Stochastic models for the evaluation of magnetisation faults

Offermann, Peter; Hameyer, Kay

doi:10.1108/compel-10-2012-0210

Cited by 6 publications

(3 citation statements)

References 8 publications

(8 reference statements)

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“…For the uncertainty quantification the deterministic and uncertain parameters need to be concretized. According to measurement studies of a manufactured PMSM in [36], the magnitude B r,i and the direction φ i of the magnetic field of the i-th permanent magnet are consequently assumed uncertain, i.e., they are modeled as uniformly distributed random variables with mean values p = B r,1 , . .…”

Section: A Problem Settingmentioning

confidence: 99%

Multi-Objective Yield Optimization for Electrical Machines using Machine Learning

Huber¹,

Fuhrländer²,

Schöps³

2022

Preprint

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This work deals with the design optimization of electrical machines under the consideration of manufacturing uncertainties. In order to efficiently quantify the uncertainty, blackbox machine learning methods are employed. A multiobjective optimization problem is formulated, maximizing simultaneously the reliability, i.e., the yield, and further performance objectives, e.g., the costs. A permanent magnet synchronous machine is modeled and simulated in commercial finite element simulation software. Four approaches for solving the multiobjective optimization problem are described and numerically compared, namely: ε-constraint scalarization, weighted sum scalarization, a multi-start weighted sum approach and a genetic algorithm.

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Section: A Problem Settingmentioning

confidence: 99%

Multi-Objective Yield Optimization for Electrical Machines using Machine Learning

Huber¹,

Fuhrländer²,

Schöps³

2022

Preprint

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“…4. The modelling of those uncertainties is partially based on [6]. In total we have to deal with 48 uncertain parameters.…”

Section: B Permanent Magnet Synchronous Machinementioning

confidence: 99%

“…In a magnetics context, uncertainties in the material have been addressed for example in [2], [3], [4]. An uncertain material geometry was considered in [5], whereas uncertainties in sources have been discussed in [6]. Often one is interested in the forward propagation of those uncertainties to quantify the yield, rates of failure, stochastic moments, e.g.…”

Section: Introductionmentioning

confidence: 99%

A multilevel Monte Carlo method for high-dimensional uncertainty quantification of low-frequency electromagnetic devices

Galetzka,

Bontinck,

Römer

et al. 2018

Preprint

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This work addresses uncertainty quantification of electromagnetic devices determined by the eddy current problem. The multilevel Monte Carlo (MLMC) method is used for the treatment of uncertain parameters while the devices are discretized in space by the finite element method. Both methods yield numerical approximations such that the total errors is split into stochastic and spatial contributions. We propose a particular implementation where the spatial error is controlled based on a Richardson-extrapolationbased error indicator. The stochastic error in turn is efficiently reduced in the MLMC approach by distributing the samples on multiple grids. The method is applied to a toy problem with closed-form solution and a permanent magnet synchronous machine with uncertainties. The uncertainties under consideration are related to the material properties in the stator and the magnets in the rotor. The examples show that the error indicator works reliably, the meshes used for the different levels do not have to be nested and, most importantly, MLMC reduces the computational cost by at least one order of magnitude compared to standard Monte Carlo.

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