Orbit Analysis From a Stray Flux Full Spectrum for Induction Machine Fault Detection

Filho, Paulo Cezar Monteiro Lamim; Baccarini, Lane Maria Rabelo; Batista, Fabiano Bianchini; Araújo, Ana Carolina Santos

doi:10.1109/jsen.2021.3074815

Cited by 12 publications

(11 citation statements)

References 35 publications

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“…The lifespan of a generator is typically determined by the longevity of the statorwinding insulation material [6]. According to a relevant survey, winding failures account for more than 29% of faults in electrical machinery, and 36% of these failures were mostly related to stator windings [1,5,7,8]. Stator winding failures are the most common in DFIG and are typically caused by inter-turn short-circuit (ITSC) faults, in which two or more turns in the winding coil are short-circuited.…”

Section: Introductionmentioning

confidence: 99%

“…Stator winding failures are the most common in DFIG and are typically caused by inter-turn short-circuit (ITSC) faults, in which two or more turns in the winding coil are short-circuited. A low-turn ITSC fault (such as two-turn short circuit) is the most incipient and indicates the appearance of degradation in the winding insulation and various subsequent stator faults [1,7,8]. Therefore, an effective incipient diagnosis and evaluation method is required for interturn insulation faults that can realize early insulation detection and identify the severity before winding degradation and failure.…”

Section: Introductionmentioning

confidence: 99%

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Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Zhao,

Chen,

Liang

et al. 2023

Meas. Sci. Technol.

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Although doubly fed induction generators (DFIG) are widely used, difficulties in early fault detection and severity assessment for inter-turn short-circuit (ITSC) faults are highly prominent. In this manuscript, a novel incipient fault detection and state assessment method based on external leakage flux sensing and modified multiscale poincare plots (MMSPP) is proposed. An external leakage flux sensor is placed on the axial-end position of the generator to monitor the presence and evolution of ITSC faults. Multiscale poincare mapping is a novel nonlinear tool that is further developed and modified using the normal cumulative distribution function (NCDF) and multiscale computing methods to capture the behavior evolution and changes in the generator’s external leakage flux signals. The healthy indicator is based on the analysis of elliptical orbit features extracted from modified multiscale poincare plots, established by support vector data description (SVDD) with parameter optimization. The effectiveness of the proposed method was implemented and verified under an experimental environment on a 100 kW DFIG platform to detect incipient inter-turn short circuits (mainly considering the two-turn ITSC) and evaluate the performance degradation (three- to eight-turn ITSC) with 0%, 50%, and 100% different load conditions. The experimental results showed the viability of the approach and fault indicator for incipient fault detection and condition assessment of the wind generator’s low inter-turn insulation faults and for relative quantification of inter-turn short-circuit fault severity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Zhao,

Chen,

Liang

et al. 2023

Meas. Sci. Technol.

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“…Proc. 2022, 14, 19 2 of 10 of REM diagnosis have recently been interested in the stray flux signature analysis (SFSA) technique [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…2022, 14, 19 2 of 10 (MVSA), and motor current signature analysis (MCSA) [10][11][12][13]. Many scholars in the field of REM diagnosis have recently been interested in the stray flux signature analysis (SFSA) technique [14][15][16]. This paper examines the feasibility of using stray flux to detect REB faults in IM type.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Failure Detection in Induction Motors Using Stator Current and Stray Flux Analysis Techniques

Pusca

Sbaa

Bessous

et al. 2022

The 1st International Conference on Computational Engineering and Intelligent Systems

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Because of its benefits, an induction machine is used in a variety of applications. The machine’s robustness is one of its benefits. Generally, mechanical faults cause torque oscillations, eccentricity, and vibration, which affect the stator current value and magnetic field distribution. As a result, early warning of mechanical failures helps to prevent damage to the induction system or sudden stopping. In this sense, the accuracy of techniques in detecting rolling bearing failure is investigated in this article. The first method focused on stator current analysis and the second on stray flux signature analysis. The aim of this research is to compare the success of the stray flux technique and the stator current analysis in detecting inner raceway faults. In addition, this research suggests a novel method for determining the relationship between two signals based on a transfer function estimate and magnitude-squared coherence between current and flux. Experimental tests were realized in a laboratory to artificially create the bearing damage. After that, the analysis focused on characteristic harmonics related to the different harmonics.

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“…To improve the AWSC fault diagnosis accuracy and reliability by effectively using the fault-related features extracted from various sensors, a variety of fault diagnosis methods based on the physical characteristics have been developed on the different types of motors, mainly including induction motors [12,13,14,15,16], synchronous generators [17,18,19,20], brushless dc motors [21,22,23,24] and permanent magnet motors [25,26,27,28]. All of them are primarily based on the harmonic currents of the stator or rotor [12,18,21,25], the sequence component of the current [14,22], the magnetic characteristics [13,19,23,27], the vibration characteristics [20,24,28] and some novel feature quantities [16,26]. However, due to the little impact of a small-turn short-circuit on the electromagnetic field, the above methods perform poorly in VFRMs.…”

Section: Introductionmentioning

confidence: 99%

Characteristic analysis and diagnosis of short-circuit fault in armature winding of four-phase variable flux reluctance machine

Zhao

Zhang

et al. 2022

IEICE Electron. Express

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To improve the reliability of the variable flux reluctance machines (VFRMs) in the early stages, this letter proposes a novel fault feature for armature winding short-circuit (AWSC) fault in four-phase VFRMs. First, the harmonic characteristics of field induced voltage are researched theoretically in detail under normal and AWSC conditions. Then, a diagnosis method based on the harmonics of the field induced voltage is proposed and analyzed by simulation. Finally, related experiments are conducted to verify the proposed method. The proposed diagnosis method is simple and feasible, which can realize reliable protection of the power generation system for safe operation.

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Orbit Analysis From a Stray Flux Full Spectrum for Induction Machine Fault Detection

Cited by 12 publications

References 35 publications

Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Mechanical Failure Detection in Induction Motors Using Stator Current and Stray Flux Analysis Techniques

Characteristic analysis and diagnosis of short-circuit fault in armature winding of four-phase variable flux reluctance machine

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