Fault detection for PMSM motor drive systems by monitoring inverter input currents

Li, Jingyuan; Sumner, Mark; Zhang, He; Arellano-Padilla, J.

doi:10.23919/tems.2017.7961339

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Spectra test rig is developed to identify the fault developed in the gear box and other components of the motor. [3] Recorded signals from faulty motor were processed using spectrum analysis to demodulate information about the faulty frequencies. Signal Processing techniques like Fast Fourier transform and filters are involved to perform envelop analysis to extract fault signal from the original recorded signal from motor.…”

Section: Related Workmentioning

confidence: 99%

Automatic Speed Control of Motor via WAD Technique for Prevention of Faults in Motor

Kabilan

Selvakumar²

2018

IJPEDS

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Many technologies are introduced in monitoring the fault occurrence in the electric motors used in industrial applications. Sound Accusation, current signature analysis, and vibration based motor fault detection systems are widely used in present years. From all these methods analyzing the motor vibration pattern produces more accuracy in finding an occurrence of different faults in the electric motor. The frequency of vibration generated by the MEMS vibration sensor differs for rotor, stator and bearing faults. The signal generated is analyzed using three important techniques namely wavelet analysis, Dyadic Transformation, and Adaptive Neuro-Fuzzy Inference System(WAD Technique). Hardware with ARM microcontroller and ADXL MEMS vibration sensor was used to perform signal acquisition, and the generated signal is processed using the MATLAB software, and the speed of the motor is controlled based on the processed result. The performance of the system with all three algorithms was recorded, and the efficiency of the system is compared.

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Section: Related Workmentioning

confidence: 99%

Automatic Speed Control of Motor via WAD Technique for Prevention of Faults in Motor

Kabilan

Selvakumar²

2018

IJPEDS

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“…An overview of fault detection techniques for drives is presented in Reference 19. In Li et al, 20 a fault detection approach for DC motor power structures using DC input current monitoring is supplied.…”

Section: Introductionmentioning

confidence: 99%

ANFIS model based on fuzzy C‐mean , grid partitioning and subtractive clustering to detection of stator winding inter‐turn fault for PM synchronous motor

Mola

Amiri‐Ahouee

2021

Int Trans Electr Energ Syst

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Summary In this paper, one‐of‐a‐kind hybrid intelligence models based on the adaptive neuro‐fuzzy inference system (ANFIS) the use of C‐mean fuzzy clustering (FCM), grid partitioning (GP), and subtractive clustering (SC) fashions are used. Three extraordinary gaining knowledge of algorithms that were incorporated with the ANFIS version are used to locate fault prevalence within the permanent magnet synchronous motor (PMSM). Due to the fact it may both stumble on any inconvenience with any force and is flexible enough, and it can be used in offline and online identification of this method. In the beginning, the dynamic model of the PMSM along with its positive fault could be delivered. On the grounds that fault detection in these engines could be very critical, one‐of‐a‐kind strategies have been proposed for detecting stator deflection in electric machines. On the way to determine the fault percentage in the PMSM, hybrid intelligence fashions are used to identify the fault. The advantages of the proposed algorithm are the potential to stumble on faults with one‐of‐a‐kind domain names. The inputs of the proposed set of rules are PMSM current and torque alerts in normal and defective conditions. Inside the proposed set of rules, the club function structure turned into created with the FCM, SC, and GP methods. The outcomes show that the proposed approach can follow the fault in a brief time.

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Inter-turn fault detection in PM synchronous motor by neuro-fuzzy technique

Ahouee

Mola

2020

Int J Syst Assur Eng Manag

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Fault detection for PMSM motor drive systems by monitoring inverter input currents

Cited by 5 publications

References 10 publications

Automatic Speed Control of Motor via WAD Technique for Prevention of Faults in Motor

Automatic Speed Control of Motor via WAD Technique for Prevention of Faults in Motor

ANFIS model based on fuzzy C‐mean , grid partitioning and subtractive clustering to detection of stator winding inter‐turn fault for PM synchronous motor

Inter-turn fault detection in PM synchronous motor by neuro-fuzzy technique

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