scholarly journals The Rotating Components Performance Diagnosis of Gas Turbine Based on the Hybrid Filter

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 819 ◽  
Author(s):  
Li Zeng ◽  
Shaojiang Dong ◽  
Wei Long
Keyword(s):  
Particle Filter ◽  
Gas Turbine ◽  
Mechanical Energy ◽  
Parameters Estimation ◽  
Accurate Estimation ◽  
Method Performance ◽  
Performance Diagnosis ◽  
Strong Tracking Filter ◽  
Tracking Filter ◽  
Health Parameters

Gas turbine converts chemical energy into mechanical energy and provide energy for aircraft, ships, etc. The performance diagnosis of rotating components of gas turbine are essential in terms of the high failure rate of these parts. A problem that the sudden changing of operation state of turbines may lead to the misdiagnosis due to the defect of gas turbine’s model. This paper constructs the strong tracking filter based on the unscented Kalman filter to achieve accurate estimation of gas turbine’s measured parameters when the state changes suddenly. In the strong tracking filter, a parameter optimization method based on the residual similarity of measured parameters is proposed. Next, adopt the measured parameters filtered by the strong tracking filter to construct the health parameters estimation algorithm based on the particle filter. The particle weight is optimized by the mean adjustment method. Performance diagnosis is realized by checking the changes of health parameters output by particle filter. The results show that the proposed method improves the accuracy of performance diagnosis obviously.

A novel method based on least squares support vector regression combing with strong tracking particle filter for machinery condition prognosis

2013 ◽  
Vol 228 (6) ◽  
pp. 1048-1062 ◽  
Author(s):  
Chengliang Li ◽  
Zhongsheng Wang ◽  
Shuhui Bu ◽  
Hongkai Jiang ◽  
Zhenbao Liu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Extend Kalman Filter ◽  
Combining Data ◽  
Novel Method ◽  
Strong Tracking Filter ◽  
Tracking Filter

A reliable prediction method is very important to avoid a catastrophic failure. This paper presents a novel method for machinery condition prognosis, named least squares support vector regression strong tracking particle filter which is based on least squares support vector regression combing with strong tracking particle filter. There are two main contributions in our work: first, the regression function of least squares support vector regression is extended, which constructs a bridge for the application of combining data-driven method with a recursive filter based on extend Kalman filter; second, an extend Kalman filter-based particle filter is studied by introducing a strong tracking filter into a particle filter. The strong tracking filter is used to update particles and produce importance densities which can improve the performance of the particle filter in tracking saltatory states, and finally strong tracking particle filter improves the prediction performance of least squares support vector regression in predicting saltatory states. In the experiment, it can be concluded that the proposed method is better than classical condition predictors in machinery condition prognosis.

scholarly journals A Novel Strong Tracking Fault Prognosis Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Qi Zhang ◽  
Wei Jiang ◽  
Tian-Mei Li ◽  
Jian-Fei Zheng
Keyword(s):  
Particle Filter ◽  
Immune Algorithm ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Fault Prognosis ◽  
Artificial Immunity ◽  
Simulation Results ◽  
Strong Tracking Filter ◽  
Tracking Filter ◽  
Degeneracy Problem

Improving the ability to track abruptly changing states and resolving the degeneracy are two difficult problems to particle filter applied to fault prognosis. In this paper, a novel strong tracking fault prognosis algorithm is proposed to settle the above problems. In the proposed algorithm, the artificial immunity algorithm is first introduced to resolve the degeneracy problem, and then the strong tracking filter is introduced to enhance the ability to track abruptly changing states. The particles are updated by strong tracking filter, and better particles are selected by utilizing the artificial immune algorithm to estimate states. As a result, the degeneracy problem is resolved and the accuracy of the proposed fault prognosis algorithm is improved accordingly. The feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the DTS200 system.

Sensor Fusion Based on Strong Tracking Filter for Augmented Reality Registration

2011 ◽  
Vol 467-469 ◽  
pp. 108-113
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Augmented Reality ◽  
Inertial Sensor ◽  
Measurement Data ◽  
Sensor Data ◽  
Data Synchronization ◽  
Error Covariance ◽  
Tightly Coupled ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter

Accurate tracking for Augmented Reality applications is a challenging task. Multi-sensors hybrid tracking generally provide more stable than the effect of the single visual tracking. This paper presents a new tightly-coupled hybrid tracking approach combining vision-based systems with inertial sensor. Based on multi-frequency sampling theory in the measurement data synchronization, a strong tracking filter (STF) is used to smooth sensor data and estimate position and orientation. Through adding time-varying fading factor to adaptively adjust the prediction error covariance of filter, this method improves the performance of tracking for fast moving targets. Experimental results show the efficiency and robustness of this proposed approach.

scholarly journals Simulation and optimization integrated gasification combined cycle by used aspen hysys and aspen plus

2015 ◽  
Vol 3 (1) ◽  
pp. 178
Author(s):  
Mohsen Darabi ◽  
Mohammad Mohammadiun ◽  
Hamid Mohammadiun ◽  
Saeed Mortazavi ◽  
Mostafa Montazeri
Keyword(s):  
Gas Turbine ◽  
Coal Gasification ◽  
Aspen Plus ◽  
Combined Cycle ◽  
Parameters Estimation ◽  
Integrated Gasification Combined Cycle ◽  
Support Tool ◽  
Simulation And Optimization ◽  
Aspen Hysys ◽  
Integrated Gasification

<p>Electricity is an indispensable amenity in present society. Among all those energy resources, coal is readily available all over the world and has risen only moderately in price compared with other fuel sources. As a result, coal-fired power plant remains to be a fundamental element of the world's energy supply. IGCC, abbreviation of Integrated Gasification Combined Cycle, is one of the primary designs for the power-generation market from coal-gasification. This work presents a in the proposed process, diluted hydrogen is combusted in a gas turbine. Heat integration is central to the design. Thus far, the SGR process and the HGD unit are not commercially available. To establish a benchmark. Some thermodynamic inefficiencies were found to shift from the gas turbine to the steam cycle and redox system, while the net efficiency remained almost the same. A process simulation was undertaken, using Aspen Plus and the engineering equation solver (EES).The The model has been developed using Aspen Hysys® and Aspen Plus®. Parts of it have been developed in Matlab, which is mainly used for artificial neural network (ANN) training and parameters estimation. Predicted results of clean gas composition and generated power present a good agreement with industrial data. This study is aimed at obtaining a support tool for optimal solutions assessment of different gasification plant configurations, under different input data sets.</p>

Kinetic parameters estimation in an anaerobic digestion process using successive quadratic programming

2005 ◽  
Vol 52 (1-2) ◽  
pp. 419-426 ◽  
Author(s):  
C.A. Aceves-Lara ◽  
E. Aguilar-Garnica ◽  
V. Alcaraz-González ◽  
O. González-Reynoso ◽  
J.P. Steyer ◽  
...  
Keyword(s):  
Experimental Data ◽  
Anaerobic Digestion ◽  
Quadratic Programming ◽  
Kinetic Parameters ◽  
Parameters Estimation ◽  
Fixed Bed Reactor ◽  
Accurate Estimation ◽  
Successive Quadratic Programming ◽  
Anaerobic Digestion Process ◽  
Digestion Process

In this work, an optimization method is implemented in an anaerobic digestion model to estimate its kinetic parameters and yield coefficients. This method combines the use of advanced state estimation schemes and powerful nonlinear programming techniques to yield fast and accurate estimates of the aforementioned parameters. In this method, we first implement an asymptotic observer to provide estimates of the non-measured variables (such as biomass concentration) and good guesses for the initial conditions of the parameter estimation algorithm. These results are then used by the successive quadratic programming (SQP) technique to calculate the kinetic parameters and yield coefficients of the anaerobic digestion process. The model, provided with the estimated parameters, is tested with experimental data from a pilot-scale fixed bed reactor treating raw industrial wine distillery wastewater. It is shown that SQP reaches a fast and accurate estimation of the kinetic parameters despite highly noise corrupted experimental data and time varying inputs variables. A statistical analysis is also performed to validate the combined estimation method. Finally, a comparison between the proposed method and the traditional Marquardt technique shows that both yield similar results; however, the calculation time of the traditional technique is considerable higher than that of the proposed method.

scholarly journals High‐Order Extended Strong Tracking Filter

2021 ◽  
Vol 30 (6) ◽  
pp. 1152-1158
Author(s):  
SUN Xiaohui ◽  
WEN Tao ◽  
WEN Chenglin ◽  
CHENG Xingshuo ◽  
WU Yunkai
Keyword(s):  
High Order ◽  
Strong Tracking Filter ◽  
Tracking Filter

scholarly journals The Working Principle of a Turbine “Case Study: GE Frame 9E Gas Turbine”

2019 ◽  
pp. 1-20
Author(s):  
Obolo Olupitan Emmanuel
Keyword(s):  
Internal Combustion Engine ◽  
Gas Turbine ◽  
Combustion Engine ◽  
Mechanical Energy ◽  
Internal Combustion ◽  
Reciprocating Motion ◽  
Fuel Gas ◽  
Spark Plug ◽  
Working Principle

Gas Turbine is one of the machines that use the thermodynamic principle converting fuel energy to mechanical energy. It is an internal combustion engine. Also, designed to accelerate a stream of gas, which is used to produce a reactive thrust to propel an object or to produce mechanical power that turns a load. It functions in the same way as the internal combustion engine. It sucks in air from the atmosphere, and compress it. The fuel (gas) is injected and ignited (spark plug). The gases expand doing work and finally exhausts outside. Instead of reciprocating motion, the gas turbine uses a rotary motion throughout, and that is the only difference.

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