Reliable H∞ filtering for the SP resonant ICPT system with stochastic multiple sensor faults

2021 ◽  
Vol 42 ◽  
pp. 101082
Author(s):  
Shuangxin Zhu ◽  
Engang Tian ◽  
Jinliang Liu ◽  
Hongtian Chen
2008 ◽  
Vol 2008 ◽  
pp. 1-10
Author(s):  
S.S. Yang ◽  
J. Chen

This paper presents an enhanced robust control design structure to realise fault tolerance towards sensor faults suitable for multi-input-multi-output (MIMO) systems implementation. The proposed design permits fault detection and controller elements to be designed with considerations to stability and robustness towards uncertainties besides multiple faults environment on a common mathematical platform. This framework can also cater to systems requiring fast responses. A design example is illustrated with a fast, multivariable and unstable system, that is, the double inverted pendulum system. Results indicate the potential of this design framework to handle fast systems with multiple sensor faults.


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.


1999 ◽  
Vol 23 ◽  
pp. S585-S588 ◽  
Author(s):  
A. Aïtouche ◽  
F. Busson ◽  
B. Ould Bouamama ◽  
M. Staroswiecki

Author(s):  
S. O T. Ogaji ◽  
R Singh

A diagnostic framework has been developed for the detection of faults in the gas path of a three-shaft aeroderivative gas turbine thermodynamically similar to the Rolls Royce RB211-24GT. The framework involves a large-scale integration of artificial neural networks (ANNs) designed and trained to detect, isolate and assess faults in the gas path components of the engine. The approach has the capacity to assess both multiple-component and multiple-sensor faults. The results obtained demonstrate the promise of ANNs applied to engine diagnostic activities.


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