# Electrical Monitoring under Transient Conditions:
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Real Machine** & \multicolumn{3}{c}{**Diagnostic Conclusion MCSA**} \\ \cline{2-4}
**Condition** & **Healthy** & \multicolumn{3}{c}{**Faulty**} \\ \hline \hline \multirow{6}{*}{**Healthy**} & \multicolumn{3}{c}{**FALSE POSITIVE:**} \\  & & - & Load torque oscillations [10,11] \\  & & - & Cooling axial ducts [12,13] \\  & & - & Rotor core magnetic anisotropy [14] \\  & & - & Blade pass frequency vibrations [15] \\ \hline \hline \multirow{6}{*}{**Faulty**} & \multicolumn{3}{c}{**FALSE NEGATIVE:**} \\  & - & Non-adjacent bar breakages [16] \\  & - & Outer bar breakages in double \\ \cline{1-1}  & & cage rotors [17] & **CORRECT** \\ \cline{1-1}  & - & Erroneous speed estimation \\ \cline{1-1}  & - & Diagnosis under reduced slip \\ \cline{1-1}  & & conditions [10] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the diagnostic conclusion provided by Motor Current Signature Analysis (MCSA) and the real machine condition: sources of possible false positive and false negative indications. cage breakage in double cage rotors, existence of rotor magnetic anisotropy, influence of blade pass frequencies caused by certain loads... ). * _High reliability in the diagnostic_: since the diagnosis is based on the identification of the time-evolutions of frequency components, it is much more reliable than that relying on the assessment of single frequency components in the Fourier spectra; in other words, the time-frequency (t-f) patterns raising in the resulting t-f maps are less likely to be masked or interfered by other phenomena than a specific peak in the spectrum. If this were not enough, the diagnosis does not rely on a single evolution but on the multiple evolutions caused by the fault harmonics under the considered transient. * _Suitability for different operation regimes_: the analysis of transient quantities yields correct diagnosis conclusions regardless of the load level of the machine. Therefore, it is perfectly valid for machines operating under reduced slip conditions, provided that the transient is long enough. This does not happen with the conventional methods, such as MCSA. Moreover, the new methodology is especially valid for applications subjected to frequent transients (traction systems, renewable energy applications), since the employed tools are especially suited for these regimes. Again, the conventional methods, such as MCSA, are restricted to stationary conditions, providing possible wrong diagnostics in applications in which either the speed or the supply frequency changes during the capture [7]. The previous advantages are especially relevant in the case of large motors in which a correct diagnostic conclusion is critical. On the one hand, the decision of inspecting the machine is not trivial, since significant transportation and disassembly costs are involved (leaving aside the costs associated to the interruption of the production): hence, possible false positive indications must be avoided, because these could lead to unnecessarily incur in these costs. At the same time, it is crucial to detect the presence of the fault if it exists, since eventual false negative indications (i.e., fault remains undetected) may imply even higher losses for the company due to unplanned production downtimes. In this context, the application of these modern and powerful technologies plays a crucial role to reach a diagnosis with maximum reliability. The present paper intends to review the basic operation of the modern transient-based methodologies that have been proposed over recent years to electrical quantities of the motor, emphasizing their application to starting current and stray flux signals. Moreover, the paper includes several examples that prove the reliability of these approaches and demonstrate their usefulness, even in cases in which the classical techniques, such as MCSA or vibration data analysis, may fail.