Analysis and Detection of Short Circuits in Fractional Horsepower Commutator Machines

Retana, R.; Paweletz, A.; Herzog, Hans-Georg

doi:10.1109/tec.2008.918655

Cited by 8 publications

(8 citation statements)

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“…Diagnostic tests are used to detect possible damages in electric circuits of DC and AC motors [2,7,10,12,13,17,20,21]. For this purpose the relevant methods of analysis and recognition of measuring signals are used [1, 3, 4, 14-16, 18, 22, 23].…”

Section: Introductionmentioning

confidence: 99%

Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier

Głowacz¹,

Głowacz²

2015

Archives of Electrical Engineering

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Abstract:In this article results of diagnostic investigations of separately excited DC motor were presented. In diagnostics were applied a Fourier analysis method based on the fast Fourier transform (FFT) and a recognition method using Bayes classifier. In training process a set of the most important frequencies has been determined for which differences of corresponding signals in two states are the largest. Three categories of signals have been recognized in identification process: faultless state, state of the rotor broken one coil and state of the rotor shorted three coils.

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Section: Introductionmentioning

confidence: 99%

Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier

Głowacz¹,

Głowacz²

2015

Archives of Electrical Engineering

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“…Moreover, no criterion has been presented for short-circuit fault detection in [13]. A mathematical model, that quantifies the variation of the slot-leakage flux as a function of the relative position of partially short-circuited turns, has been developed in [14]. It should be noted that performance analysis of the faulty motors in different conditions are the primary stages of the fault-detection procedure and proposing competent and applicable indices is the goal of this procedure that is missed in [14].…”

mentioning

confidence: 99%

“…A mathematical model, that quantifies the variation of the slot-leakage flux as a function of the relative position of partially short-circuited turns, has been developed in [14]. It should be noted that performance analysis of the faulty motors in different conditions are the primary stages of the fault-detection procedure and proposing competent and applicable indices is the goal of this procedure that is missed in [14]. The second-level wavelet-packet-transform coefficients of line currents have been employed as inputs of a three-layer feed-forward neural network for short-circuit fault recognition [15].…”

mentioning

confidence: 99%

Feature Extraction for Short-Circuit Fault Detection in Permanent-Magnet Synchronous Motors Using Stator-Current Monitoring

Ebrahimi

Faiz

2010

IEEE Trans. Power Electron.

172

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In this paper, a novel frequency pattern and competent criterion are introduced for short-circuit-fault recognition in permanent-magnet synchronous motors (PMSMs). The frequency pattern is extracted from the monitored stator current analytically and the amplitude of sideband components at these frequencies is introduced as a proper criterion to determine the number of shortcircuited turns. Impacts of the load variation on the proposed criterion are investigated in the faulty PMSM. In order to demonstrate the aptitude of the proposed criterion for precise short-circuit fault detection, the relation between the nominated criterion and the number of short-circuited turns is specified by the mutual information index. Therefore, a white Gaussian noise is added to the simulated stator current and robustness of the criterion is analyzed with respect to the noise variance. The occurrence and the number of short-circuited turns are predicted using support-vector machine as a classifier. The classification results indicate that the introduced criterion can detect the short-circuit fault incisively. Simulation results are verified by the experimental results.Index Terms-Classification, fault diagnosis, feature extraction, mutual information (MI), permanent-magnet synchronous motors (PMSM), short circuit, support-vector machine (SVM).

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“…increased air gap k cs Carter factor stator k cr Carter factor rotor τ sp pole pitch of the stator winding t c turns per coil γ sc coil width stator b s slot width b t tooth width τ s slot pitch h y yoke height h s slot height λ ratio between b s and δ r 1 inner radius r 2 outer radius ρ specific resistance a m number of parallel coil groups II. INTRODUCTION LECTRIC MACHINES are widely used in different applications.…”

mentioning

confidence: 99%

“…A method often applied to provide such a model based on the winding function theory. Retana, Paweletz and Herzog [2] investigated the consequence of short circuits between adjacent commutator bars in a fractional horsepower commutator machine for automotive application. In [1] Joksimovic and Penman developed a model of a poly phase cage induction machine for simulation inter-turn circuits in the stator winding.…”

mentioning

confidence: 99%

Parameter identification of an axial-flux induction machine by winding functions

Hecker

Igelspacher

Herzog

2010

The XIX International Conference on Electrical Machines - ICEM 2010

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In this paper the parameters of an axial-flux induction machine are determined. The parameters are part of equations describing the dynamic of the machine and hence required to develop a dynamic model based on the winding function theory. This theory is widely used to simulate faults in different radial-flux machines. Former articles all dealing just with radial-flux machines. Here the theory for axial-flux induction machines is introduced and applied for parameter determination. This paper will be useful to construct a model of axial-flux induction machines in a simulation environment like MATLAB/ SIMULINK.

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Analysis and Detection of Short Circuits in Fractional Horsepower Commutator Machines

Cited by 8 publications

References 10 publications

Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier

Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier

Feature Extraction for Short-Circuit Fault Detection in Permanent-Magnet Synchronous Motors Using Stator-Current Monitoring

Parameter identification of an axial-flux induction machine by winding functions

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