# Electrical Monitoring under Transient Conditions:
## 5 Conclusions  
This paper has been intended to provide an overview of electrical monitoring under transient conditions, which is a new approach that is drawing the attention of many researchers and companies in the electric motors condition monitoring area.  
Unlike the classical fault diagnosis methods that are based on the analysis of electrical quantities under steady-state operation of the motor by applying conventional signal processing tools, the new condition monitoring philosophy relies on registering the corresponding electrical quantity (i.e., current or flux) under transient operation of the machine (e.g., under starting) and analyzing it using time-frequency transforms. The ultimate objective is to identify, in the time-frequency maps resulting from these transforms, the characteristic patterns caused by the evolutions of fault-related components during the considered transient. It has been proven that these patterns are reliable'signatures' of the fault, since they are very unlikely caused by other phenomena that do not correspond to the failure. This fact enables avoiding false indications that are typical not only of classical electrical-based techniques but also of other predictive maintenance methods (e.g., vibration data analysis). These statements are proven by the results obtained during these last years in a wide range of motors in which the transient-based techniques have proven their accuracy regardless of the constructive characteristics of the machine (e.g., presence of rotor cooling ducts, double cage rotors, rotor magnetic anisotropy... ) and of its operating conditions (e.g., reduced slip operation, presence of load torque oscillations, blade pass vibrational frequencies due to the load... ).  
The paper has shown the main foundations and underlying ideas of the transient-based analysis of electrical quantities, emphasizing its advantages versus classical methods. Moreover, it has brought several examples, which referred to both laboratory and industrial motors (of different typology) that illustrate how the methodology is applied.  
Today, one of the most dynamic research lines is focused on the automatic identification of the fault patterns arising in the time-frequency maps resulting from the application of the transforms. In this regard, modern investigations are making use of artificial intelligence methods and pattern recognition algorithms to automatically identify the mentioned patterns, hence avoiding the necessity of user intervention. The development and optimization of these algorithms increases the possibility of implementing these technologies in portable condition monitoring devices or autonomous diagnostic systems that enable obtaining a direct diagnosis of the motor condition. The implementation of these expert systems in smart sensors is also a hot topic in the area [35]. The paper content may be of interest for researchers involved in the electric motors predictive maintenance area who want to be aware of the potential of this new technology that is expected to have a strong penetration in industry during the coming years.  
Figure 10: Time-frequency maps of the starting current for a H.V. wound rotor induction motor of a cement plant.  
**Funding:** This research was funded by by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I + D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I + D + i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).  
**Conflicts of Interest:** The authors declare no conflict of interest.