scholarly journals From Modeling to Failure Prognosis of Permanent Magnet Synchronous Machine

2020 ◽  
Vol 10 (2) ◽  
pp. 691 ◽  
Author(s):  
Riham Ginzarly ◽  
Ghaleb Hoblos ◽  
Nazih Moubayed
Keyword(s):  
Finite Element ◽  
Permanent Magnet ◽  
Manufacturing Sector ◽  
Short Circuit ◽  
Renewable Energy Sources ◽  
Remaining Useful Life ◽  
Electrical Machine ◽  
Permanent Magnet Machines ◽  
Detection And Identification ◽  
Fault Detection And Identification

Due to the accelerating pace of environmental concerns and fear of the depletion of conventional sources of energy, researchers are working on finding renewable energy sources of power for different axes of life. The transportation sector has intervened in this field and introduced hybrid electric vehicles. Many complaints have been mentioned concerning fault detection and identification in the vehicle to ensure its safety, reliability and availability. Diagnosis has not been able to overcome all these concerns, and research has shifted toward prognosis, where the manufacturing sector is urged to integrate fault prognosis in the vehicle’s electrical powertrain. In this article, prognosis of the vehicle’s electrical machine is treated using a hidden Markov model after modeling the electrical machine using the finite element method. Permanent magnet machines are preferable in this application. The modeling of the machine is a combination of the electromagnetic, thermal and vibration finite element models. The considered faults are demagnetization, turn-to-turn short circuit and eccentricity. A strategy for the calculation of the remaining useful life (RUL) is suggested when a turn-to-turn short circuit fault occurs.

scholarly journals A Design Method for the Cogging Torque Minimization of Permanent Magnet Machines with a Segmented Stator Core Based on ANN Surrogate Models

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1880
Author(s):  
Elia Brescia ◽  
Donatello Costantino ◽  
Paolo Roberto Massenio ◽  
Vito Giuseppe Monopoli ◽  
Francesco Cupertino ◽  
...  
Keyword(s):  
Finite Element ◽  
Permanent Magnet ◽  
Design Method ◽  
Heuristic Approach ◽  
Topological Optimization ◽  
Permanent Magnet Machines ◽  
Stator Core ◽  
Cogging Torque ◽  
Design Formula ◽  
Harmonic Components

Permanent magnet machines with segmented stator cores are affected by additional harmonic components of the cogging torque which cannot be minimized by conventional methods adopted for one-piece stator machines. In this study, a novel approach is proposed to minimize the cogging torque of such machines. This approach is based on the design of multiple independent shapes of the tooth tips through a topological optimization. Theoretical studies define a design formula that allows to choose the number of independent shapes to be designed, based on the number of stator core segments. Moreover, a computationally-efficient heuristic approach based on genetic algorithms and artificial neural network-based surrogate models solves the topological optimization and finds the optimal tooth tips shapes. Simulation studies with the finite element method validates the design formula and the effectiveness of the proposed method in suppressing the additional harmonic components. Moreover, a comparison with a conventional heuristic approach based on a genetic algorithm directly coupled to finite element analysis assesses the superiority of the proposed approach. Finally, a sensitivity analysis on assembling and manufacturing tolerances proves the robustness of the proposed design method.

scholarly journals Cogging force reduction in linear tubular flux switching permanent-magnet machines

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 243-248
Author(s):  
Doudou Sarr Lo ◽  
Yacine Amara ◽  
Georges Barakat ◽  
Ferhat Chabour
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Energy Conversion ◽  
Permanent Magnet ◽  
Permanent Magnet Machines ◽  
Free Piston ◽  
Force Reduction ◽  
Element Method

Abstract The aim of this paper is to explore the possibility of using linear tubular flux switching permanent magnet machines in a free piston energy conversion (FPEC) system. In FPEC systems, acceleration and therefore speed are often relatively high, which impose to have a reduced number of poles, meanwhile the cogging force will be relatively high. In order to reduce the cogging force two techniques are combined. The analysis is done using finite element method.

scholarly journals Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Aerospace ◽  
2019 ◽  
Vol 6 (9) ◽  
pp. 94 ◽  
Author(s):  
Matteo D. L. Dalla Vedova ◽  
Alfio Germanà ◽  
Pier Carlo Berri ◽  
Paolo Maggiore
Keyword(s):  
Fault Detection ◽  
Flight Control ◽  
Failure Modes ◽  
Numerical Models ◽  
Short Circuit ◽  
Fault Detection And Isolation ◽  
Model Based ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Functional Components

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.

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