# Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors
## 2 Related Work
### Induction Motor Faults by Current Analysis  
The extensive and preferred use of the induction motor is due to its robustness, efficiency, and low cost. However, despite the robustness of these electric machines, the unavoidable occurrence of faults is inherent to their working operation. For that reason, in the last decades there have been different attempts to replace the preventive maintenance methods, i.e., the classical practice of preventive maintenance, which is based on the scheduled replacement of the components susceptible to failure, by new maintenance approaches. Preventive maintenance has evolved towards the continuous monitoring and control of certain operating parameters and variables that are observed to determine the right time to intervene and to correct an incipient fault that may produce irreparable damages in the future [13; 15]. In this regard, several condition monitoring approaches have been proposed around the world in order to face the unavoidable appearance of faults. Most have been based on the analysis of vibration and temperatures, although the MCSA (motor current signature analysis), which is based on the monitoring of the stator current signal, is still the most preferable and reliable strategy for fault detection in induction motors, as it is capable of detecting faults such as broken rotor bars, misalignments, unbalances, bearing damage and electrical problems in the stator or winding [28].  
Different processing techniques may be used to conduct the induction motor assessment; however, MCSA is mainly based on the analysis of those characteristic fault-related frequency components that a particular fault can induce in the stator current signal. For example, in [29], the characteristics of an infinite impulse response (IIR) filter were exploited for detecting the broken rotor bar fault-related frequencies over the estimated stator current spectrum. Likewise, the detection of broken rotor bars in an induction motor was reported in [30] through an analysis of the transient stator current by means of the MUSIC algorithm, which performs an analysis in the time-frequency domain. Thus, MCSA is not limited to a specific domain of analysis. Although complex signal processing is performed by MCSA, the classical approaches are based on the analysis of the effects produced by a specific fault; in this sense, the inspection of different types of misalignments was conducted in [31] by means of MCSA where the diagnosis was achieved though the examination of those fault-related frequency components that appear in the FFT current spectrum. On the other hand, the detection of rotor unbalances was carried out in [32] by performing a bi-spectrum analysis over the stator current signature in an induction motor that was fed by a variable frequency converter. In fact, signal processing techniques that consider complex processes are focused on specific applications where the condition assessment may be complicated, i.e., variable frequency converters which induce additional frequency components in the current consumption during the induction motor operation. Consequently, regardless of the signal processing method that is employed, it has been demonstrated that the application of MCSA may be used as a reliable diagnostic tool capable of detecting the most common failures that affect the working operation of induction motors.