scholarly journals Fault Detection Method Research of Three-phase Asynchronous Motor

Author(s):  
Miao Shang ◽  
Yongtao Sun ◽  
Min Ji ◽  
Jie Chen
Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1437
Author(s):  
Sang-Hun Kim ◽  
Seok-Min Kim ◽  
Sungmin Park ◽  
Kyo-Beum Lee

This paper proposes a fault-detection method for open-switch failures in hybrid active neutral-point-clamped (HANPC) rectifiers. The basic HANPC topology comprises two SiC-based metal-oxide-semiconductor field-effect transistors (MOSFETs) and four Si insulated-gate bipolar transistors (IGBTs). A three-phase rectifier system using the HANPC topology can produce higher efficiency and lower current harmonics. An open-switch fault in a HANPC rectifier can be a MOSFET or IGBT fault. In this work, faulty cases of six different switches are analyzed based on the current distortion in the stationary reference frame. Open faults in MOSFET switches cause immediate and remarkable current distortions, whereas, open faults in IGBT switches are difficult to detect using conventional methods. To detect an IGBT fault, the proposed detection method utilizes some of the reactive power in a certain period to make an important difference, using the direct-quadrant (dq)-axis current information derived from the three-phase current. Thus, the proposed detection method is based on three-phase current measurements and does not use additional hardware. By analyzing the individual characteristics of each switch failure, the failed switch can be located exactly. The effectiveness and feasibility of the proposed fault-detection method are verified through PSIM simulations and experimental results.


2018 ◽  
Vol 2018 (13) ◽  
pp. 524-528 ◽  
Author(s):  
Zhihuang Wei ◽  
Weiguo Liu ◽  
Zan Zhang ◽  
Ningfei Jiao ◽  
Jichang Peng ◽  
...  

Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


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