Jawad Faiz

Bio: Jawad Faiz is an academic researcher from University of Tehran. The author has contributed to research in topics: Induction motor & Stator. The author has an hindex of 43, co-authored 327 publications receiving 6781 citations. Previous affiliations of Jawad Faiz include Texas A&M University & University of Tabriz.

Static-, Dynamic-, and Mixed-Eccentricity Fault Diagnoses in Permanent-Magnet Synchronous Motors

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.

Advanced Eccentricity Fault Recognition in Permanent Magnet Synchronous Motors Using Stator Current Signature Analysis

Abstract: In this paper, a novel index is introduced for static and dynamic eccentricity fault diagnosis in permanent magnet synchronous motors (PMSMs). The proposed index is a linear combination of the energy, shape factor, peak, head angle of the peak, area below the peak, gradient of the peak of the detail signals in wavelet decomposition, and coefficients of the autoregressive model, which are extracted from the stator current signature analysis. Principal component analysis is applied to the features as the linear transform for dimension reduction and elimination of linear dependence between the features. In order to demonstrate the capability of these indexes to estimate eccentricity type and degree, the fuzzy support vector machine is employed as a classifier. Classification of the results indicates that the nominated index can be utilized to detect eccentricity occurrence, recognize its type, and determine its degree precisely. Since extraction of efficient indexes closely depends on precise computation of necessary signals, the time-stepping finite element method is utilized to model the PMSM under eccentricity fault and calculate the stator currents as a proper signal for processing. Simulation results are verified by the experimental results.

Extension of winding function theory for nonuniform air gap in electric machinery

Abstract: This paper extends the winding function theory for nonuniform air gap in rotating electric machinery. It shows that the winding function differs from that used in the symmetrical case, although several papers employ the uniform air-gap winding function to study electric motor performance under fault conditions. The extended theory will be particularly helpful in the study of squirrel-cage induction motors with a nonuniform air gap such as that caused by eccentricity of the rotor and stator.

Dissolved gas analysis evaluation in electric power transformers using conventional methods a review

Abstract: Transformers are the most important equipment in power systems, and their failure can cause serious problems. In order to avoid hazardous operating conditions and reduce outage rates, fault detection in the incipient stage is necessary. Incipient faults cause thermal or/and electrical stresses on the transformer with a major consequence on insulation decomposition. The insulation decomposition causes the evolution of gases which can be dissolved in oil. Dissolved gas analysis (DGA) interpretation is one of the main techniques used for fault diagnosis in oil-immersed transformers. In this paper, DGA interpretation is evaluated in detecting different faults and the techniques considered as conventional methods of DGA are investigated. The evaluation is based on DGA data obtained from oil samples of real transformers.

Feature Extraction for Short-Circuit Fault Detection in Permanent-Magnet Synchronous Motors Using Stator-Current Monitoring

Abstract: In this paper, a novel frequency pattern and competent criterion are introduced for short-circuit-fault recognition in permanent-magnet synchronous motors (PMSMs). The frequency pattern is extracted from the monitored stator current analytically and the amplitude of sideband components at these frequencies is introduced as a proper criterion to determine the number of short-circuited turns. Impacts of the load variation on the proposed criterion are investigated in the faulty PMSM. In order to demonstrate the aptitude of the proposed criterion for precise short-circuit fault detection, the relation between the nominated criterion and the number of short-circuited turns is specified by the mutual information index. Therefore, a white Gaussian noise is added to the simulated stator current and robustness of the criterion is analyzed with respect to the noise variance. The occurrence and the number of short-circuited turns are predicted using support-vector machine as a classifier. The classification results indicate that the introduced criterion can detect the short-circuit fault incisively. Simulation results are verified by the experimental results.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Introduction to the Finite Element Method

Abstract: This chapter introduces the finite element method (FEM) as a tool for solution of classical electromagnetic problems. Although we discuss the main points in the application of the finite element method to electromagnetic design, including formulation and implementation, those who seek deeper understanding of the finite element method should consult some of the works listed in the bibliography section.

Advances in Diagnostic Techniques for Induction Machines

Abstract: This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.