The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics

Asad, Bilal; Vaimann, Toomas; Belahcen, Anouar; Kallaste, Ants; Rassõlkin, Anton; Iqbal, Muhammad Naveed

doi:10.3390/app10217572

Cited by 18 publications

(13 citation statements)

References 60 publications

(60 reference statements)

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“…FEM is an efficient numerical tool, widely used for induction motors electromagnetic and thermal analyses, envisioning design optimization [18,19], performance analysis [20], and fault diagnosis [21,22] among others.…”

Section: Finite Element Methodsmentioning

confidence: 99%

Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults

Abbadi

Cardoso

2020

Machines

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Three-phase induction motors are considered to be the workhorse of industry. Therefore, induction motor faults are not only the cause of users’ frustrations but they also drive up the costs related to unexpected breakdowns, repair actions, and safety issues. One of the most critical faults in three-phase induction motors is related to the occurrence of inter-turn short circuits, due to its devastating consequences. The topic of inter-turn short-circuit faults in three-phase induction motors has been discussed over recent decades by several researchers. These studies have mainly dealt with early fault detection to avoid dramatic consequences. However, they fall short of addressing the potential burnout of the induction motor before the detection step. Furthermore, the cumulative action played by an inevitable degree of unbalanced supply voltages may exacerbate such consequences. For that reason, in deep detail, this paper delves into the thermal analysis of the induction motor when operating under these two harsh conditions: unbalanced supply voltages and the presence of the most incipient type of inter-turn short-circuit condition—a short-circuit between two turns only. In this work, the finite element method has been applied to create the faulty scenarios, and a commercial software (Flux2D) has been used in order to simulate the electromagnetic and thermal behavior of the machine for various degrees of severity of the aforementioned faulty modes. The obtained results confirm that the diagnostic tools reported in the literature might not be effective, failing to warrant the required lead time so that suitable actions can be taken to prevent permanent damage to the machine.

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Section: Finite Element Methodsmentioning

confidence: 99%

Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults

Abbadi

Cardoso

2020

Machines

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“…Thereby, simulation times and memory resources are significantly reduced. Similarly, the authors of [ 110 ] solve through FEM the complete geometry of the IM to compute the coupling parameters and then, importing these parameters in the analytical model of the machine. In this case, FEM analysis run on multiple processor cores working in parallel with each other in order to speed up the simulations.…”

Section: Hybrid Modelsmentioning

confidence: 99%

A Review of Techniques Used for Induction Machine Fault Modelling

Terron-Santiago

Martínez-Román

Puche-Panadero

2021

Sensors

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Over the years, induction machines (IMs) have become key components in industry applications as mechanical power sources (working as motors) as well as electrical power sources (working as generators). Unexpected breakdowns in these components can lead to unscheduled down time and consequently to large economic losses. As breakdown of IMs for failure study is not economically feasible, several IM computer models under faulty conditions have been developed to investigate the characteristics of faulty machines and have allowed reducing the number of destructive tests. This paper provides a review of the available techniques for faulty IMs modelling. These models can be categorised as models based on electrical circuits, on magnetic circuits, models based on numerical methods and the recently proposed in the technical literature hybrid models or models based on finite element method (FEM) analytical techniques. A general description of each type of model is given with its main benefits and drawbacks in terms of accuracy, running times and ability to reproduce a given fault.

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“…Thanks to the increasing computational power of computers and cloud computation, the mathematical models of electrical machines can train AI algorithms. A comparison of different types of mathematical models of induction motors and their attributes can be found in [42,43].…”

Section: Diagnostic Possibilities With Machine Learningmentioning

confidence: 99%

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

et al. 2021

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A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.

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The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics

Cited by 18 publications

References 60 publications

Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults

Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults

A Review of Techniques Used for Induction Machine Fault Modelling

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

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