Metal Amorphous Nanocomposite (MANC) Alloy Cores with Spatially Tuned Permeability for Advanced Power Magnetics Applications

Byerly, Kevin; Ohodnicki, Paul R.; Moon, Seung‐Ryul; Leary, Alex; Keylin, Vladimir; McHenry, Michael E.; Simizu, S.; Beddingfield, Richard; Yu, You‐Jun; Feichter, G.; Noebe, Ronald D.; Bowman, Randy R.; Bhattacharya, Subhashish

doi:10.1007/s11837-018-2857-5

Cited by 25 publications

(17 citation statements)

References 16 publications

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“…Recent developments in magnetic material treatment has enabled a continuous spatial permeability tuning around the core [13]- [15]. In [15], the concept employed in [11] is taken IEEE POWER ELECTRONICS REGULAR PAPER/LETTER/CORRESPONDENCE one step further to obtain a continuous permeability profile around a toroid through gradient sintering.…”

Section: Introductionmentioning

confidence: 99%

“…That process requires only one material, NiZnCu ferrite, and a controlled temperature gradient across the core to obtain a permeability profile in a toroidal inductor. In [13], the spatial manipulation of permeability in Metal Amorphous Nanocomposite (MANC) alloy cores is reported. These materials are commonly referred as nanocrystalline core materials and some chemistries are commercially available (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Optimization Paradigm for Toroidal Inductors With Spatially Tuned Permeability

Nascimento

Moon

Byerly

et al. 2021

IEEE Trans. Power Electron.

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Spatially tuning core permeability of an electromagnetic device enables superior performance. A permeability profile can be heuristically selected to improve the flux distribution in a device with a given geometry, but in order to fully leverage the capacity of spatial dependent permeability engineering, the geometry and the permeability should be optimized simultaneously. The work herein presented sets forth a multi-physics design optimization paradigm that includes the permeability profile tuning in the context of both inductor and converter design. This approach enables the determination of Pareto optimal fronts consisting of a set of optimal solutions against competing objectives (e.g. mass and loss) under imposed constraints. To this end, computationally efficient analytical solutions of the heat transfer and electromagnetic formulations are derived for toroidal inductors, which are validated with Finite Element Analysis based simulations. The software implemented in Matlab 2018b is available online as an attachment to this paper.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization Paradigm for Toroidal Inductors With Spatially Tuned Permeability

Nascimento

Moon

Byerly

et al. 2021

IEEE Trans. Power Electron.

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“…Mobility, transportation, logistics, construction, production, agriculture, food, automation, and basically any economic activity and industry directly or indirectly depend on rotating electrical machines, as discussed in literatures, e.g., [2][3][4][5][6]. The rapidly evolving industries have suggested that we will be witnessing a further increase in this rate [7]. Furthermore, the increasing demand for hybrid and electric vehicles, the rapid transition toward automated systems and micro and nano mechatronics devises, increasing interests for more efficient energy conversion systems, and emerging new robotics machines have been motivating further advancement in the rotating electrical machines reported in the works of scholars, e.g., [7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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“…Mobility, transportation, logistics, construction, production, agriculture, food, automation, and basically, any economical activities and industries directly or indirectly depend on rotating electrical machines [4][5][6]. The rapidly evolving industries have suggested that we will be witnessing further increase this rate [7][8][9][10][11][12]. Furthermore, the increasing demand for the hybrid and electric vehicles, the rapid transition toward automated systems and micro and nano mechatronics devises, increasing interests for more efficient energy conversion systems, and emerging new robotics machines have been motivating further advancement in the rotating electrical machines [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Dineva¹,

Mosavi²,

Gyimesi³

et al. 2019

Preprint

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Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

show abstract

Metal Amorphous Nanocomposite (MANC) Alloy Cores with Spatially Tuned Permeability for Advanced Power Magnetics Applications

Cited by 25 publications

References 16 publications

Multiobjective Optimization Paradigm for Toroidal Inductors With Spatially Tuned Permeability

Multiobjective Optimization Paradigm for Toroidal Inductors With Spatially Tuned Permeability

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

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