# Electrical Monitoring under Transient Conditions:
## 1 Introduction
This can be due either to an erroneous utilization of the technique or to the individual characteristics or operation conditions of the machine. For instance, the classical method based on the analysis of motor currents, known as Motor Current Signature Analysis (MCSA) [6] has led to erratic diagnostic conclusions when diagnosing rotor damages under variable speed conditions (e.g., in mining applications, as reported in [7]). Moreover, during  
Figure 1: Recent trend in the electric motors condition monitoring area based on the integration of the monitoring of different motor quantities for the development of intelligent diagnostic systems. recent years, some researchers have discovered that certain constructive characteristics of the motor (e.g., existence of cooling axial ducts, rotor core magnetic anisotropy issues, double cage rotors... ) and certain operational conditions of the machine (e.g., operation under reduced slips, existence of load torque oscillations, passing frequencies of the components of certain types of loads... ) may lead to false indications (positive or negative) when MCSA is applied to the detection of rotor asymmetries [8,9] and other faults. This situation is illustrated in Table 1 that shows how there are cases in which this consolidated technique may yield erroneous diagnostics. This lack of reliability is also common for other predictive maintenance methods that are widespread in industry. Due to the aforementioned constraints of the techniques currently available in industry, there has been intense research activity oriented toward the development of new methodologies that can overcome the drawbacks of these existing technologies. In this context, one of the most promising trends that has emerged in the condition monitoring area over recent years relies on the development of advanced techniques that are based on the analysis of quantities under any operation regime of the machine (including transients, i.e., operation regimes during which the speed of the machine varies such as motor starting, plug stopping, load variations... ) [9]. Indeed, it been proven that the analysis of transient quantities can provide very useful information for the diagnosis, avoiding false indications of classical methods. The underlying idea of this new approach is simple: unlike the classical stationary methods that rely on the analysis of steady-state quantities (currents, vibrations, fluxes... ) and the further detection of fault-related harmonics in the resulting Fourier spectra, the new transient-based methods are based on the identification of the time-frequency evolutions that are followed by the fault-related components during the transient [9]. To detect these evolutions, it is necessary to analyze the corresponding transient signals by applying advanced signal processing tools, which are known as time-frequency transforms. To sum up, this new diagnosis trend relies on identifying characteristic evolutions caused by fault components in the time-frequency maps resulting from the analysis of transient signals. Over recent years, this new approach has been applied with success to transient current, stray flux, and even vibration signals, yielding important advantages versus conventional approaches. These advantages can be synthesized in the following points:  
* _Avoidance of false indications_: the time-frequency analyses of starting currents or fluxes have proven to be immune to some phenomena that may yield false indications when classical approaches are employed (e.g., presence of cooling ducts, existence of load torque oscillations, diagnosis of outer