# The Cluster Computation-Based Hybrid FEM-Analytical Model of Induction Motor for Fault Diagnostics
## 8 Conclusions  
This paper presents a hybrid FEM-analytical model solved in a parallel fashion on a cluster of computers for the reduction of computational time. The artificial intelligence (AI) based  
Figure 10: The simulation time, (**a**) per step with different number of single computer workers (cores) in parallel, (**b**) the mean per-step time with different number of cores of a single computer working in parallel, (**c**) overall time on one computer with different cores engaged in parallel, (**d**) overall time with different number of computers making a cluster with distributed cores in parallel. condition-monitoring techniques for predictive maintenance of electrical machines is gaining heightened popularity. This is not only because of their ability to detect faults at the incipient stage but also because of their aptitude for faults segregation. The accurate model of the electrical machines is the key element of these techniques, which is crucial for the collection of big data under various healthy and faulty conditions. This data is essential for the training of the diagnostic systems and for making the safety rules. Among several modeling techniques, the FEM-based models have proved their accuracy in the field of electrical machine design for the past few decades. The FEM models have very few approximations and can deal with almost any kind of geometrical complexities of the system as compared to their analytical counterparts. The analytical models have their own attractions, such as the reduced simulation time and the development of analytical equations, which are integral parts of drives and inverse problem theory, etc. The biggest challenge for FEM-based models is their complexity in the forms of computational time and required memory. While on the analytical side, the approximations are fatal for any reliable diagnostic algorithm. The world is witnessing the exponentially increasing trend in the power of processors and sophisticated IT networks, which leads toward cloud computation and industry 4.0 standards. By exploiting the benefits of analytical, FEM models, and cloud computation, this paper proposes a hybrid analytical-FEM model for the simulation of the machine under healthy and faulty conditions with reduced calculation time. Most of the techniques dealing with the reduction in the simulation time of FEM models fail when the machine is in a faulty condition, which makes it purely unsymmetrical. Moreover, any approximation for the sake of reduced complexity can decrease the reliability of the model-dependent diagnostic algorithm. With the development of more sophisticated processors and industry 4.0 standards, the complete models of the system can be solved in very much less time as compared to the conventional techniques. In this paper, the model is first divided into offline and online portions. All the inductances and other necessary parameters are calculated in the offline section, and the results are saved in 3D matrices as a function of rotor position. Once the inductances are calculated, they can be used in an online dynamic model where the performance parameters such as speed, torque, flux, and currents can be investigated under different conditions. In the case of some faults such as broken bars or broken end rings, the inductance matrices need not be calculated again but the fault can be manipulated in the online portion. This can be achieved by changing the values of the corresponding elements in the resistance matrix. For reducing the calculation time, the FEM model is divided into several computers before dividing the specific portion into the cores of any particular processor. For making a computer cluster resembling a cloud, four computers having the same specifications were used as a local area network. The total rotor steps were then divided into several cores working in parallel and the results were collected on the main computer. This technique reduces the simulation time drastically without any need for model approximation.