# Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors
## 5 Application Validation and User Testing of the VR-Tool Application  
Special emphasis was placed on failure case studies to evaluate the accessibility, efficiency and commitment of the system as a whole (both modules) due to the novelty of integrating and processing raw data in the application. In the application, the user can train with the mentioned faults that can be found in induction motors and that can be appreciated in Figure 11; each one of the faults was artificially generated, _e.g._, the broken rotor bar was generated by drilling a hole that damaged one of the bars, the unbalance was induced by attaching a bolt simulating an eccentricity mass, and the misalignment was produced by a parallel bar misaligning the pulleys. On the other hand, regarding the fault of uniform wear that was analyzed in a gearbox, each one of the three levels, 25%, 50% and 75%, were artificially induced by a gear factory and all the teeth of the gearing were uniformly worn. * _Broken rotor bar_: The application can be used to monitor the stator current signal for detecting rotor faults in induction motors. Motor current signature analysis (MCSA) is an effective technique that can detect fault occurrence in induction motors. It basically relies on the analysis of current signatures by means of different techniques such as the fast Fourier transform (FFT). As mentioned above, when an induction motor operates under the influence of broken rotor bars, an affectation is produced that leads to a distortion of the airgap magnetic field that will induce some components in the stator current signal. In an estimated current spectrum, these components appear as sideband harmonics around the supply frequency, \(f_{s;}\) according to Equation (2), first-order sidebands components may appear at (1) when \(k=1\), and these first-order components are the most significant for detecting the occurrence of faults associated with broken rotor bars. The user must first learn how to identify this failure in virtual reality, as shown in Figure 12A, through an inspection of the cross-section of a motor. The user can visualize this failure of internal parts thanks to that arrangement in order to better understand the inspected elements. Second, as shown in Figure 12B, the user proceeds to the measurement phase; the raw data are automatically processed (although it should be noted that the data were available for export from the application if the user wished to apply other processes) and the result is displayed in the lower panel. In the  
Figure 11: Set of faults in the induction motor: (**a**) broken rotor bar, (**b**) unbalance, (**c**) misalignment, and the set of gears with (**d**) 25%, (**e**) 50% and (**f**) 75% uniform wear of the gear teeth. upper panel, the user can see this processed sample compared with a sample from a normal motor. In addition, it shows where to look for the evidence of a broken bar failure in an induction motor;
* _Unbalance and misalignment_: Eccentricity problems occur due to uneven airgaps between the stator and rotor. In the presence of eccentricities, low-frequency sidebands appear in the stator current spectrum given in Equation (3). The existence of these harmonics is due to the interaction between the two most common types of eccentricities, static and dynamic. Misalignment problems can be detected in induction motors through the following current spectral analysis in Equation (4). To learn how to identify this failure in virtual reality, the user can first inspect the different parts involved in unbalance and misalignment problems. Figure 13A shows an example of misalignment problems that the user can explore and perceive better in virtual reality through stereoscopic visualization and observation of the elements from any angle. Second, as in previous levels, the user proceeds to measurement. Processed data is displayed in the lower panel. In the upper panel, the user can see the processed sample compared with a sample from a normal motor.