An Open-Circuit Faults Diagnosis Approach for Three-Phase Inverters Based on an Improved Variational Mode Decomposition, Correlation Coefficients, and Statistical Indicators

Abdelkader, Rabah; Chérif, Bilal Djamal Eddine; Bendiabdellah, Azedine; Kaddour, Abdelhafid

doi:10.1109/tim.2022.3149339

Cited by 21 publications

(10 citation statements)

References 21 publications

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“…In order to quantitatively describe the separation performance of the two methods, we calculate the correlation correlation. The formula of the correlation coefficient ξ is as follows [32],…”

Section: â1mentioning

confidence: 99%

Core consistency blind source separation based on compressive sensing trilinear decomposition

Huang,

Li,

Wang

et al. 2024

Meas. Sci. Technol.

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In the traditional fault diagnosis method based on blind source separation, the mechanical signals are required to meet some additional conditions in the process of estimation of mixed matrix and separation of source signals, which may cause many problems in practice application because the mechanical signals do not suffer to these additional conditions. Additionally, the trilinear model has complex construction, high computational complexity, and large storage capacity in the fault source blind separation model with trilinear parallel factors. Based on the above deficiencies, a core consistency blind source separation method is proposed based on Compressive Sensing Trilinear Decomposition. In the proposed method, the core consistency diagnostic (CORCONDIA) is used to fit the tensor model, and determine the number of fault sources. The mixed matrix of the observed signal is estimated by the load matrices. Then, the minimum norm method is used to solve the underdetermined blind separation of fault sources. The simulation and experiment results show that the proposed method is superior the traditional core consistency blind separation method based on trilinear parallel factor analysis in the underdetermined blind separation. the proposed method can effectively solve the problem that the number of sources is unknown and the number of observed signals is less than the number of source signals.

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Section: â1mentioning

confidence: 99%

Core consistency blind source separation based on compressive sensing trilinear decomposition

Huang,

Li,

Wang

et al. 2024

Meas. Sci. Technol.

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“…Several studies have focused on OC faults, and different algorithms have been proposed, as summarized by many survey articles [1][2][3]7,8]. OC fault-diagnostics methods are mainly classified as model-based approaches [9][10][11][12][13][14][15][16][17][18][19][20][21], signal-based approaches [22][23][24][25][26][27][28][29][30], and data-driven approaches [31][32][33][34][35][36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

“…Signal-based methods typically involve motor-current signature analysis [26][27][28][29][30]. In [26], a single current sensor is employed to detect OS faults in the PMSM drive system.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm

Gmati,

Ben Rhouma,

Meddeb

et al. 2024

WEVJ

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Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.

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“…A major drawback of model-based fault diagnosis method is that the effectiveness is heavily dependent on the accuracy of model parameters, which makes them less suitable for complex systems with varying operating conditions [17]. Data-driven fault diagnosis algorithms utilize machine learning and artificial intelligence techniques to analyze historical and real-time data for detecting and diagnosing system faults [18][19][20][21]. Data-driven methods reduced reliance on accurate modeling, and the ability to learn from historical data to detect unknown faults.…”

Section: Introductionmentioning

confidence: 99%

An Open-Circuit Fault Diagnosis Method Based on Adjacent Trend Line Relationship of Current Vector Trajectory for Motor Drive Inverter

Sun,

Diao,

Song

et al. 2023

Machines

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This paper proposes a power switch open-circuit (OC) fault diagnosis method for a motor drive inverter system. This method utilizes adjacent trend lines to extract fault features from the current vector trajectory, enabling rapid fault diagnosis and localization. Firstly, the current vector trajectory is obtained by applying the Clark transformation to the three-phase currents, and trend line slopes are calculated using equidistant current signal nodes. Subsequently, the method determines fault by calculating changes in adjacent trend line slopes and quickly locates the faulty power switch by considering the average slope of adjacent trend lines and the beta-axis current direction in the alpha–beta coordinate system. Accurate OC fault localization can be achieved with just a few trend line data points calculated in each current cycle, reducing the extra hardware and computational burden. This method is not only suitable for load variations but also applicable to the diagnosis of both single-switch and multiple-switch faults. Finally, simulation and experimental results validate the effectiveness and robustness of the method.

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An Open-Circuit Faults Diagnosis Approach for Three-Phase Inverters Based on an Improved Variational Mode Decomposition, Correlation Coefficients, and Statistical Indicators

Cited by 21 publications

References 21 publications

Core consistency blind source separation based on compressive sensing trilinear decomposition

Core consistency blind source separation based on compressive sensing trilinear decomposition

Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm

An Open-Circuit Fault Diagnosis Method Based on Adjacent Trend Line Relationship of Current Vector Trajectory for Motor Drive Inverter

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