Fault Tolerant Control of an Industrial Gas Turbine Based on a Hybrid Fuzzy Adaptive Unscented Kalman Filter

2013 ◽  
Vol 135 (12) ◽  
Author(s):  
Amin Mirzaee ◽  
Karim Salahshoor
Keyword(s):  
Kalman Filter ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Unscented Kalman Filter ◽  
Fault Tolerant ◽  
Fuzzy Model ◽  
Fault Tolerant Control ◽  
The Core ◽  
Adaptive Unscented Kalman Filter ◽  
Industrial Gas Turbine

Gas turbines are generally used in power generation, the oil and gas industries, and as jet engines in aircrafts. Fault tolerance and reliability is important in such applications. Thus, accurate modeling and control system design is necessary. In this paper, first a nonlinear hybrid fuzzy model was developed for an industrial gas turbine, and then this model was used as the core of a fault tolerant control (FTC) system. The aforementioned model was trained by use of three months of operational data of a GE MS 5002 D gas turbine that is used for gas injection application, then it was fine tuned using expert knowledge and physical principles. A graphical user interface (GUI) was also developed to run various realistic operational scenarios of the gas turbine. The main point of the present work consists in introducing nonlinear fuzzy model schemes as the core of an adaptive unscented Kalman filter (AUKF) for fault diagnostic purposes. Analysis of the simulation results discloses that this FTC approach alleviates the effects of faults in two different scenarios such as sequential drift and bias in sensors/actuators and also in simultaneous faults that are a disastrous situation.

A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter

2017 ◽  
Vol 66 ◽  
pp. 262-274 ◽  
Author(s):  
Yashar Shabbouei Hagh ◽  
Reza Mohammadi Asl ◽  
Vincent Cocquempot
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Fault Tolerant ◽  
Fault Tolerant Control

Sensor fault–tolerant control of a quadrotor unmanned aerial vehicle

2021 ◽  
pp. 095441002110155
Author(s):  
Mehmet Gokberk Patan ◽  
Fikret Caliskan
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Unmanned Aerial Vehicle ◽  
Unscented Kalman Filter ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Sensor Faults ◽  
Cubature Kalman Filter ◽  
Non Linear ◽  
Aerial Vehicle

This article handles the issue of fault-tolerant control of a quadrotor unmanned aerial vehicle (UAV) in the existence of sensor faults. A general non-linear model of the quadrotor is presented. Several non-linear Kalman filters namely, the extended Kalman filter, the unscented Kalman filter and the cubature Kalman filter (CKF) are utilized to estimate the states of the quadrotor and to compare the estimation performances. Some flight scenarios are simulated, and the simulation results show that the CKF has the smallest estimation error as expected in theory. Control of the quadrotor heavily depends on the measured values received from sensors. Therefore, the control system requires fault-free sensors. However, small quadrotors and UAVs are mostly equipped with low-cost and low-quality sensors, and hence, they may fail to indicate correct measurement values. If the sensors are faulty, then the control system itself should be actively tolerant to sensor faults. Measurements of these kinds of sensors suffer from bias and external noise due to temperature variations, vibration and other external conditions. Since the bias is one of the very common faults in these sensors, a sensor bias is taken into consideration as a fault and occurs abruptly at a certain time and continues throughout the considered scenarios. By using the residual signals generated by the non-linear filters, sensor faults are detected and isolated. Then, two different methods are proposed for removing the effects of faults and achieving active fault–tolerant control. The effectiveness of the presented two techniques is shown in the simulations.

scholarly journals Fault Isolation for an Industrial Gas Turbine With a Model-Based Diagnosis Approach

2010 ◽  
Author(s):  
Emil Larsson ◽  
Jan A˚slund ◽  
Erik Frisk ◽  
Lars Eriksson
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Fault Tolerant ◽  
Final Diagnosis ◽  
Fault Isolation ◽  
Observer Design ◽  
Model Based ◽  
Model Size ◽  
Diagnosis Model ◽  
Industrial Gas Turbine

Model based diagnosis and supervision of industrial gas turbines are studied. Monitoring of an industrial gas turbine is important as it gives valuable information for the customer about service performance and process health. The overall objective of the paper is to develop a systematic procedure for modelling and design of a model based diagnosis system, where each step in the process can be automated and implemented using available software tools. A new Modelica gas media library is developed, resulting in a significant model size reduction compared to if standard Modelica components are used. A systematic method is developed that, based on the diagnosis model, extracts relevant parts of the model and transforms it into a form suitable for standard observer design techniques. This method involves techniques from simulation of DAE models and a model reduction step. The size of the final diagnosis model is 20% of the original model size. Combining the modeling results with fault isolation techniques, simultaneous isolation of sensor faults and fault tolerant health parameter estimation is achieved.

scholarly journals Atmospheric PM2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 607
Author(s):  
Jihan Li ◽  
Xiaoli Li ◽  
Kang Wang ◽  
Guimei Cui
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Support Vector ◽  
Multiple Model ◽  
Single Model ◽  
Adaptive Unscented Kalman Filter ◽  
Time Periods ◽  
Atmospheric Pm2.5 ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

The PM2.5 concentration model is the key to predict PM2.5 concentration. During the prediction of atmospheric PM2.5 concentration based on prediction model, the prediction model of PM2.5 concentration cannot be usually accurately described. For the PM2.5 concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM2.5 concentration may be different, and the single model cannot play the corresponding ability to predict PM2.5 concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM2.5 concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM2.5 concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM2.5 concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM2.5 concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM2.5 concentration prediction is improved in whole time period.

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