Vibroacoustic Prediction and Mechanism Analysis of Claw Pole Alternators

Wu, Shuanglong; Wu, Xudong; Zhong, Hong; Zhang, Yaodan

doi:10.1109/tie.2016.2645502

Cited by 58 publications

(18 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…cross entropy (2) following the same notation as Equation 1. It consists of two terms with the first one being the L 2 loss between the estimated and actual occupancy grid.…”

Section: Network Architecturementioning

confidence: 99%

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

The prediction of upcoming events in industrial processes has been a longstanding research goal since it enables optimization of manufacturing parameters, planning of equipment maintenance and more importantly prediction and eventually prevention of defects. While existing approaches have accomplished substantial progress, they are mostly limited to processing of one dimensional signals or require parameter tuning to model environmental parameters. In this paper, we propose an alternative approach based on deep neural networks that simulates changes in the 3D structure of a monitored object in a batch based on previous 3D measurements. In particular, we propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters and predict upcoming events related to sub-optimal performance. We validate our framework on a microelectronics use-case using the recently published "PCB scans" dataset where we simulate changes on the shape and volume of glue deposited on an Liquid Crystal Polymer (LCP) substrate before the attachment of integrated circuits (IC). Experimental evaluation examines the impact of different choices in the cost function during training and shows that the proposed method can be efficiently used for defect prediction.

show abstract

“…cross entropy (2) following the same notation as Equation 1. It consists of two terms with the first one being the L 2 loss between the estimated and actual occupancy grid.…”

Section: Network Architecturementioning

confidence: 99%

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

show abstract

“…There will be obvious noise peaks at the modal frequency regardless of speed. 26 Therefore, this study analyzed the radiated noise order characteristics at the motor rated speed of 3980 r/min.…”

Section: Vibro-acoustic Predictions and Experimental Verificationmentioning

confidence: 99%

Vibro-acoustic prediction and evaluation of permanent magnet synchronous motors

Dong

Liu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

In this study, a multiphysics finite element method is proposed to predict and evaluate the electromagnetic vibration and noise of the permanent magnet synchronous motors. First, the expressions of radial electromagnetic force waves were derived based on the established mathematical models of airgap magnetic field using the analytical methods. Subsequently, the main circumferential spatial orders influencing electromagnetic noise were analyzed and discussed. Then, a multiphysics simulation model that consists of mechanical field, electromagnetic field, and acoustic field was established for the calculation of the electromagnetic radiation noise. Finally, the multiphysics simulation model developed for the electromagnetic vibration and noise prediction was validated by comparing the finite element analysis and experimental data. It is shown that, although the local differences exist, the results from the finite element calculation and test analysis have a good agreement on the analytical mechanism overall, both in amplitude and main orders. In addition, this paper has made a detailed analysis to the electromagnetic noise generation mechanism, which lays the basis for further study in predicting and suppressing the electromagnetic vibration and noise of the drive motors of pure electric vehicle.

show abstract

“…In [22], the material parameters of the stator were measured directly by the ultrasonic technology in a SRM, but the test equipment requirements were high. In [23][24][25], the FE models of claw pole alternators and permanent magnet synchronous motors, considering the orthotropy of the stator core and the windings, was established to solve the modal parameters. However, the analysis of the effects of the orthotropic material parameters on the vibration characteristics and the modal frequency has not been implemented.…”

Section: Introductionmentioning

confidence: 99%

Method for acquisition of equivalent material parameters considering orthotropy of stator core and windings in SRM

Zuo

Liu

et al. 2019

IET Electric Power Applications

Self Cite

View full text Add to dashboard Cite

To solve the problem of the orthotropic material parameters acquisition, a novel method for the acquisition of the equivalent material parameters considering the orthotropy of the stator core and the windings in a switched reluctance motor (SRM) is proposed. First, a theoretical analysis of the influences of the orthotropic material parameters on the vibration characteristics is implemented. Second, according to the finite element (FE) equivalent model and the modal parameters obtained by the modal test, the equivalent orthotropic material parameters of the stator core and the windings are captured, respectively. Finally, considering the complex connections among components, a FE model of a SRM is established and validated by the modal test. The results indicate that the vibration characteristics of the FE model of the stator core and the windings with the orthotropic features can be analysed by assigning the equivalent orthotropic material parameters; the elastic modulus E x (E y) and the shear modulus G xy have effects on both the symmetric modal frequencies and the antisymmetric modal frequencies; the shear modulus G xz (G yz) only affects the antisymmetric modal frequencies; the elastic modulus E z can neither change the symmetric modal frequencies nor the antisymmetric modal frequencies.

show abstract

Vibroacoustic Prediction and Mechanism Analysis of Claw Pole Alternators

Cited by 58 publications

References 24 publications

A Deep Learning framework for simulation and defect prediction applied in microelectronics

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Vibro-acoustic prediction and evaluation of permanent magnet synchronous motors

Method for acquisition of equivalent material parameters considering orthotropy of stator core and windings in SRM

Contact Info

Product

Resources

About