Squirrel Cage Induction Motor under Stator and Rotor Bars Faults Modeling and Diagnosis

Bensaoucha, Saddam; Ahmed, Bessedik Sid; Ameur, A.; Teta, Ali

doi:10.1109/ccee.2018.8634502

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…2. Induction machine modeling with inter-turn short-circuit faults The state model of the SCIM in the reference frame dq related to the rotor is defined by the nonlinear system (Bachir et al, 2006;Saddam et al, 2018):…”

Section: Compel 403mentioning

confidence: 99%

Induction machine stator short-circuit fault detection using support vector machine

Bensaoucha

Brik

Moreau

et al. 2021

COMPEL

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Purpose This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases. Design/methodology/approach To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set. Findings The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load. Originality/value The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

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Section: Compel 403mentioning

confidence: 99%

Induction machine stator short-circuit fault detection using support vector machine

Bensaoucha

Brik

Moreau

et al. 2021

COMPEL

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“…This work focused on experiments and numerical implementations of faults caused by a rotor breakage since, in its intermediate stage, it can be detected to stunt the IM accelerated aging [4]. However, frequency-related indicators are complicated to extract from an inverter-fed IM rotor because of power conversion harmonics appear in the stator current spectrum and they bury fault-related frequencies given by…”

Section: Induction Motor Faultsmentioning

confidence: 99%

“…These BRB frequencies f BRB depend on the motor characteristic values k/p = 2n + 1, ∀ n ∈ Z ++ , the per-unit motor slip s, the number of poles p, and the supply current frequency f s . Fault frequencies are well-known as Left Side-band Harmonic (LSH) for the minus sign and Right Side-band Harmonic (RSH) for the plus sign [3][4][5].…”

Section: Induction Motor Faultsmentioning

confidence: 99%

“…It is because of their important advantages in terms of cost, reliability, and robustness [2]. For their operating conditions under corrosive and harsh environments, these electro-mechanical converters require special supervision and maintenance to avoid, in the most extreme case, an industrial blackout [3,4]. On intermediate cases of mechanical, thermal, electrical, and external stresses, severe electrical and mechanical faults can be caused by accelerating functional aging in parts, such as the rotor, the stator or even its bearings [5,6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems

et al. 2019

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Fault monitoring systems in Induction Motors (IMs) are in high demand since many production environments require yielding detection tools independent of their power supply. When IMs are inverter-fed, they become more complicated to diagnose via spectral techniques because those are susceptible to produce false positives. This paper proposes an innovative and reliable methodology to ease the monitoring and fault diagnosis of IMs. It employs fractional Gaussian windows determined from Caputo operators to stand out from spectral harmonic trajectories. This methodology was implemented and simulated to process real signals from an induction motor, in both healthy and faulty conditions. Results show that the proposed technique outperforms several traditional approaches by getting the clearest and most useful patterns for feature extraction purposes.

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“…This method has some drawbacks like spectral leakage, need for high resolution, varying load conditions and ambiguous frequencies (Matić et al, 2012). Methods like Park's vector approach, wavelet transform, Hilbert transform (Saddam et al, 2018) and ZOOM-FFT (Tan et al, 2013) have been applied to overcome these limitations.…”

Section: Introductionmentioning

confidence: 99%

A knowledge-based diagnosis algorithm for broken rotor bar fault classification using FFT, principal component analysis and support vector machines

Arabaci

Mohamed

2020

IJIEI

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Despite their ruggedness and reliability, induction motors experience faults due to stresses and manufacturing errors. Early detection of these faults is important in preventing further damages and minimising down-time. In this study, a machine learning algorithm is proposed for detection and classification of broken rotor bar (BRB) faults according to their severity. Removal of high frequency components then amplification was performed on the measured single-phase current. Features were then extracted using FFT and principal component analysis (PCA). Support vector machines (SVM) was used for classification. Two classification schemes were analysed; one classifying in one step and another in two steps. Experiments were performed to evaluate the algorithms by analysing their recognition rates. Six different SVM kernels were studied. Recognition rates as high as 97.9% were achieved. False negative rate as low as 0% was also realised. Furthermore, it was found out that using more principle components does not yield significant improvements.

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Squirrel Cage Induction Motor under Stator and Rotor Bars Faults Modeling and Diagnosis

Cited by 8 publications

References 11 publications

Induction machine stator short-circuit fault detection using support vector machine

Induction machine stator short-circuit fault detection using support vector machine

Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems

A knowledge-based diagnosis algorithm for broken rotor bar fault classification using FFT, principal component analysis and support vector machines

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