Kalman Filtering and Torque Spectral Analysis for Broken Bar Detection in Induction Motors

Eltabach, Mario; Charara, Ali; Zein, I.

doi:10.3182/20020721-6-es-1901.00773

Cited by 5 publications

(5 citation statements)

References 14 publications

(5 reference statements)

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“…A Kaiser-Bessel window of length = 3972 is selected as the sliding window function, with parameter = 40 and 80% overlap between the time-frames. This adhoc selection accrued from fine tuning of the parameters accounting for two factors: to achieve a windowing with a response as close as possible to rectangular, and secondly to yield a good trade-off between time and frequency resolution to observe the harmonic trajectories in the spectrogram [15], [31].…”

Section: Spectral Components Extractionmentioning

confidence: 99%

“…Rotor, stator and supply related fault signatures have previously been studied for space and time dependent harmonics. The detection of such signatures is based on early and more recent studies, in which the theoretical, experimental and quantitative modelling of the harmonic and spectral content is provided [4]- [6], [12]- [15].…”

Section: Introductionmentioning

confidence: 99%

“…Further, [14] introduces an analytical categorization of harmonics for different case studies of healthy cages. An interesting comparison of signal spectral analysis methods is also given by [15] for broken bar faults using observers and providing a descriptive analysis of internal and external diagnostic methods. An analytical approach for the stator related frequencies under bar breakages is provided in [16] and is achieved through the experimental investigation of the sidebands of the 1 st , 5 th and 7 th harmonic.…”

Section: Introductionmentioning

confidence: 99%

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A New Approach for Broken Rotor Bar Detection in Induction Motors Using Frequency Extraction in Stray Flux Signals

Panagiotou

Arvanitakis

Lophitis

et al. 2019

IEEE Trans. on Ind. Applicat.

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This work offers a reliable solution to the detection of broken rotor bars in induction machines with a novel methodology, which is based on the fact that the fault related harmonics will have oscillating amplitudes due to the speed ripple effect. The method consists of two main steps: Initially, a timefrequency transformation is used and the focus is given on the steady-state regime; thereupon, the fault related frequencies are handled as periodical signals over time and the classical Fast Fourier Transform is used for the evaluation of their own spectral content. This leads to the discrimination of sub-components related to the fault and evaluation of their amplitudes. The versatility of the proposed method relies on the fact that it reveals the aforementioned signatures to detect the fault, regardless of the spatial location of the broken rotor bars. Extensive finite element simulations on a 1.1MW induction motor and experimental testing on a 1.1kW induction motor lead to the conclusion that, the method can be generalized on any type of induction motor independently from the size, power, number of poles and rotor slot numbers.

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Section: Spectral Components Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Approach for Broken Rotor Bar Detection in Induction Motors Using Frequency Extraction in Stray Flux Signals

Panagiotou

Arvanitakis

Lophitis

et al. 2019

IEEE Trans. on Ind. Applicat.

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“…In order to validate the EKF, we use the observer benchmark given in Eltabach et al (2002) and evaluate the error between the actual and the estimated speeds. The aim of this test is to prove the effectiveness of the EKF under various operating conditions, as displayed in Figure 10.…”

Section: Induction Motor Speed Observersmentioning

confidence: 99%

New fault-tolerant control architectures based on voting algorithms for electric vehicle induction motor drive

Raisemche

Boukhnifer

Diallo

2016

Transactions of the Institute of Measurement and Control

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International audienceIn this paper, we present a comparative study of four voting algorithms in two observer-based fault-tolerant control (FTC) architectures for an electric vehicle (EV) induction motor drive. The first architecture, called output FTC, is based on the mechanical sensor, an EKF and a second-order sliding mode observer (SMO2). The second one, input FTC, is based on three controllers (PI, H loop shaping and the generalized internal model control), the most appropriate being selected to ensure good behaviour in presence of a multiplicative sensor fault (the fault is modelled as an exponential type emulating a bias). A third architecture, called hybrid FTC, based on the previous output and input fault-tolerant schemes, is built to mitigate simultaneous faults. Simulation and experimental results for a 7.5-kW induction motor drive show the efficiency of the approaches and their robustness against parametric variations for different load conditions

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“…The Extended Kalman Filter is a popular method to observe non-measurable variables and estimate physical parameters. (Eltabach M. et al 2002, Ouhrouche M.A. 2000, Hajiyev C. M. 1999, Lapeyre, F. et al 1997Grover, R. and Hawng, P. 1985).…”

Section: Introductionmentioning

confidence: 99%

Overcoming Sensor Faults in Controlled Induction Motors Using Ekf

Perán¹,

Miguel²,

Mendoza³

et al. 2005

IFAC Proceedings Volumes

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Kalman Filtering and Torque Spectral Analysis for Broken Bar Detection in Induction Motors

Cited by 5 publications

References 14 publications

A New Approach for Broken Rotor Bar Detection in Induction Motors Using Frequency Extraction in Stray Flux Signals

A New Approach for Broken Rotor Bar Detection in Induction Motors Using Frequency Extraction in Stray Flux Signals

New fault-tolerant control architectures based on voting algorithms for electric vehicle induction motor drive

Overcoming Sensor Faults in Controlled Induction Motors Using Ekf

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