Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors

Hwang, Don-Ha; Youn, Young-Woo; Sun, Jong-Ho; Kim, Yong‐Hwa

doi:10.5370/jeet.2014.9.1.037

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…High resolution methods (HRM) can detect frequencies with low SNR. They have been recently introduced in the area of induction motors and wind generators faults diagnosis by the application of multiple signal classification (MUSIC) method [2], [6], [29]. MUSIC and its zooming methods are conjugated to improve the detection by identifying a large number of frequencies in a given bandwidth [2], [28], [30].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods

Chakkor

Baghouri

Hajraoui

2014

IJACSA

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1 Abstract-Electrical energy production based on wind power has become the most popular renewable resources in the recent years because it gets reliable clean energy with minimum cost. The major challenge for wind turbines is the electrical and the mechanical failures which can occur at any time causing prospective breakdowns and damages and therefore it leads to machine downtimes and to energy production loss. To circumvent this problem, several tools and techniques have been developed and used to enhance fault detection and diagnosis to be found in the stator current signature for wind turbines generators. Among these methods, parametric or super-resolution frequency estimation methods, which provides typical spectrum estimation, can be useful for this purpose. Facing on the plurality of these algorithms, a comparative performance analysis is made to evaluate robustness based on different metrics: accuracy, dispersion, computation cost, perturbations and faults severity. Finally, simulation results in MATLAB with most occurring faults indicate that ESPRIT and R-MUSIC algorithms have high capability of correctly identifying the frequencies of fault characteristic components, a performance ranking had been carried out to demonstrate the efficiency of the studied methods in faults detecting.

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Section: Related Workmentioning

confidence: 99%

Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods

Chakkor

Baghouri

Hajraoui

2014

IJACSA

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“…However, the diagnosis of the BRB fault is challenging because there are very slight fault signals at the current, voltage, rotor speed, and vibration. To overcome this problem, several studies have investigated BRB fault diagnosis methods for induction motors [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The studied BRB fault diagnosis methods can be categorized into digital signal analysis, other information from special sensors, fault start cases, and neural networks or machine-learning-based algorithms [3].…”

Section: Introductionmentioning

confidence: 99%

“…In [5], Park transform in a synchronous reference frame was used to convert the fundamental AC component to DC. Otherwise, under time-varying conditions, short-period data-based methods, such as multiple signal classification (MUSIC) [6], discrete wavelet transform (DWT) [7,9], minimum description length (MDL) [8], and the Prony method [10] can be used. However, with a low slip frequency, they also require steady-state data with a long period to separate the specific fault signal components from the large fundamentals.…”

Section: Introductionmentioning

confidence: 99%

Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square

2022

Energies

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This study proposes a broken rotor bar (BRB) fault diagnosis method for an induction motor using the sum of the weighted Fourier series coefficients squares of a complex current as a diagnosis signal. First, the sum of the squares of the Fourier series coefficients confirms the very narrow band-pass filter characteristics to derive a specific frequency component. This assists us in obtaining a BRB fault diagnosis signal that exists in a limited frequency range. Second, the magnitude of the Fourier series coefficients of the BRB fault signal is proportional to the slip frequency and load condition. A weighting factor is proposed to render the BRB fault signal irrelevant to the slip frequency and load condition. Consequently, the proposed fault diagnosis can be conducted without the slip frequency information or searching for the maximum coefficient component. Finally, the proposed fault diagnosis method is validated through experiments using a 55 kW induction motor with and without a BRB fault. It is implemented with a DSP controller at time intervals of 20, 10, 5, and 4 s for the Fourier series. The proposed diagnosis method performs well under various load conditions and shows that the derived fault signal exhibits a large difference between healthy and BRB faulty induction motors.

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“…Various sensing techniques have been developed for broken rotor bar detection in electrical motors [5]. For instance, motor current signature analysis (MCSA) is a widely used technique due to its low cost and non-invasive nature [6]. In MCSA, the steady state current of a running motor is collected and recorded.…”

Section: Introductionmentioning

confidence: 99%

Fault detection of broken rotor bar in LS-PMSM using random forests

et al. 2018

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Abstract-This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the startup transient current signal, and used these features to train and test a random forest to determine whether the motor was operating under normal or faulty conditions. For feature selection, we used the feature importances from the random forest to reduce the number of features to two features. The results showed that the random forest classifies the motor condition as healthy or faulty with an accuracy of 98.8% using all features and with an accuracy of 98.4% by using only the mean-index and impulsion features. The performance of the random forest was compared with a decision tree, Naïve Bayes classifier, logistic regression, linear ridge, and a support vector machine, with the random forest consistently having a higher accuracy than the other algorithms. The proposed approach can be used in industry for online monitoring and fault diagnostic of LS-PMSM motors and the results can be helpful for the establishment of preventive maintenance plans in factories.

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Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors

Cited by 11 publications

References 21 publications

Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods

Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods

Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square

Fault detection of broken rotor bar in LS-PMSM using random forests

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