retrieved and investigated for its time-frequency response to segregate the rotor faults and spatial harmonics. For studying the effect of reduced voltage on various parameters and the theoretical definition of the fault patterns, a winding function analysis-based model is presented. Moreover, an algorithm to improve the spectrum legibility was proposed. It was shown that, through efficient utilization of the attenuation filter and consideration of the area containing the maximum power spectral density, the diagnostic algorithm gave promising results. Machine learning advances and its utilization in machine fault diagnosis were outlined in [4], where the authors dealt with transfer learning-based fault diagnosis under data deficiency. In fault diagnosis studies, data deficiency, meaning that the fault data for the training are scarce, is often encountered, which may greatly deteriorate the performance of the fault diagnosis.