Demagnetization fault detection in axial flux PM machines by using sensing coils and an analytical model

Author(s):  
Jan De Bisschop ◽  
Peter Sergeant ◽  
Luc Dupre
2017 ◽  
Vol 53 (6) ◽  
pp. 1-4 ◽  
Author(s):  
Jan De Bisschop ◽  
Hendrik Vansompel ◽  
Peter Sergeant ◽  
Luc Dupre

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1644 ◽  
Author(s):  
Caixia Gao ◽  
Yanjie Nie ◽  
Jikai Si ◽  
Ziyi Fu ◽  
Haichao Feng

This paper proposes a demagnetization fault detection, mode recognition, magnetic pole positioning, and degree evaluation method for permanent magnet synchronous motors. First, the analytical model of the single-coil no-load back electromotive force (EMF) of demagnetization fault for Permanent magnet synchronous motor (PMSM) arbitrary magnetic poles is established. In the analytical model, the single-coil no-load back EMF residual of the health state and the single magnetic pole sequential demagnetization fault are calculated and normalized. Model results are used as the fault sample database. Second, the energy interval database of the single-coil no-load back EMF residual with different numbers of magnetic pole demagnetization is established. Demagnetization fault detection and degree evaluation are performed by the real-time acquired amplitudes of the single-coil no-load back EMF residual. The number of demagnetization poles is determined by comparing the energy of the single-coil no-load back EMF residual with the energy interval database. Demagnetization mode recognition and magnetic pole positioning are realized by analyzing the correlation coefficients between normalized the single-coil no-load back EMF residual and the fault sample database. Finally, results of analysis of the finite element simulation validate the feasibility and effectiveness of the proposed method.


2012 ◽  
Vol 48 (6) ◽  
pp. 1838-1845 ◽  
Author(s):  
Seyyed Mehdi Mirimani ◽  
Abolfazl Vahedi ◽  
Fabrizio Marignetti ◽  
Enzo De Santis

2021 ◽  
Author(s):  
Christopher Day

A fault in the primary mass flow sensor of an aircraft engine bleed air system can cause significant deterioration of overall system performance. This project uses an analytical model of the bleed air system to create a fault detection and accommodation scheme for the mass flow sensor. The analytical model uses information from the upstream and downstream pressure sensors to predict the output of the mass flow sensor. Faults are detected by comparing the output from the sensor with the predicted output from the analytical model. A fuzzy logic rule base is used to determine the degree of the flow sensor fault. The degree of the sensor fault is used to determine the inaccuracy of the faulty sensor output. A corrected estimation of the flow rate is then created using a weighted algorithm consisting of the predicted flow rate from the analytical model and the flow rate from the faulty sensor. The analytical model is also used to detect and accommodate transient responses from the flow sensor including signal overshoot, oscillations and time constant errors. A MATLAB computer simulation is conducted to evaluate the performance of the bleed air system degrades slightly in the event of a fault of the flow sensor. While the sensor fault will degrade the performance of the bleed air system, the degradation is not significant, and the bleed air system is able to maintain acceptable performance in the presence of faults.


Author(s):  
Hang Zhao ◽  
K.T. Chau ◽  
Tengbo Yang ◽  
Zaixin Song ◽  
Chunhua Liu

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