scholarly journals Robust air data sensor fault diagnosis with enhanced fault sensitivity using moving horizon estimation

Author(s):  
Yiming Wan ◽  
Tamas Keviczky ◽  
Michel Verhaegen
2021 ◽  
Author(s):  
N. Cartocci ◽  
F. Crocetti ◽  
G. Costante ◽  
P. Valigi ◽  
M.R. Napolitano ◽  
...  

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 20305-20317
Author(s):  
Shenglei Zhao ◽  
Jiming Li ◽  
Xuezhen Cheng

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2248
Author(s):  
Dimitrios A. Papathanasopoulos ◽  
Konstantinos N. Giannousakis ◽  
Evangelos S. Dermatas ◽  
Epaminondas D. Mitronikas

A non-invasive technique for condition monitoring of brushless DC motor drives is proposed in this study for Hall-effect position sensor fault diagnosis. Position sensor faults affect rotor position feedback, resulting in faulty transitions, which in turn cause current fluctuations and mechanical oscillations, derating system performance and threatening life expectancy. The main concept of the proposed technique is to detect the faults using vibration signals, acquired by low-cost piezoelectric sensors. With this aim, the frequency spectrum of the piezoelectric sensor output signal is analyzed both under the healthy and faulty operating conditions to highlight the fault signature. Therefore, the second harmonic component of the vibration signal spectrum is evaluated as a reliable signature for the detection of misalignment faults, while the fourth harmonic component is investigated for the position sensor breakdown fault, considering both single and double sensor faults. As the fault signature is localized at these harmonic components, the Goertzel algorithm is promoted as an efficient tool for the harmonic analysis in a narrow frequency band. Simulation results of the system operation, under healthy and faulty conditions, are presented along with the experimental results, verifying the proposed technique performance in detecting the position sensor faults in a non-invasive manner.


Author(s):  
Honghui Dong ◽  
Fuzhao Chen ◽  
zhipeng wang ◽  
Limin Jia ◽  
Yong Qin ◽  
...  

Author(s):  
V. Kamatchi Kannan ◽  
R. Srimathi ◽  
V. Gomathi ◽  
R. Valarmathi ◽  
L.T. PrithiEkammai

2016 ◽  
Vol 3 (1-2) ◽  
pp. 1-248 ◽  
Author(s):  
Vasso Reppa ◽  
Marios M Polycarpou ◽  
Christos G. Panayiotou
Keyword(s):  

2011 ◽  
Vol 25 (7) ◽  
pp. 2733-2744 ◽  
Author(s):  
Reza Sharifi ◽  
Reza Langari
Keyword(s):  

2011 ◽  
Vol 2-3 ◽  
pp. 117-122 ◽  
Author(s):  
Peng Peng Qian ◽  
Jin Guo Liu ◽  
Wei Zhang ◽  
Ying Zi Wei

Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.


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