Multiple sensor fault diagnosis for dynamic processes

2010 ◽  
Vol 49 (4) ◽  
pp. 415-432 ◽  
Author(s):  
Cheng-Chih Li ◽  
Jyh-Cheng Jeng
2018 ◽  
Vol 8 (10) ◽  
pp. 1816 ◽  
Author(s):  
Zhimin Yang ◽  
Yi Chai ◽  
Hongpeng Yin ◽  
Songbing Tao

This paper deals with the current sensor fault diagnosis and isolation (FDI) problem for a permanent magnet synchronous generator (PMSG) based wind system. An observer based scheme is presented to detect and isolate both additive and multiplicative faults in current sensors, under varying torque and speed. This scheme includes a robust residual generator and a fault estimation based isolator. First, the PMSG system model is reformulated as a linear parameter varying (LPV) model by incorporating the electromechanical dynamics into the current dynamics. Then, polytopic decomposition is introduced for H ∞ design of an LPV residual generator and fault estimator in the form of linear matrix inequalities (LMIs). The proposed gain-scheduled FDI is capable of online monitoring three-phase currents and isolating multiple sensor faults by comparing the diagnosis variables with the predefined thresholds. Finally, a MATLAB/SIMULINK model of wind conversion system is established to illustrate FDI performance of the proposed method. The results show that multiple sensor faults are isolated simultaneously with varying input torque and mechanical power.


2012 ◽  
Vol 229-231 ◽  
pp. 1265-1271 ◽  
Author(s):  
Zhi Gang Yao ◽  
Li Cheng ◽  
Qing Lin Wang

This paper provides an overview and analysis of data-driven sensor fault detection, diagnosis and validation from the application viewpoint. The typical sensor fault detection indices in the literature and the fundamental issues of necessary and sufficient conditions for detectability, reconstructability, identifiability and isolatability are analyzed. The main objective is to study the essential and important algorithms and techniques for single or multiple sensor fault diagnosis and validation. The issues of optimal principal components, sensor validity index, maximized sensitivity, as well as robust sensor fault diagnosis, etc. are discussed. Additional focuses are summarized at the end of the paper for future investigation.


2007 ◽  
Vol 40 (5) ◽  
pp. 267-272
Author(s):  
Ahmed Alawi ◽  
Julian Morris

2014 ◽  
Vol 259 ◽  
pp. 346-358 ◽  
Author(s):  
M. El-Koujok ◽  
M. Benammar ◽  
N. Meskin ◽  
M. Al-Naemi ◽  
R. Langari

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

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