## 1 Introduction
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{Vibration} & [1–12] \\ \cline{3-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Temperature & [2–6,10,13–18] \\ \cline{3-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Instantaneous rotation speed & [6,9,14,16,18–21] \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Acoustic & [5,8,11,12,14] \\ \cline{3-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Magnetic field & [20,22,23] \\ \cline{3-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Stray flux & [6,24] \\ \hline \multirow{5}{*}{Sensors} & \multicolumn{1}{c}{} & Instantaneous current & [2,4,6–9,11,12,14,15,17,18,20,24–35] \\ \cline{3-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Instantaneous voltage & [2,13,26,36] \\ \cline{3-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Instantaneous (active) electrical power & [13,19,21,25,34] \\ \cline{3-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Phase shift (power factor) & [6,13,17,30,33,37] \\ \hline \multirow{5}{*}{Processing techniques for monitoring} & \multicolumn{1}{c}{} & Fast Fourier Transform & [2,6,8,9,17,21,34] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Wavelet transform & [2,8,9,13,14,17,18,20,26–28,37–39] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Neural networks & [2,5,11,13,18,26,38,40] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Machine learning & [3,5,8,10,14,29,36] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Support vector machine & [2,13] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Deep learning & [11] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Vienna monitoring method & [41] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Early detection & [1,4,10,14,15,41] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Online & [8,9,17] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & IoT & [16,38] \\ \hline \multirow{5}{*}{Detectable failures} & \multicolumn{1}{c}{} & Bearing condition & [1–5,7–14,17,22,25,26,32,34,38,39] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Rotor mechanical imbalance/eccentricity & [4,6,9,10,13–15,17,20,22,28,37,39] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Broken bars in squirrel-cage rotor & [2,4,7–9,12,14,15,17,22–24,26,30,32,37,39,40] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Stator winding faults & [5,8,13,14,17,22,27,32,37,39,40] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Magnetic asymmetries in stator/rotor & [2,22,23] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Current imbalance & [3,8,9,14,22,28,37] \\ \cline{3-4} \cline{2-4}  & \multicolumn{1}{c}{} & Phase loss & [9,36] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A brief summary of the subject of bibliographical references on the condition monitoring of motors. dry and viscous friction) is driven in rotation by a rotating magnetic field (with torsional elasticity) generated (electromagnetically) by the stator. The purpose of this paper is to present some experimental results on IAEP analysis, which are useful in condition monitoring of three-phase AC asynchronous induction motors (with a squirrel-cage rotor) running with no load. It is shown experimentally that in the variation of the variable part of the IAEP over time (numerically described with a high sampling rate), there is a periodic pattern (with the same period as the period of rotation of the rotor) that can be used to describe the state of the rotor (or motor as well). Some results related to the recognition and the analyses of this pattern (the repeatability, the synthetic and analytical description, the influence of different factors on the shape of the pattern, etc.) are presented. This paper is organised as follows: Section 2 presents the materials and methods, Section 3 presents the experimental results, and Section 4 is reserved for discussions.