scholarly journals Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering

2003 ◽  
Author(s):  
Dan Simon ◽  
Donald L. Simon
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Inequality Constraints ◽  
Computational Effort ◽  
Estimation Accuracy ◽  
State Variables ◽  
Turbofan Engine ◽  
State Variable ◽  
State Variable Constraints

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results obtained from application to a turbofan engine model. This model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

scholarly journals Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering

2005 ◽  
Vol 127 (2) ◽  
pp. 323-328 ◽  
Author(s):  
Dan Simon ◽  
Donald L. Simon
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Inequality Constraints ◽  
Computational Effort ◽  
Estimation Accuracy ◽  
State Variables ◽  
Turbofan Engine ◽  
State Variable ◽  
State Variable Constraints

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state-variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state-variable inequality constraints in the Kalman filter. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state-variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results obtained from application to a turbofan engine model. This model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

Parallel Optimal Kalman Filtering for Stochastic Systems in Multimodeling Form

1999 ◽  
Vol 122 (3) ◽  
pp. 542-550 ◽  
Author(s):  
Cyril Coumarbatch ◽  
Zoran Gajic
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Stochastic Systems ◽  
State Variables ◽  
State Transformation ◽  
Eighth Order ◽  
Reduced Order ◽  
Filtering Technique ◽  
Ill Conditioning

In this paper we show how to completely and exactly decompose the optimal Kalman filter of stochastic systems in multimodeling form in terms of one pure-slow and two pure-fast, reduced-order, independent, Kalman filters. The reduced-order Kalman filters are all driven by the system measurements. This leads to a parallel Kalman filtering scheme and removes ill-conditioning of the original full-order singularly perturbed Kalman filter. The results obtained are valid for steady state. In that direction, the corresponding algebraic filter Riccati equation is completely decoupled and solved in terms of one pure-slow and two pure fast, reduced-order, independent, algebraic Riccati equations. A nonsingular state transformation that exactly relates the state variables in the original and new coordinates (in which the required decomposition is achieved) is also established. The eighth order model of a passenger car under road disturbances is used to demonstrate efficiency of the proposed filtering technique. [S0022-0434(00)01703-2]

scholarly journals Kalman Filtering Applied to Induction Motor State Estimation

2020 ◽  
Author(s):  
Yassine Zahraoui ◽  
Mohamed Akherraz
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Speed Estimation ◽  
Stochastic Algorithm ◽  
State Variables ◽  
Extended Kalman Filtering ◽  
Filtering Theory ◽  
State Observation

This chapter presents a full definition and explanation of Kalman filtering theory, precisely the filter stochastic algorithm. After the definition, a concrete example of application is explained. The simulated example concerns an extended Kalman filter applied to machine state and speed estimation. A full observation of an induction motor state variables and mechanical speed will be presented and discussed in details. A comparison between extended Kalman filtering and adaptive Luenberger state observation will be highlighted and discussed in detail with many figures. In conclusion, the chapter is ended by listing the Kalman filtering main advantages and recent advances in the scientific literature.

A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

2016 ◽  
Vol 33 (3) ◽  
Author(s):  
Feng Lu ◽  
Yafan Wang ◽  
Jinquan Huang ◽  
Qihang Wang
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filter ◽  
Fault Diagnosis ◽  
Fitness Function ◽  
Performance Degradation ◽  
Estimation Accuracy ◽  
Adaptive Genetic Algorithm ◽  
Turbofan Engine ◽  
Gas Path Fault ◽  
Fault Mode

AbstractA hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

On-Line Performance Monitoring and Engine Diagnostic Using Robust Kalman Filtering Techniques

2003 ◽  
Author(s):  
Pierre Dewallef ◽  
Olivier Le´onard
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Kalman Filters ◽  
Performance Monitoring ◽  
Stochastic Methods ◽  
Inlet Temperature ◽  
State Variables ◽  
Present Contribution ◽  
Regression Problem ◽  
On Line

In this contribution, an on-line engine performance monitoring is carried out through an engine health parameter estimation based on several gas path measurements. This health parameter estimation makes use of the analytical redundancy of an engine model and therefore implies the knowledge of the engine state. As the latter is a priori not known the second task is therefore an engine state variable estimation. State variables here designate working conditions such as inlet temperature, pressure, Mach number, rotational speeds, … Estimation of the state variables constitutes a general application of the Extended Kalman Filter theory, while the health parameter estimation is a classical recurrent regression problem. Recent advances in stochastic methods [1] show that both problems can be solved by two Kalman filters working jointly. Such filters are usually named Dual Kalman Filters. The present contribution aims at using a dual Kalman filter modified to provide robustness. This procedure should be able to cope with as much as 20 to 30% of faulty data. The resulting online method is applied to a turbofan model developed in the frame of the OBIDICOTE 1 project. Several tests are carried out to check the performance monitoring capability and the robustness that can be achieved.

Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Trung Nguyen ◽  
George K. I. Mann ◽  
Andrew Vardy ◽  
Raymond G. Gosine
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
High Speed ◽  
Three Dimensional ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Deterministic Sampling ◽  
Cubature Kalman Filter

This paper presents a computationally efficient sensor-fusion algorithm for visual inertial odometry (VIO). The paper utilizes trifocal tensor geometry (TTG) for visual measurement model and a nonlinear deterministic-sampling-based filter known as cubature Kalman filter (CKF) to handle the system nonlinearity. The TTG-based approach is developed to replace the computationally expensive three-dimensional-feature-point reconstruction in the conventional VIO system. This replacement has simplified the system architecture and reduced the processing time significantly. The CKF is formulated for the VIO problem, which helps to achieve a better estimation accuracy and robust performance than the conventional extended Kalman filter (EKF). This paper also addresses the computationally efficient issue associated with Kalman filtering structure using cubature information filter (CIF), the CKF version on information domain. The CIF execution avoids the inverse computation of the high-dimensional innovation covariance matrix, which in turn further improves the computational efficiency of the VIO system. Several experiments use the publicly available datasets for validation and comparing against many other VIO algorithms available in the recent literature. Overall, this proposed algorithm can be implemented as a fast VIO solution for high-speed autonomous robotic systems.

A study of a Kalman filter active vehicle suspension system using correlation of front and rear wheel road inputs

2000 ◽  
Vol 214 (5) ◽  
pp. 493-502 ◽  
Author(s):  
F Yu ◽  
J-W Zhang ◽  
D. A. Crolla
Keyword(s):  
Kalman Filter ◽  
Rear Wheel ◽  
Suspension System ◽  
Estimation Accuracy ◽  
Vehicle Suspension ◽  
Vehicle Speed ◽  
State Variables ◽  
Performance Improvements ◽  
Vehicle Suspension System ◽  
Active Vehicle Suspension System

Based on a half-vehicle model, an algorithm is proposed for a Kalman filter optimal active vehicle suspension system using the correlation between front and rear wheel road inputs. In this paper, two main issues were investigated, i.e. the estimation accuracy of the Kalman filter for state variables, and the potential improvements from wheelbase preview. Simulations showed good estimations from the state observer. However, if the wheelbase preview algorithm is incorporated, the estimation accuracy for the additional states significantly decreases as vehicle speed and the corresponding measurement noises increase. Significant benefits from wheelbase preview were further proved, and the available performance improvements of the rear wheel station could be up to 35 per cent. Because of the feasibility and effectiveness of the proposed algorithm, and no additional cost for measurements and sensing needs, wheelbase preview can be a promising algorithm for Kalman filter active suspension system designs.

Application of the Extended Kalman Filter to Control of a Shape Memory Alloy Arm

2003 ◽  
Author(s):  
Mohammad H. Elahinia ◽  
Hashem Ashrafiuon ◽  
Mehdi Ahmadian ◽  
Daniel J. Inman
Keyword(s):  
Kalman Filter ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Extended Kalman Filter ◽  
State Vector ◽  
Angular Position ◽  
The State ◽  
State Variables ◽  
Time Step ◽  
State Variable

This paper presents a robust nonlinear control that uses a state variable estimator for control of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA) wire. A model for SMA actuated manipulator is presented. The model includes nonlinear dynamics of the manipulator, a constitutive model of the Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. The current experimental setup allows for the measurement of only one state variable which is the angular position of the arm. Due to measurement difficulties, the other three state variables, arm angular velocity and SMA wire stress and temperature, cannot be directly measured. A model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter (EKF). This estimator predicts the state vector at each time step and corrects its prediction based on the angular position measurements. The estimator is then used in a nonlinear and robust control algorithm based on Variable Structure Control (VSC). The VSC algorithm is a control gain switching technique based on the arm angular position (and velocity) feedback and EKF estimated SMA wire stress and temperature. The state vector estimates help reduce or avoid the undesirable and inefficient overshoot problem in SMA one-way actuation control.

Measurement of Step Responses of Flowrate in a Pipe Using a Kalman-Filtering Laminar Flowmeter

2021 ◽  
Author(s):  
Kazushi Sanada
Keyword(s):  
Kalman Filter ◽  
Laminar Flow ◽  
Response Time ◽  
Kalman Filtering ◽  
The State ◽  
Step Response ◽  
Working Fluid ◽  
State Variables ◽  
Turbulent Friction ◽  
Experimental Calibration

Abstract The aim of our research project is to develop a Kalman filter system which estimates unsteady flowrate of a pipe using a laminar flowmeter. In this study, incompressible flow is assumed as working fluid. When the flow becomes turbulent, it is difficult to establish flow model for turbulent friction. In this study, a laminar flowmeter is constructed in which thirty-two narrow pipes of 1mm in inner diameter are bundled and inserted in main flow path. When fluid flow in the narrow pipe is laminar flow, the Kalman filter theory can be applied to the flow of the narrow pipe. Kalman filter is applied to one of narrow pipes of laminar flowmeter. Both upstream and downstream pressure signals of the targeted narrow pipe are input to the Kalman filter. Midpoint pressure measured by a pressure sensor is compared with midpoint pressure signal which is estimated by the Kalman filter. When flow is laminar flow or the system has linear characteristics, an error signal between estimated pressure and measured pressure decreases according to Kalman filter principle. As a result, because the state variables of the Kalman filter converge to real variables, unsteady flowrate is estimated from the state variables of the Kalman filter. Experimental calibration of the Kalman-filtering laminar flowmeter under steady-state flow condition has been performed. In this study, experiments of step response of flowrate in a pipe are conducted by constructing an experimental circuit using solenoid valves. The purpose of experiment is confirmation of a response time of the Kalman-filtering laminar flowmeter. As a result of experiments, it was shown that the response time is 0.05s.

scholarly journals Influence of State-Variable Constraints on Partially Observable Monte Carlo Planning

2019 ◽  
Author(s):  
Alberto Castellini ◽  
Georgios Chalkiadakis ◽  
Alessandro Farinelli
Keyword(s):  
Markov Random Fields ◽  
State Variables ◽  
Discrete State ◽  
State Variable ◽  
Online Planning ◽  
State Variable Constraints ◽  
Markov Decision ◽  
Planning Methods ◽  
Amount Of Knowledge ◽  
Partially Observable

Online planning methods for partially observable Markov decision processes (POMDPs) have recently gained much interest. In this paper, we propose the introduction of prior knowledge in the form of (probabilistic) relationships among discrete state-variables, for online planning based on the well-known POMCP algorithm. In particular, we propose the use of hard constraint networks and probabilistic Markov random fields to formalize state-variable constraints and we extend the POMCP algorithm to take advantage of these constraints. Results on a case study based on Rocksample show that the usage of this knowledge provides significant improvements to the performance of the algorithm. The extent of this improvement depends on the amount of knowledge encoded in the constraints and reaches the 50% of the average discounted return in the most favorable cases that we analyzed.

Sign in / Sign up

Export Citation Format

Share Document