Fusing unscented Kalman filter for performance monitoring and fault accommodation in gas turbine

2016 ◽  
Vol 232 (3) ◽  
pp. 556-570 ◽  
Author(s):  
Feng Lu ◽  
Yafan Wang ◽  
Jinquan Huang ◽  
Yihuan Huang ◽  
Xiaojie Qiu
Keyword(s):  
Kalman Filter ◽  
Gas Turbine ◽  
Extended Kalman Filter ◽  
Kalman Filters ◽  
Performance Monitoring ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Health State ◽  
Sensor Failure ◽  
Turbine Performance

The Kalman filter is widely utilized for gas turbine health monitoring due to its simplicity, robustness, and suitability for real-time implementations. The most common Kalman filter for linear systems is linearized Kalman filter, and for nonlinear systems are extended Kalman filter and unscented Kalman filter. These algorithms have proven their capabilities to estimate gas turbine performance variations with a good accuracy, and the studies are done provided that all sensor measurements are available. In this paper, a nonlinear fusion approach with consistent diagnostic mechanism based on unscented Kalman filter is proposed, especially for gas turbine performance monitoring in the case of sensor failure. The architecture of fusion method comprises a set of local unscented Kalman filters and an information mixer. The local unscented Kalman filters are utilized to estimate health parameters of various component combinations, and the results are then transferred to the mixer for the integrated estimation of global health state in fusion structure. The consistent fault diagnosis and isolation logic is designed based on the fusion architecture and combined with the fusing unscented Kalman filter, called an improved fusing unscented Kalman filter. A systematic comparison of the generic linearized Kalman filter, extended Kalman filter, and unscented Kalman filter to their fusion filter kinds is presented for engine health estimation of gradual deterioration and abrupt fault. The studies show that the fusing unscented Kalman filter evidently outperforms the fusing linearized Kalman filter and fusing extended Kalman filter, while the fusing Kalman filters have slightly better estimation accuracy than the basic Kalman filters. In addition, the proposed methodology can reach the reliable performance monitoring with measurement uncertainty while the conventional Kalman filters collapse.

Fast Kalman SLAM

2010 ◽  
Vol 44-47 ◽  
pp. 3174-3179
Author(s):  
Wu Zhou ◽  
Chun Xia Zhao ◽  
Mian Hao Zhang
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Extended Kalman Filter ◽  
Simulation Analysis ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Map Building ◽  
Complex Environments ◽  
Path Component ◽  
High Computational Complexity

When Simultaneous Localization and Map Building is carried out in complex environments, reduction of computational complexity is a key problem. With a view to the high computational complexity of particle filter, a SLAM solution named ‘Fast Kalman SLAM’ is introduced. Adopting the ‘decomposition’ idea in the FastSLAM algorithm, Fast Kalman SLAM factors the joint SLAM state into a path component and a conditional map component. The robot pose is estimated recursively with Mean Extended Kalman Filter (MEKF) or Unscented Kalman Filter (UKF), while the map with Extended Kalman Filter (EKF). Simulative experiments are carried out to evaluate the performance of the presented algorithm. And Simulation analysis is made for the presented algorithm. The experimental results indicate that the new algorithm reduces computational complexity greatly and ensures estimation accuracy at the same time.

Parameter Estimation and Performance Seeking of a Marine Gas Turbine Based on Extended Kalman Filter

2020 ◽  
Author(s):  
Zhitao Wang ◽  
Junxin Zhang ◽  
Wanling Qi ◽  
Shuying Li
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Gas Turbine ◽  
Extended Kalman Filter ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Stable Operation ◽  
Health State ◽  
Sqp Algorithm ◽  
And Performance

Abstract Marine gas turbines have been widely used and developed in the field of marine power. It is important to make them operated safely and efficiently. In this paper, a marine triaxial gas turbine is taken as an example to study the method of estimating the health state of the gas path using extended Kalman filter (EKF). To verify the accuracy of EKF, a comparison was made between linearized Kalman filtering (LKF) and EKF. In addition, the sequential quadratic programming (SQP) algorithm is used to seek the performance in case of gas path abnormal. The combination of parameter estimation and performance seeking forms a comprehensive method for diagnosis and optimization of marine gas turbines. The results show that the EKF method is an effective method for combining nonlinear systems with traditional Kalman filter. EKF has a good estimation effect on the gas path health state under different operating conditions. Also, the marine triaxial gas turbine achieved the target performance under the constraints of the SQP algorithm. Performance seeking restores the output power of the marine gas turbine and reduces the inlet and outlet temperatures of turbines. It can effectively prevent the problem of excessive combustion and ensure the safe and stable operation of the marine gas turbine.

Gas turbine performance monitoring based on extended information fusion filter

2018 ◽  
Vol 233 (2) ◽  
pp. 483-497 ◽  
Author(s):  
Feng Lu ◽  
Yihuan Huang ◽  
Jinquan Huang ◽  
Xiaojie Qiu
Keyword(s):  
State Estimation ◽  
Gas Turbine ◽  
Information Fusion ◽  
Kalman Filters ◽  
Performance Monitoring ◽  
Maintenance Cost ◽  
Global State ◽  
State Estimate ◽  
Turbine Performance ◽  
Information Filters

Performance monitoring is a critical issue for gas turbine engine for improving the operation safety and reducing the maintenance cost. With regard to this, variants of Kalman-filters-based state estimation have been employed to detect gas turbine performance, but the classical centralized Kalman filters are subject to heavy computational effort and poor fault tolerance. A novel nonlinear fusion filter algorithm using information description with distributed architecture is proposed and applied to gas turbine performance monitoring. This methodology is developed from federated Kalman filter, and a bank of local extended information filters and one information mixer are combined with extended information fusion filter. The local state estimates and covariance calculated in parallel by the local extended information filters are integrated in the information mixer to yield a global state estimate. The global state estimate of nonlinear system is fed back to the local filters with weighted factor for next iteration. The aim of the proposed methodology is to reduce the computational efforts of state estimation and improve robustness to sensor faults in cases of gas turbine performance monitoring. The simulation results on a turbofan engine confirm the extended information fusion filter's effective capabilities in comparison to the general central ones.

scholarly journals AN EXTENDED AND UNSCENTED KALMAN FILTERS SIMULATION AND DESIGN FOR A MOBILE ROBOT

2020 ◽  
Vol 15 (11) ◽  
Author(s):  
Rashid Ali
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Kalman Filters ◽  
Unscented Kalman Filter ◽  
Mobile Platform ◽  
Localization System ◽  
Different Types

This research analyzes the design and simulation of a mobile robot using Extended Kalman Filter (EKF) and Unscented Kalman filter (UKF). The mobile platform has a differential configuration, where each track of a wheel is associated with an encoder. The EKF and UKF methods are used to integrate the measurements of a novel odometric system based on the optical mice and the measurements of a localization system based on a map of geometric beacons. Two different types of simulations have been performed for validating the results, either using the mouse-based odometric system or using the conventional wheel encoder-based odometric system, to compare and evaluate the errors made by each system.

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

scholarly journals Extraction and Filter Algorithm of Guidance Information for Full-strapdown Seeker on Rotation Missile

2018 ◽  
Vol 214 ◽  
pp. 03008 ◽  
Author(s):  
YongShan Liu ◽  
Li Song ◽  
JingLong Li
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Simple Structure ◽  
Unscented Kalman Filter ◽  
High Reliability ◽  
Engineering Application ◽  
Line Of Sight ◽  
Light Weight ◽  
Rate Information ◽  
Guidance Information

Strapdown seekers are superior to platform seekers for their simple structure, high reliability and light weight but cannot measure the line-of-sight angle rate information for the guidance of rotation missile directly. This paper aims at the engineering application of full-strapdown seekers on rotation missile problem. Firstly, a line-of-sight angle rate solution model is established. Based on the MATLAB, the extended Kalman filter (EKF) algorithm and unscented Kalman filter (UKF) algorithm are used to estimate the line-of-sight angle rate information of the full-strapdown seekers. The results show that using EKF filter and UKF filter both can obtain effective guidance information and the UKF’s effect is better.

scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

scholarly journals A New Linear Regression Kalman Filter with Symmetric Samples

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2139
Author(s):  
Xiuqiong Chen ◽  
Jiayi Kang ◽  
Mina Teicher ◽  
Stephen S.-T. Yau
Keyword(s):  
Kalman Filter ◽  
Linear Regression ◽  
Particle Filter ◽  
Normal Distribution ◽  
Extended Kalman Filter ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Standard Normal Distribution ◽  
Leibler Divergence ◽  
Standard Normal

Nonlinear filtering is of great significance in industries. In this work, we develop a new linear regression Kalman filter for discrete nonlinear filtering problems. Under the framework of linear regression Kalman filter, the key step is minimizing the Kullback–Leibler divergence between standard normal distribution and its Dirac mixture approximation formed by symmetric samples so that we can obtain a set of samples which can capture the information of reference density. The samples representing the conditional densities evolve in a deterministic way, and therefore we need less samples compared with particle filter, as there is less variance in our method. The numerical results show that the new algorithm is more efficient compared with the widely used extended Kalman filter, unscented Kalman filter and particle filter.

Sign in / Sign up

Export Citation Format

Share Document