Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis

Duda, Arkadiusz; Drozdowski, Piotr

doi:10.3390/en13246528

Cited by 15 publications

(10 citation statements)

References 41 publications

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“…The performance of Induction is affected by the faults. The statistical distribution of these electrical and mechanical faults in induction motors has been proven by surveys undertaken by the IEEE and EPRI [18], and [19]. The statistical studies of the IEEE and EPRI of the motor faults are discussed in [20][21][22][23][24] and listed in table II.…”

Section: Fault Studymentioning

confidence: 99%

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

Shaikh¹

2022

SSURJET

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In the industry, continuous health monitoring of electric motors is considered as an essential requirement. The continuous operation of the electric motor may cause malfunctions and addressing them timely is a critical challenge. The development of an efficient health monitoring system based on the identification of electrical motor faults is on great demand. This paper addresses the fault detection technique using discrete wavelet transform (DWT) algorithm for continuous health monitoring of electric motor-based systems. The faults have been detected through Motor Current Signature Analysis (MCSA) series procedures using the proposed method. Concurrently, the wavelet transform algorithm produces frequency-based spectrum related to the stator current parameters to accomplish the fault classification. This study provides an analysis of three motor faults of Phase imbalance, Rotor misalignment, and High contact resistance (HCR). DWT has the ability to categorize the input signals into approximate coefficient state for low frequency signals and detailed coefficient state for high frequency signals. In this research, this technique is used to detect faults because it is able of processing signals of very low frequency, and effectively deal with intermittent sharp signals that appear frequently during processing. DWT technique based on conditional monitoring of an induction motor with precise detailed coefficients and more skilled at light loads given on a motor-shaft with relatively fast execution time compared to FFT. Furthermore, the comparison of healthy and faulty induction motors has been compiled by Linear Discriminant Analysis (LDA) technique, a sub-application of MATLAB, and used for faults management purposes. LDA in comparison with PCA gives more perfect results. In this research, different faults have been detected with 100% accuracy using LDA classifier. The implementation of the proposed scheme will be beneficial in avoiding faults by ensuring that preemptive measures are taken timely against these faults, and the production of industries is protected from revenue losses.

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Section: Fault Studymentioning

confidence: 99%

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

Shaikh¹

2022

SSURJET

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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%

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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“…Regarding rotor faults, particularly by broken bar, many advanced studies with different methodologies have been extensively studied as the analysis by: Fault signature [11], motor current signature analysis [12], zero sequence current spectrum analysis [13], starting current analysis using optimized Stockwell transform [14], artificial intelligence analysis using neuronal networks [15,16], etc.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Induction Motor Using D-Q Simplified Model and Parity Equations

et al. 2022

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Induction motors are the horsepower in the industrial environment, and among them, 3-phase induction motors (3PIMs) stand out for their robustness and standard 3-phase power supply. In the literature, there are many approaches to diagnose faults for the nonlinear 3PIM model, and the vast majority focus on a single motor fault, although others address more faults but at the cost of greater computational complexity. In this sense, one of the methods with less computational load and early detection is the parity equation approach, which is based on analyzing the discrepancy between the input and output signals of a real process and a linear mathematical model to generate a residual signal, which contains important information about the fault and is obtained through a suitable selection of a weighting matrix W to isolate the faults as much as possible. The problem in this case study is that the 3PIM model is a nonlinear system. In this work, the fault detection method based on the parity equations approach applied in the 3PIM is explored using a simplified and proposed model of the 3PIM working in the D-Q synchronous reference frame, which is matched with the direct current motor model to guarantee both the existence of the parity space and to ensure a large set of detectable faults in the 3PIM parameters. Simulation and experimental results validate the proposed scheme and confirm a very simple set of residual equations to guarantee both early detection and a large set of detectable faults in: Stator and rotor resistances, stator and rotor inductances, as well as current, voltage, and speed sensors. Additionally, development of human machine interface (HMI) is implemented to validate the proposed scheme.

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Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis

Cited by 15 publications

References 41 publications

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

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

Fault Diagnosis of Induction Motor Using D-Q Simplified Model and Parity Equations

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