Managing Uncertainties of Permanent Magnet Synchronous Machine by Adaptive Kriging Assisted Weight Index Monte Carlo Simulation Method

Ren, Ziyan; Ma, Jiangang; Qi, Yanli; Koh, Chang-Soon

doi:10.1109/tec.2020.3009249

Cited by 15 publications

(11 citation statements)

References 24 publications

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“…Formula ( 5) is only a simplified version of the principal formula, and the detailed construction process of the Kriging surrogate model method can be referred to [11].…”

Section: Worst-case Estimation Based On the Kriging Surrogate Model M...mentioning

confidence: 99%

“…However, as the number of random variables describing the uncertainty input of EMC simulation increases, the computational efficiency of the SCM will decrease exponentially, which is the dimensional disaster problem [7][8][9]. In order to effectively solve the dimensional disaster problem, the Stochastic Reduced-Order Model (SROM) [10] and Kriging surrogate model method [11] have been applied to the uncertainty analysis of EMC simulation in recent years. The SROM has the best application range for the random input mathematical model, but it can only provide the mean prediction value and variance prediction value in the uncertainty analysis results.…”

Section: Introductionmentioning

confidence: 99%

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Application of Non-embedded Uncertainty Analysis Methods in Worst Case Estimation of the EMC

Bai,

Geng,

Niu

2023

PIER C

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In recent years, the non-embedded uncertainty analysis method has been widely used in the field of Electromagnetic Compatibility due to its wide application range. In this paper, from the perspective of the practical application of uncertainty analysis methods, four non-embedded uncertainty analysis methods are applied to the worst-case estimation of Electromagnetic Compatibility, which are the Monte Carlo Method, Stochastic Collocation Method, Stochastic Reduced-Order Models, and Kriging surrogate model method. The performances of four uncertainty analysis methods in terms of computational accuracy, computational efficiency, and ability to deal with complex problems are compared in detail by using the parallel cable crosstalk prediction example in the existing literature and the uncertainty analysis example of self-constructed optimization test function, which provides a theoretical basis for uncertainty analysis method to guide the actual Electromagnetic Compatibility design.

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“…Formula ( 5) is only a simplified version of the principal formula, and the detailed construction process of the Kriging surrogate model method can be referred to [11].…”

Section: Worst-case Estimation Based On the Kriging Surrogate Model M...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Non-embedded Uncertainty Analysis Methods in Worst Case Estimation of the EMC

Bai,

Geng,

Niu

2023

PIER C

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“…Typical nonembedded uncertainty analysis methods include Monte Carlo Method (MCM) [3], Stochastic Collocation Method (SCM) [4], Kriging surrogate model method [5], Stochastic Reduced-Order Models (SROM) [6], etc. MCM is based on the principle of weak law of large numbers and utilizes a large number of discrete sampling points to describe the uncertainty of model parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The Kriging method uses a small amount of deterministic simulation results as the training set to train the surrogate model, and finally performs a large number of samplings on the input randomness of the model to obtain uncertainty anal-ysis results. However, its disadvantage is that the accuracy is poor when EMC simulation has large nonlinearity [5].…”

Section: Introductionmentioning

confidence: 99%

Application of Improved SROM Based on RBF Neural Network Model in EMC Worst Case Estimation

Hu,

Wang,

Huo

et al. 2024

PIER Letters

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The Stochastic Reduced-Order Models (SROMs) are a non-embedded uncertainty analysis method that has the advantages of high computational efficiency, easy implementation, and no dimensional disasters. Recently, it has been widely used in the field of EMC simulation. In the process of optimizing electromagnetic protection design, the worst-case estimation value is an extremely important uncertainty quantification simulation result. However, the SROMs have a large error in providing this result, which limits its application in the field of EMC simulation prediction. An improved SROM based on the Radial Basis Function (RBF) neural network algorithm is proposed in this paper, which improves the fitness function in the genetic algorithm center clustering process and constructs an RBF neural network model to obtain accurate worst-case estimation results. The accuracy improvement effect of the algorithm proposed in this paper in worst-case estimation is quantitatively verified by using a parallel cable crosstalk prediction example from published literature.

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“…Consequently, it was possible to design a motor that simultaneously improves motor performance and minimizes quality fluctuations. Ziyan et al performed RBDO to reduce the cogging torque considering the magnetic flux density of the stator [20]. Mun et al performed RBDO to reduce the cogging torque by considering the performance variation due to manufacturing tolerance and operating temperature [21].…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Robust Design Optimization for Maximizing the Output Torque of Brushless Direct Current (BLDC) Motors Considering Manufacturing Uncertainty

et al. 2022

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In recent years, the deterministic design optimization method has been widely used to improve the output performance of brushless direct current (BLDC) motors. However, it does not contribute to reducing the failure rate and performance variation of products because it cannot determine the manufacturing uncertainty. In this study, we proposed reliability-based robust design optimization to improve the output torque of a BLDC motor while reducing the failure rate and performance variation. We calculated the output torque and vibration response of the BLDC motor using the electromagnetic–structural coupled analysis. We selected the tooth thickness, slot opening width, slot radius, slot depth, tooth width, magnet thickness, and magnet length as the design variables related to the shape of the stator and rotor that affect the output torque. We considered the distribution of design variables with manufacturing tolerances. We performed a reliability analysis of the BLDC motor considering the distribution of design variables with manufacturing tolerances. Using the reliability analysis results, we performed reliability-based robust design optimization (RBRDO) to maximize the output torque; consequently, the output torque increased by 8.8% compared to the initial BLDC motor, the standard deviation in output performance decreased by 46.9% with improved robustness, and the failure rate decreased by 99.2% with enhanced reliability. The proposed reliability-based robust design optimization is considered to be useful in the actual product design field because it can evaluate both the reliability and robustness of the product and improve its performance in the design stage.

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Managing Uncertainties of Permanent Magnet Synchronous Machine by Adaptive Kriging Assisted Weight Index Monte Carlo Simulation Method

Cited by 15 publications

References 24 publications

Application of Non-embedded Uncertainty Analysis Methods in Worst Case Estimation of the EMC

Application of Non-embedded Uncertainty Analysis Methods in Worst Case Estimation of the EMC

Application of Improved SROM Based on RBF Neural Network Model in EMC Worst Case Estimation

Reliability-Based Robust Design Optimization for Maximizing the Output Torque of Brushless Direct Current (BLDC) Motors Considering Manufacturing Uncertainty

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