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This paper presents an improved magnetic equivalent circuit (MEC) method for modeling linear permanent-magnet synchronous motors (LPMSMs) with adjustable accuracy. The performance of machine with different dimensions, poles and slot numbers can be studied by the proposed flexible MEC, where the core nonlinearity is fitted on the material B–H curve. End effect is modeled by considering two virtual zones with desired accuracy at both entrance and exit ends of the primary. A new structure based on magnets segmentation is also proposed to investigate its effect on the motor performance. Finally, the results of the proposed method are compared with 3D-FEM to show the effectiveness of the presented model. The results show improvement in processing time with good accuracy compared to previous classic methods. In general, introducing a new model based on an improved MEC approach for modeling LPMSMs considering slot effect, iron core saturation and segmented PMs with flexible accuracy by adjusting the number of flux tubes is the paper novelty which is studied in this work.

Purpose – The purpose of this paper is to obtain an integrated method for inter-turn short circuit fault detection for the cage-rotor induction machine (CRIM) considering saturation effect. Design/methodology/approach – The magnetic equivalent circuit (MEC) is proposed for machine modeling and nonlinear B-H curve is considered for saturation effect. The machine has some differential equations which are converted to algebraic type by trapezoidal method. On the other hand, some nonlinear equations are present due to saturation effect. A set of nonlinear algebraic equation should be solved by numerical method. Therefore, the Newton-Raphson technique is used for equation solving during of the considered time step. Findings – Generally, the operating point of electrical machines is close to the saturation zone due to designing considerations. Moreover, some current and torque harmonics will be produced due to time and space harmonics combination, which cannot be studied when saturation modeling is neglected. Considering both space and time harmonics, a method is proposed for inter-turn short circuit fault detection based on the stator current signatures and the machine performance is analyzed in healthy and faulty cases. In order to obtain the integrated method, two sample machines (two and also four-pole machines) are modeled and finally the accuracy of the proposed method is verified through the experimental results. Research limitations/implications – The calculations have been done in this work is limited to CRIM considering. However, the presented modeling method can be used for another types of electrical machines by some minor modifications. Originality/value – Obtaining of an integrated formula for the inter-turn short circuit fault detection which has been presented for first time is the more advantages of present work. Moreover, in order to saturation effect considering, a new method is presented for solving of nonlinear equations which is another novelty of paper.

Summary In this research, considering slot opening and distributed winding effects, torque ripple analysis is studied for four separate synchronous reluctance machines (SynRMs) under health and eccentricity fault conditions using a mathematical method. A finite element method (FEM) validated model is produced by mathematical modeling, and numerical method is used for the machine's behavior studying with voltage source supplying. With this aim, the machine's parameters have been computed under health and eccentricity fault conditions, using modified winding function theory (MWFT). A method of dynamic and static eccentricity fault diagnosis is proposed, which uses machine vibration analysis, and the results suggest that the eccentricity fault does not have any significant effect on the machine's currents. Moreover, unbalance magnetic pull (UMP) is computed under eccentricity fault conditions and mechanical un‐stability is shown. Mathematical base model and voltage source machine supply and also presenting a discrete machine modeling method are the main advantages of this paper compared with other similar works that are usually FEM‐based studies or have been based on current source power supply. The research is applicable to healthy and faulty machine behavior, and the methodical modeling is obviously applicable to other machines. Copyright © 2015 John Wiley & Sons, Ltd.

This paper presents a comprehensive approach for modeling and classification of air gap asymmetry and inter-turn short circuit faults in Ladder-Secondary Linear Induction Motors (LS-LIMs). It is based on a modified Magnetic Equivalent Circuit (MEC) model incorporated with a current signal-based fault detection method using Convolution Neural Network (CNN). The feature sets of the mentioned faults are classified separately by a CNN, and the training and test data are extracted using three-phase currents obtained from MEC. For this purpose, both healthy and faulty motors are modeled initially by the proposed MEC model to generate different labeled data for training the designed CNN. Tt is also shown that fault diagnosis of this motor by Fast Fourier Transform (FFT) is not possible. Finally, the proposed networks are trained based on the obtained currents from Finite Element Method (FEM) to validate their accuracy. Since faults diagnosis in LS-LIMs based on CNN has not been introduced in the relevant literature so far, it is presented in this paper for the first time.

This paper presents a novel technique for synchronous reluctance machines model (SynRMs). The model can be used in both healthy and faulty situations with phase-to-phase fault. The machine properties such as the number of poles and slots, as well as rotor and stator dimension, can be selected arbitrarily in the proposed model. Therefore, various SynRMs with selective properties can be modeled by the proposed method. Considering the nonlinear magnetic equivalent circuit, the healthy and faulty saturable machine with phase-to-phase fault between various windings is also studied. Validation is performed by finite-element method (FEM). Regarding the performed validation, very shorter processing time is obtained compared to FEM.

In this paper, an analytical model is proposed for evaluating electromagnetic performances of Permanent Magnet Vernier Machines (PMVMs) under healthy and faulty conditions. The proposed model employs flexible Magnetic Equivalent Circuit (MFC) method, whose accuracy can be determined by tunable parameters. The model is capable of considering the influence of saturation effect, skewed slots, slot leakage fluxes, and various winding arrangements for the machines with desired properties. First, the proposed model is used to predict the no-load performance of a machine in a healthy condition. Then, the machine loading behaviors under healthy and demagnetization fault conditions are analyzed by the MFC model. Moreover, the results of the proposed model are compared and validated with those of 2D Finite Element Method (FEM) and 3D-FEM. Eventually, a specific pattern is extracted from the stator current spectrum to detect the demagnetization fault.

Purpose The purpose of this paper is to propose a saturable model based on the magnetic equivalent circuit (MEC) for evaluating the electromagnetic performance of the variable area resolver. Design/methodology/approach The equivalent circuit is developed where three different reluctance types are used to calculate permeances based on geometrical approximations. The proposed model typically has two types of equations, including the magnetic and electrical equations. The magnetic and electrical equations are related to the resolver core and the windings, respectively. Applying the well-known trapezoidal method, the magnetic and electrical equations can be simultaneously solved. A nonlinearity of the magnetic equations, the algebraic equations system, which is obtained from Kirchhoff’s laws, should be solved by the Newton-Raphson technique in each step-time. Findings The flexible MEC model, in which the number of flux tubes in different parts of the resolver can be arbitrarily selected, is proposed to analyze the variable reluctance resolver. Besides, the design parameters such as geometrical dimensions, windings arrangement and a number of the rotor saliencies can be chosen as desired. To consider the effect of time harmonics, a new nonlinear function is used for the core magnetization. Furthermore, different winding layouts can be implemented in the model to take space harmonics into account. The model obtained results are compared with the finite element method in terms of accuracy and simulation time. Originality/value Generally, the accuracy of the predictions in the MEC method is dependent on the number of flux tubes; therefore, the flexibility of the proposed MEC model in its capability to choose the desired number of flux paths is the advantage of this work. Moreover, the proposed model can analyze both wound and saliency rotor resolvers by changing the design parameters.

Purpose The aim of this paper is to propose the model for analyzing the electromagnetic performances of permanent magnet vernier machines (PMVMs) under healthy and faulty conditions. Design/methodology/approach The model uses interconnected reluctance network formed based on the geometrical approximations to predict magnetic performances of the machine. The network consists of several types of reluctances for modeling different parts of machine. Applying Kirchhoffs laws in the network and the machine windings, magnetic and electrical equations are obtained, respectively. To construct the model system of equations, the electrical equation is converted into algebraic form by using the trapezoidal technique. Moreover, the system of equations must be solved by Newton–Raphson method in each step-time because of considering the core saturation effect. Findings The proposed model is developed based on the modified magnetic equivalent circuit (MEC) method, in which the number of flux paths in different parts of the machine can be arbitrary selected. The saturation effect, skewed slots, the desired machine geometrical parameters and various winding arrangements are included in the proposed model; therefore, it can evaluate the time and space harmonics in modeling the PMVMs. Furthermore, a pattern for inter-turn fault detection is extracted from the stator current spectrum. Finally, 2 D-finite element method (FEM) and 3 D-FEM analysis are carried out to evaluate and verify the results of the proposed MEC model. Originality/value Generally, the element numbers have important role in modeling the machine and calculating its performance. Hence, the proposed MEC model’s capability to choose desired number of flux paths is advantage of this paper. Moreover, the developed MEC can be used for analyzing several electrical machines, including other types of vernier machines, with simple modification.

The COVID-19 is rapidly scattering worldwide, and the number of cases in the Eastern Mediterranean Region is rising. Thus, there is a need for immediate targeted actions. We designed a longitudinal study in a hot outbreak zone to analyze the serial findings between infected patients for detecting temporal changes from February 2020. In a hospital-based open-cohort study, patients are followed from admission until one year from their discharge (the 1st, 4th, 12th weeks, and the first year). The patient recruitment phase finished at the end of August 2020, and the follow-up continues by the end of August 2021. The measurements included demographic, socio-economics, symptoms, health service diagnosis and treatment, contact history, and psychological variables. The signs improvement, death, length of stay in hospital were considered primary, and impaired pulmonary function and psychotic disorders were considered main secondary outcomes. Moreover, clinical symptoms and respiratory functions are being determined in such follow-ups. Among the first 600 COVID-19 cases, 490 patients with complete information (39% female; the average age of 57±15 years) were analyzed. Seven percent of these patients died. The three main leading causes of admission were: fever (77%), dry cough (73%), and fatigue (69%). The most prevalent comorbidities between COVID-19 patients were hypertension (35%), diabetes (28%), and ischemic heart disease (14%). The percentage of primary composite endpoints (PCEP), defined as death, the use of mechanical ventilation, or admission to an intensive care unit was 18%. The Cox Proportional-Hazards Model for PCEP indicated the following significant risk factors: Oxygen saturation < 80% (HR = 6.3; [CI 95%: 2.5,15.5]), lymphopenia (HR = 3.5; [CI 95%: 2.2,5.5]), Oxygen saturation 80%-90% (HR = 2.5; [CI 95%: 1.1,5.8]), and thrombocytopenia (HR = 1.6; [CI 95%: 1.1,2.5]). This long-term prospective Cohort may support healthcare professionals in the management of resources following this pandemic.