Influence of high‐order electromagnetic force on vibration characteristics of induction motor

Liu, Xiaopeng; Hong, Jianfeng; Sun, Bingxiang; Zhang, Weige; Jiang, Jiuchun

doi:10.1049/elp2.12348

Cited by 4 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the electromagnetic theory of asynchronous motors, the air gap magnetic field of asynchronous motors can be written as [21]:…”

Section: Normalmentioning

confidence: 99%

See 1 more Smart Citation

Vibration Analysis for Fault Diagnosis in Induction Motors Using One-Dimensional Dilated Convolutional Neural Networks

Liu,

Hong,

Zhao

et al. 2023

Machines

Self Cite

View full text Add to dashboard Cite

Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromagnetic force under rotor eccentricity and broken bar fault are analyzed. Then, the fault vibration acceleration value collected by a one-dimensional dilated convolution pair is extracted, and the SeLU activation function and residual connection are introduced to solve the problem of gradient disappearance and network degradation, and the fault motor model is established by combining average ensemble learning and SoftMax multi-classifier. Finally, experiments of normal rotor eccentricity and broken bar faults are carried out on 4-pole asynchronous motors. The experimental results show that the accuracy of the proposed method for motor fault detection can reach 99%, which meets the requirements of fault motor detection and is helpful for further application.

show abstract

“…According to the electromagnetic theory of asynchronous motors, the air gap magnetic field of asynchronous motors can be written as [21]:…”

Section: Normalmentioning

confidence: 99%

“…. By substituting (2) into (1), the radial force wave generated by the air gap magnetic field is [21]:…”

Section: Normalmentioning

confidence: 99%

Vibration Analysis for Fault Diagnosis in Induction Motors Using One-Dimensional Dilated Convolutional Neural Networks

Liu,

Hong,

Zhao

et al. 2023

Machines

Self Cite

View full text Add to dashboard Cite

show abstract

Electromagnetic noise characteristics analysis and noise traceability in induction motor

Cheng,

Wang,

Zhang

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

To investigate the electromagnetic vibration and noise issues in induction motors, this study employs a combined methodology of experimental, theoretical, and simulation approaches to analyze the characteristics of electromagnetic noise. Initially, a theoretical analysis of the radial electromagnetic force of the motor is conducted, followed by a simulation of the motor’s electromagnetic vibration and noise at a working condition of 3000 rpm, incorporating a Multiphysics coupled analysis of electromagnetic-vibration-noise. Vibration testing on a specific compressor motor reveals that the primary vibration noise characteristic frequency is centered around 2000 Hz. A comparison between simulation and experimental results shows a high degree of congruence, indicating the reliability of the simulation method. Subsequently, an acoustic camera kit, Brüel & Kjær PULSE Reflex, based on a beamforming method, is employed for noise source identification and acoustic holography testing at 3000 rpm, which successfully localizes the noise sources. The analysis and experiments provided herein can serve as a reference for the development of electromagnetic vibration noise suppression techniques in induction motors.

show abstract

A Reliable Neural Network Approach for Monitoring and Diagnosing Bearing Faults in Induction Motors

Guerti,

Khoualdia,

Khoualdia

2023

2023 International Conference on Decision Aid Sciences and Applications (DASA)

View full text Add to dashboard Cite

Influence of high‐order electromagnetic force on vibration characteristics of induction motor

Cited by 4 publications

References 20 publications

Vibration Analysis for Fault Diagnosis in Induction Motors Using One-Dimensional Dilated Convolutional Neural Networks

Vibration Analysis for Fault Diagnosis in Induction Motors Using One-Dimensional Dilated Convolutional Neural Networks

Electromagnetic noise characteristics analysis and noise traceability in induction motor

A Reliable Neural Network Approach for Monitoring and Diagnosing Bearing Faults in Induction Motors

Contact Info

Product

Resources

About