Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

Radhi, Ahmed Thamer; Zayer, Wael Hussein

doi:10.11591/ijpeds.v12.i1.pp597-611

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Signal parameters monitoring during motor run-out is relevant for assessing loads, eliminating overloads during motor self-start, etc. [28][29][30]. This is especially true for group run-out in complex load nodes, when there is their mutual influence due to the electrical energy recovery.…”

Section: Discussionmentioning

confidence: 99%

Neural net monitoring of signals parameters during the induction motors run-out

Andreev,

Grigoriev,

Malinin

et al. 2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

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The study is devoted to the possibility of using neural network algorithms to monitor the current and voltage signals parameters during transients in electrical engineering complexes. The direct propagation neural network can be programmed in standard microprocessor equipment and allows approximating signals in real time in the "sliding time window" mode. The proposed approach is demonstrated in laboratory experiments on the asynchronous motors run-out physical model as a complex load node part. It is shown that the multilayer perceptron use makes it possible to track changes in the signals frequency and amplitude with percentage accuracy for the observation time window duration of no more than a industrial frequency quarter period. The results demonstrate the using neural networks advantages for signal processing in non-stationary modes in electrical engineering and electric power industry.

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Section: Discussionmentioning

confidence: 99%

Neural net monitoring of signals parameters during the induction motors run-out

Andreev,

Grigoriev,

Malinin

et al. 2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

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“…There are a number of works aimed to carry out a comparative analysis of the effectiveness of these methods for various [2]- [5]. In this paper the development of current analysis method is proposed allowing to investigate SCIMs working in long steady-state conditions, typical for many technological processes without their stopping both at supplying from autonomous voltage inverter [6] and at supplying from grid [7]. Various methods of signal processing are used to analyze current and voltage peaks: frequency (fast Fourier transform), time (time series analysis, Park transform), frequency-time (wavelet transform), and intelligence (neural networks, fuzzy logic and genetic algorithms).…”

Section: Introductionmentioning

confidence: 99%

Neural network based detecting induction motor defects supplied by unbalanced grid

Dobroskok

Skakun

Belskii

et al. 2023

IJPEDS

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Predictive diagnosis of motor defects can reduce repair and downtime costs of electrical equipment. This paper presents the results of a research on the effectiveness of the defect classification and recognition system based on convolutional neural network to detect defects in a squirrel-cage induction motor based on the stator supply voltage and output phase currents when supplied from an industrial grid of limited power with possible voltage asymmetry and harmonic distortion taken into account. In this work a simulation model, implemented in MATLAB/Simulink, is proposed to investigate unbalanced conditions. The mathematical model has been verified on a physical test bench and then used to create a database of currents from measured asymmetric grid voltages in the presence of defects such as short circuit between turns of one phase, rotor bar breakage, rotor eccentricity and bearing defects. The classification quality of all defects in the neural network was 72%, with the exclusion of intercoil short circuit defect and the merging of different bearing defects, the quality of the model was achieved 91%. A further increase in the quality of the defect recognition system is due to a building up modification of the neural network architecture.

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“…In order to attenuates the fault detection errors due to external disturbances, some proposals are based on artificial intelligence (AI) systems such as vector support machines [13], neural networks [14], [15] or rulebased classifier [16]. These methods require an exhaustive training of the learning algorithms for the recognition of fault patterns through tests under different fault severities and operating conditions.…”

Section: Introductionmentioning

confidence: 99%

“…These methods require an exhaustive training of the learning algorithms for the recognition of fault patterns through tests under different fault severities and operating conditions. In [15] was proposed a faults diagnosis method to detect a instantaneous slip frequency control (ISFC) in stator winding of three-phase high-speed solid rotor IMs. The method is based on the negative sequence of the fundamental current and a fuzzy neural network algorithm.…”

Section: Introductionmentioning

confidence: 99%

Online detection of interturn short-circuit fault in induction motor based on 5th harmonic current tracking using Vold-Kalman filter

Mazzoletti

Gentile

Donolo

et al. 2023

IJECE

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In this paper we propose a strategy for real-time detection of interturn short-circuit faults (ISCF) on three-phase induction motor (IM) by using a Vold-Kalman filter (VKF) algorithm. ISCF produce a thermal stress into the stator winding due to large current that flows through the short-circuited turns. Therefore, incipient fault detection is required in order to avoid catastrophic failures such as phase to phase, or phase to ground faults. The strategy is based on an analytical IM model that includes a ISCF fault in any of the phase windings and considering the h-th harmonic in the voltage supply. Based on equivalent electrical circuits with harmonics in sequence components, we propose a strategy for detection of an ISCF on IM by tracking the 5th harmonic current component using a VKF algorithm. The proposed model is experimentally validated using a three-phase IM with modified stator windings to generate ISCF. Also, the IM is feeded by a programmable voltage source to synthesize distorted voltage supply with the 5th harmonic. The results demonstrated that the positive-sequence magnitude for the 5th harmonic current component is a good indicator of the fault severity once it exceeds a threshold limit value, even under load variations and unbalanced voltages.

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Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

Cited by 6 publications

References 18 publications

Neural net monitoring of signals parameters during the induction motors run-out

Neural net monitoring of signals parameters during the induction motors run-out

Neural network based detecting induction motor defects supplied by unbalanced grid

Online detection of interturn short-circuit fault in induction motor based on 5th harmonic current tracking using Vold-Kalman filter

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