مقاله انگلیسی رایگان در مورد تشخیص خطاهای استاتیک، پویا و مرکب – IEEE 2009

IEEE

 

مشخصات مقاله
ترجمه عنوان مقاله تشخیص خطاهای استاتیک، پویا و مرکب در موتورهای همگام مگنت دائمی
عنوان انگلیسی مقاله Static-, Dynamic-, and Mixed-Eccentricity Fault Diagnoses in Permanent-Magnet Synchronous Motors
انتشار مقاله سال ۲۰۰۹
تعداد صفحات مقاله انگلیسی  ۱۳ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) JCR – master journal list
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۷٫۰۵۰ در سال ۲۰۱۷
رشته های مرتبط مهندسی برق – مهندسی کامپیوتر
گرایش های مرتبط  الکترونیک قدرت – برق قدرت – هوش مصنوعی
نوع ارائه مقاله
ژورنال یا کنفرانس
مجله / کنفرانس Transactions on Industrial Electronics
دانشگاه School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
کلمات کلیدی دامنه نوسان اجزای جانبی (ASBC)، شبکه عصبی مصنوعی (ANN)، برون مرکزی پویا (DE) و برون مرکزی ترکیبی، تشخیص خطا، نویز گاوسی، تشخیص الگو، مغناطیس دائمی (PM)، موتور همزمان (PMSM)، استاتیک، روش اجزاء محدود (FE) گام زمانی (FEM) (TSFEM)
کلمات کلیدی انگلیسی —Amplitude of sideband components (ASBC), artificial neural network (ANN), dynamic eccentricity (DE) and mixed eccentricity (ME), fault diagnosis, Gaussian noise, pattern recognition, permanent-magnet (PM) synchronous motor (PMSM), static, time-stepping finite-element (FE) method (FEM) (TSFEM
شناسه دیجیتال – doi
https://doi.org/10.1109/TIE.2009.2029577
کد محصول E11666
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
I.Introduction
II.Time-Stepping FE Modeling
III.Experimental Setup
IV.SE, DE, and ME Fault Diagnoses
V.Load Effects on the Proposed Index
VI.Theoretical Analysis of the Introduced Index
VII.Analysis of the Proposed Indices for Estimating the Type and Degree of Eccentricity
VIII.Eccentricity Severity Estimation
IX.Discrimination between Eccentricity and Other Faults Using the Introduced Index
X.Conclusion

 

بخشی از متن مقاله:

Abstract

Mixed-eccentricity (ME) fault diagnosis has not been so far documented for permanent-magnet (PM) synchronous motors (PMSMs). This paper investigates how the static eccentricity (SE), dynamic eccentricity (DE), and ME in three-phase PMSMs can be detected. A novel index for noninvasive diagnosis of these eccentricities is introduced for a faulty PMSM. The nominated index is the amplitude of sideband components with a particular frequency pattern which is extracted from the spectrum of stator current. Using this index makes it possible to determine the occurrence, as well as the type and percentage, of eccentricity precisely. Meanwhile, the current spectrum of the faulty PMSM during a large span is inspected, and the ability of the proposed index is exhibited to detect eccentricity in faulty PMSMs with different loads. A novel theoretical scrutiny based on a magnetic field analysis is presented to prove the introduced index and generalize the illustrated fault recognition method. To show the merit of this index in the eccentricity detection and estimation of its severity, first, the correlation between the index and the SE and DE degrees is determined. Then, the type of the eccentricity is determined by a k-nearest neighbor classifier. At the next step, a three-layer artificial neural network is employed to estimate the eccentricity degree and its type. After all, a white Gaussian noise is added to the simulated current, and the robustness of the proposed index is analyzed with respect to the noise variance. In this paper, the PMSM under magnetic fault (demagnetization) and electrical faults (short and open circuits) is modeled, and the current spectrum of the faulty PMSM under demagnetization, short circuit, and open circuit faults is analyzed. It is demonstrated that the proposed index, due to eccentricity fault, is not generated in the current spectrum due to magnetic and electrical faults. Indeed, it is exposed that the introduced index is only created due to eccentricity fault and it is not sensitive to other faults. To model the PMSM eccentricities, a time-stepping finite-element method, which takes into account all geometrical and physical characteristics of the machine components, nonuniform permeance of the air gap, and nonuniform characteristics of the PM material, is employed. This model facilitates the access to the demanded signals in order to have accurate processing. A comparison of simulation and experimental results validate the proposed index.

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