Application of Artificial Intelligence to Real-Time Fault Detection in Permanent-Magnet Synchronous Machines

Nyanteh, Yaw D.; Edrington, Chris S.; Srivastava, Sanjeev; Cartes, D.A.

doi:10.1109/tia.2013.2253081

Cited by 79 publications

(26 citation statements)

References 11 publications

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“…However, the rate of increase in current (di/dt) due to the increase in load is smaller than that of the ITSF at the same operating conditions. An artificial neural network (ANN) can be used to determine the threshold of the magnitude and di/dt of the supply current for ITSF detection [71]. ANN can be trained to cover the different operating conditions of each specific machine and is able to update and reconfigure the detection algorithm quickly online during the operation.…”

Section: Stator Current Analysis Based Detection Techniquesmentioning

confidence: 99%

A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines

Ullah

Hur

2018

Energies

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Contemporary research has shown impetus in the diagnostics of permanent magnet (PM) type machines. The manufacturers are now more interested in building diagnostics features in the control algorithms of machines to make them more salable and reliable. A compact structure, exclusive high-power density, high torque density, and efficiency make the PM machine an attractive option to use in industrial applications. The impact of a harsh operational environment most often leads to faults in PM machines. The diagnosis and nipping of such faults at an early stage have appeared as the prime concern of manufacturers and end users. This paper reviews the recent advances in fault diagnosis techniques of the two most frequently occurring faults, namely inter-turn short fault (ITSF) and irreversible demagnetization fault (IDF). ITSF is associated with a short circuit in stator winding turns in the same phase of the machine, while IDF is associated with the weakening strength of the PM in the rotor. A detailed literature review of different categories of fault indexes and their strengths and weaknesses is presented. The research trends in the fault diagnosis and the shortcomings of available literature are discussed. Moreover, potential research directions and techniques applicable for possible solutions are also extensively suggested.

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Section: Stator Current Analysis Based Detection Techniquesmentioning

confidence: 99%

A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines

Ullah

Hur

2018

Energies

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“…PSO has been applied in a number of electrical machineryrelated problems, including multiobjective design and optimization of induction motors [38], design optimization of permanent-magnet-type transverse flux linear motor [36], parameter identification of induction motors [33], fault detection in permanent-magnet synchronous machines (PMSMs) [39], control of magnetic-levitation transportation system [40], and parameter identification of PMSMs [41].…”

Section: A Motivationmentioning

confidence: 99%

Induction Motor Efficiency Evaluation Using a New Concept of Stator Resistance

Salomon

Sant’Ana

Silva

et al. 2015

IEEE Trans. Instrum. Meas.

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This paper presents an air-gap torque (AGT)-based method for efficiency estimation of induction motors. A new concept of stator resistance that includes the mechanical losses effect is proposed. This new stator resistance is estimated through a particle swarm optimization approach based on the stator flux equations and minimization of torque error at the rated operation point. Then, the obtained stator resistance is used in the AGT equations to estimate the shaft torque and then the efficiency. Moreover, the rotor speed is estimated using induction motor current signature analysis. Thus, the proposed methodology for induction motor efficiency estimation relies only on line currents, line voltages, and nameplate data, being appropriate for in-service applications. Finally, the simulation and experimental results are presented to validate the proposed method at different load conditions.

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“…All these algorithms have been applied in optimization of motor, but there are still some defects. Some algorithms are fast but sensitive to initial point, and vulnerable to get different results from different initial points [8,9].Some depend on partial derivative or partial difference quotient while the optimization models of the motor are often unable to calculate derivative or its independent variable region is not continuous. Others have to be improved due to the uncertainty of the optimization efficiency, showing high efficiency in some parts but low in the others [10,11].…”

Section: Introductionmentioning

confidence: 99%

Air Gap Magnetic flux Optimization of Halbach Permanent Magnet Motor

Zhan¹,

Ji²,

Bao³

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)

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Abstract-This paper built a finite element model of the Halbach permanent magnet motor, whose accuracy was verified by comparing the output phase back-electromotive force of the model with that of the prototype motor. Keeping other parameters constant as far as possible, we investigated five variables one by one to find out the law how these variables affect air gap magnetic flux, which is a basis for further optimization research. After a new variable named Cost constructed to reflect the distortion ratio of air gap magnetic flux, this paper performed some optimization experiments by studying five parameters of the motor using Taguchi

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Application of Artificial Intelligence to Real-Time Fault Detection in Permanent-Magnet Synchronous Machines

Cited by 79 publications

References 11 publications

A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines

A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines

Induction Motor Efficiency Evaluation Using a New Concept of Stator Resistance

Air Gap Magnetic flux Optimization of Halbach Permanent Magnet Motor

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