# Electrical Monitoring under Transient Conditions:
## 1 Introduction  
According to some surveys, there are more than 300 million electric motors worldwide [1]. Most of them are used to drive diverse types of industrial machinery in a wide range of industrial areas (steel, paper, cement, petrochemical, mining industries... ). However, their use also expands to other applications that play a capital role for the development of today's societies, such as electric vehicles, traction systems, aerospace applications, or even robotics. In 2017, the global electric motor market size reached $96,967.9 million [2], which gives an idea of the significance of these machines in modern societies.  
Due to the vast utilization of electric motors, the monitoring of their condition is an area of capital importance for industry. Large electric motors are especially relevant due to their high costs (a single MW motor can reach a cost above $1 million), criticality, and complex repairs. An inspection of a large motor can imply expenses of several thousands of dollars, especially if it requires motor disassembly. Moreover, unexpected faults in these machines may lead to losses in the range of millions of dollars, as reported in some applications [3], due to production downtimes and repair costs, among others. If these facts were not enough, the presence of anomalies or defects in a machine, even if not leading to machine shutdowns, also affect its efficiency, leading to significant decrements that may makethe selection of high-efficiency motors (class IE4, IE5... ) useless in the applications in which they intervene. In this regard, recent works proved that the existence of rotor problems, stator asymmetries, cooling system defects, bearing faults, or coupling system problems may yield important drops in the efficiency of the machine [4], which is a repercussion that reinforced the importance of a proper knowledge of the health of these machines. Despite electric motors being quite reliable machines in general (especially the induction motors that are the most widespread in industry), they are prone to suffer different types of failures. Several surveys have pointed out that bearing failures and stator insulation faults are the most frequent [5], but other failures, such as rotor damages, core defects, or cooling system problems may also have very negative consequences for the motor integrity and performance. Over recent decades, there has been an increasing effort oriented toward the development of predictive maintenance techniques to determine the condition of certain parts of the machines. These techniques rely on monitoring different motor quantities (currents, vibrations, temperatures, fluxes, partial discharges... ) and analyzing them properly to detect possible evidences of the development of the fault. After many years of investigations in this area, one of the most important conclusions is that there is no method relying on a single quantity that is able to determine the integral condition of the whole motor, since each specific technique has shown good results for the diagnosis of certain faults but not for others. For instance, vibration data analysis is universally accepted as an excellent technique for the detection of mechanical failures, but its sensitivity is much worse when detecting other types of faults. As a result of the previous facts, the current trend is to develop intelligent systems that combine the information obtained from the application of diverse techniques based on the analysis of different quantities. The fact of relying on multiple and complementary informational sources enables reaching a more accurate and robust conclusion of the integral motor health. This idea is illustrated in Figure 1.  
During recent decades, it has been discovered that under some situations, very well-established condition monitoring techniques (such as current or vibration analysis) may fail, even when diagnosing the faults for which they are theoretically more suitable.