Stator fault diagnosis of a BLDC motor based on discrete wavelet analysis using ADAMS simulation

Hosseini, Saba M.; Hosseini, Faezeh Sadat; Abedi, Mostafa

doi:10.1007/s42452-019-1449-5

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…The efficacy of using Daubechies wavelets is based on the precise signal reconstruction. Moreover, using a high-order wavelet improves the analysis results, which yields satisfactory outcomes in the diagnosis operation [28][29] . These reasons form the basis for selecting Daubechies-8 as the mother wavelet in this study.…”

Section: Discrimination Of Mother Waveletmentioning

confidence: 99%

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Almounajjed

Sahoo

Kumar

et al. 2023

Chin. J. Electr. Eng.

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A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit (ITSC) faults in induction motors (IMs) is proposed. Determining the incipient ITSC fault and its severity is challenging for several reasons. The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault. Moreover, the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity. The objective of this study is the early detection of incipient ITSC faults. Furthermore, it aims to determine the percentage of shorted turns in the faulty phase, which acts as an indicator for severe damage to the stator winding.Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform. A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet. The statistical parameters L 1 and L 2 norms are computed for the detailed coefficients. These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault. Combining L 1 and L 2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim. An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process. The classifier output defines the percentage of defective turns with a high level of accuracy. The competency of the adopted methodology is validated via simulations and experiments. The results confirm the merits of the proposed method, with a classification test correctness of 95.29%.

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Section: Discrimination Of Mother Waveletmentioning

confidence: 99%

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Almounajjed

Sahoo

Kumar

et al. 2023

Chin. J. Electr. Eng.

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show abstract

“…The method of producing fault in gate pulse generation is also discussed. In [79] a method is used to detect the stator inter-turn fault in the BLDC motor. With help of stator inter currents during normal conditions is compared with the stator inter currents during fault conditions, the difference is converted as the energy spend.…”

Section: Reliability Control Techniques a Fault-tolerance Controlmentioning

confidence: 99%

“…The star-point of the motor is connected to the dc bus of the drive system and multiphase motors are used [79] Techniques used to mitigate EMI are classified as follows:…”

Section: B Electromagnetic Interference Controlmentioning

confidence: 99%

A Review of BLDC Motor: State of Art, Advanced Control Techniques, and Applications

et al. 2022

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Brushless direct current (BLDC) motors are mostly preferred for dynamic applications such as automotive industries, pumping industries, and rolling industries. It is predicted that by 2030, BLDC motors will become mainstream of power transmission in industries replacing traditional induction motors. Though the BLDC motors are gaining interest in industrial and commercial applications, the future of BLDC motors faces indispensable concerns and open research challenges. Considering the case of reliability and durability, the BLDC motor fails to yield improved fault tolerance capability, reduced electromagnetic interference, reduced acoustic noise, reduced flux ripple, and reduced torque ripple. To address these issues, closed-loop vector control is a promising methodology for BLDC motors. In the literature survey of the past five years, limited surveys were conducted on BLDC motor controllers and designing. Moreover, vital problems such as comparison between existing vector control schemes, fault tolerance control improvement, reduction in electromagnetic interference in BLDC motor controller, and other issues are not addressed. This encourages the author in conducting this survey of addressing the critical challenges of BLDC motors. Furthermore, comprehensive study on various advanced controls of BLDC motors such as fault tolerance control, Electromagnetic interference reduction, field orientation control (FOC), direct torque control (DTC), current shaping, input voltage control, intelligent control, drive-inverter topology, and its principle of operation in reducing torque ripples are discussed in detail. This paper also discusses BLDC motor history, types of BLDC motor, BLDC motor structure, Mathematical modeling of BLDC and BLDC motor standards for various applications.INDEX TERMS BLDC motor, torque ripple, current shaping techniques, controlling input voltage, direct torque control, drive-inverter topology, field orientation control, motor design, fault tolerance control and electromagnetic interference reduction.

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A Fault Detection Method Based on Embedded Feature Extraction and SVM Classification for UAV Motors

Yaman

Yol²,

Altinörs³

2022

Microprocessors and Microsystems

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Stator fault diagnosis of a BLDC motor based on discrete wavelet analysis using ADAMS simulation

Cited by 4 publications

References 31 publications

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

A Review of BLDC Motor: State of Art, Advanced Control Techniques, and Applications

A Fault Detection Method Based on Embedded Feature Extraction and SVM Classification for UAV Motors

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