The cluster computation-based hybrid FEM–analytical model of induction motor for fault diagnostics

Bilal Asad*, Toomas Vaimann, Anouar Belahcen, Ants Kallaste, Anton Rassõlkin, M. Naveed Iqbal

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)
24 Downloads (Pure)


This paper presents a hybrid finite element method (FEM)–analytical model of a three-phase squirrel cage induction motor solved using parallel processing for reducing the simulation time. The growing development in artificial intelligence (AI) techniques can lead towards more reliable diagnostic algorithms. The biggest challenge for AI techniques is that they need a big amount of data under various conditions to train them. These data are difficult to obtain from the industries because they contain low numbers of possible faulty cases, as well as from laboratories because a limited number of motors can be broken for testing purposes. The only feasible solution is mathematical models, which in the long run can become part of advanced diagnostic techniques. The benefits of analytical and FEM models for their speed and accuracy respectively can be exploited by making a hybrid model. Moreover, the concept of cloud computing can be utilized to reduce the simulation time of the FEM model. In this paper, a hybrid model being solved on multiple processors in a parallel fashion is presented. The results depict that by dividing the rotor steps among several processors working in parallel, the simulation time reduces considerably. The simulation results under healthy and broken rotor bar cases are compared with those taken from a laboratory setup for validation.

Original languageEnglish
Article number7572
Number of pages15
JournalApplied Sciences (Switzerland)
Issue number21
Publication statusPublished - 1 Nov 2020
MoE publication typeA1 Journal article-refereed


  • Fault diagnosis
  • Finite element analysis
  • Induction motors
  • Modeling
  • Parallel processing


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