Stator current model for detecting rolling bearing faults in induction motors using magnetic equivalent circuits

Han, Qinkai; Ding, Zhuang; Xu, Xueping; Wang, Tianyang; Chu, Fulei

doi:10.1016/j.ymssp.2019.06.010

Cited by 56 publications

(36 citation statements)

References 33 publications

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“…The characteristic frequencies of spectral components, which appear in the complex Fourier transform of motor current due to local bearing fault, are comprised [13] of the bearing defect frequencies, the supply grid frequency, and, for some bearing local faults, also of the shaft rotation frequency. Research studies have confirmed, e.g., [13], that spectral components, based on combinations of these characteristic frequencies, can be effectively used for identification of a variety of bearing faults in electrical motors.…”

Section: The Proposed Technologiesmentioning

confidence: 99%

“…As the faulty bearing has an outer race fault, the following characteristic frequencies related to outer race fault and combined of bearing outer race defect frequency f out and supply frequency f g [13] are used for TC estimation:…”

Section: Experimental Technology Validation and Technology Comparisonmentioning

confidence: 99%

“…One of the key components that essentially impacts induction motor functions is its rolling bearings. Fault identification and prognosis for bearings are the subjects of many research works that provide viable results [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The originality of work [12] relies on the use of high-resolution methods to detect modulations in the stator current. Stator current modeling for defective induction motor rolling bearings, based on magnetic equivalent circuits, is carried out [13]. An iterative numerical integration method is presented to solve the stator current model.…”

Section: Introductionmentioning

confidence: 99%

“…This approach is not effective for identification of early stage of bearing fault development. Therefore, more effective second order data processing techniques are employed for fault identification of motor bearing by motor current signature analysis, as follows: the Park vector approach and the extended Park vector approach [13,21,23], the spectral entropy [1], a fuzzy feedback observer [2], deep learning technology [1,6], the wavelet transform [9][10][11], and the spectral kurtosis [11,18]. However, all these advanced techniques are still the second order techniques and, therefore, not effective for identification of early stage of bearing fault development.…”

Section: Introductionmentioning

confidence: 99%

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Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

2020

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It is proposed, developed, investigated, and validated by experiments and modelling for the first time in worldwide terms new data processing technologies, higher order spectral multiple correlation technologies for fault identification for electromechanical systems via electrical data processing. Investigation of the higher order spectral triple correlation technology via modelling has shown that the proposed data processing technology effectively detects component faults. The higher order spectral triple correlation technology successfully applied for rolling bearing fault identification. Experimental investigation of the technology has shown, that the technology effectively identifies rolling bearing fault by electrical data processing at very early stage of fault development. Novel technology comparisons via modelling and experiments of the proposed higher order spectral triple correlation technology and the higher order spectra technology show the higher fault identification effectiveness of the proposed technology over the bicoherence technology.

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Section: The Proposed Technologiesmentioning

confidence: 99%

Section: Experimental Technology Validation and Technology Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

2020

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Air‐gap eccentricity fault diagnosis and estimation in induction motors using unscented Kalman filter

Bagheri

Ojaghi

Bagheri

2020

Int Trans Electr Energ Syst

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In this paper, a new approach is proposed for eccentricity fault detection in induction motors and estimation of the exact severities of the fault components. By using the Kalman filter estimator, the presented method can estimate degrees of the static, dynamic, and mixed eccentricity faults in the induction motors. The Kalman filter is a robust estimator having high capability in estimating the state variables of a dynamic system. This filter has many practical applications in industrial and nonindustrial systems and can implement continues-/discrete-time dynamic systems varying linearly or nonlinearly. In this paper, due to the nonlinear and continuous nature of the induction motor, the unscented Kalman Bucy filter (UKBF), which is a nonlinear continuous-time filter, is employed. At first, the model of the eccentric induction motor is simulated. Then, by using this model, the measured quantities and the Kalman filter, the eccentricity fault is detected, and the exact severities of the fault components (static, dynamic, and mixed) are estimated for a real motor that has been artificially made to be eccentric.According to the attained results, it is demonstrated that the conducted approach has high capability in estimating the exact severities of the fault components, and it shows efficient performance in all eccentricity states.

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Smart Energy Management System: SCIM Diagnosis and Failure Classification and Prediction Using Energy Consumption Data

Laayati

Bouzi

Chebak

2021

Lecture Notes in Networks and Systems

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Stator current model for detecting rolling bearing faults in induction motors using magnetic equivalent circuits

Cited by 56 publications

References 33 publications

Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

Air‐gap eccentricity fault diagnosis and estimation in induction motors using unscented Kalman filter

Smart Energy Management System: SCIM Diagnosis and Failure Classification and Prediction Using Energy Consumption Data

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