Aircraft engine hot-section virtual sensor creation and gas path performance monitoring

Nonlinear Kalman filters for aircraft engine gas path health estimation with measurement uncertainty

An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specifically addresses the underdetermined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy.

Data-driven model for accommodation of faulty angle of attack sensor measurements in fixed winged aircraft

The accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium‐ion batteries. Data‐driven SOC estimation is considered an emerging and effective solution. However, existing data‐driven SOC estimation methods typically involve direct estimation and lack effective feedback correction. Moreover, battery degradation poses additional challenges to accurate SOC estimation. Therefore, this study proposes an adaptive combined method for battery SOC estimation based on a long short‐term memory (LSTM) network and unscented Kalman filter (UKF) algorithm considering battery aging status. First, an LSTM model is constructed to characterize the battery's dynamic performance instead of traditional battery models. Then, the UKF algorithm is employed to perform SOC estimation through the feedback of terminal voltage prediction. To enhance estimation accuracy under different aging statuses, a proportional‐integral‐derivative controller is employed to correct the capacity fading during the SOC estimation process. Validation results indicate that the terminal voltage prediction model demonstrates exceptional robustness against interference from current and voltage noise. Compared to the traditional estimation method combining the deep learning model and Kalman filter algorithm, the proposed method demonstrates superior estimation accuracy under various complex operating conditions. Furthermore, the proposed method outperforms the traditional method in estimation performance during battery aging.

This paper is concerned with a novel state estimator to track gas path performance in real time with one sensor failure and packet dropouts for aircraft engine in an advanced distributed architecture. It is common to sensor measurement lost in the distributed network, which results in the decrease of state tracking accuracy. A hybrid extended Kalman filter (HEKF) is proposed for engine performance anomaly detection and sensor fault accommodation from previous studies. Five groups of sensor measurement are divided along gas path related to five local filters, and the local estimated results are fused in the main filter. The reception state matrix is introduced to HEKF to deal with packet dropout, and nonlinear calculation is separated from a local filter to reduce computational burden in the field. Besides, fault diagnosis and isolation strategy of sensor subsets is developed and combined to HEKF by state consistency strategy of distributed network. The contribution of this study is to provide the novel HEKF algorithm to achieve real-time state estimation for sensor-fault-tolerant monitoring of aircraft engine with packet dropout in the distributed structure. The simulation and comparison are systematically carried out, and the superiority of the proposed methodology is confirmed.

Physics-based sound radiation estimation from multiple speakers by combined lumped parameter and reduced-order finite element modeling

We develop a novel virtual stereo vision sensor which contains just single high-speed camera and two reflector sets to measure three-dimensional feature parameters of bubbles in the gas-liquid two-phase flow. The measurement principle of the binocular stereo vision sensor and the virtual stereo vision sensor are analyzed in detail. According to the mathematical model of the virtual stereo vision sensor, the structure parameters of virtual stereo vision sensor are optimized based on the actual size of view field, and the three-dimensional measurement error is analyzed. The three-dimensional modality and motion parameters are tested on the upstream bubble producing appliance. The experimental results show that the three-dimensional distance error of the virtual stereo vision sensor is better than 0.132mm and the relative error is better than 0.5%, and the three-dimensional motion trail of bubble is reconstructed.

Event Abstract Back to Event Estimation of time-variant, multivariate AR-models with application to interaction analysis in the human brain Thomas Milde1*, Lutz Leistritz1, Thomas Weiss2 and Herbert Witte1 1 Friedrich Schiller University , Institute of Medical Statistics Computer Sciences and Documentation, Germany 2 Friedrich Schiller University Jena, Department of Psychology, Germany Aim:In this Study, a new Kalman-Filter approach is presented for the estimation of high dimensional time variant multivariate AR-processes. Methods: Firstly, the new Kalman filter algorithm and an ERP dataset derived from an experiment with noxious laser stimuli are presented. To validate the possibilities of the new method, the Kalman filter and the RLS algorithm with forgetting factor are compared with regard to their applicability to high-dimensional time series. In order to test both approaches simulated and measured time series were used. In a multi-trial approach directed interactions between event-related potentials (ERPs) were computed. The time-variant Granger Causality Index was used for interaction analysis. Results: It can be shown that the Kalman approach enables a time-variant parameter estimation of a 58-dimensional multivariate AR model. The RLS-based algorithm fails for dimensions higher than . The interaction analysis on the simulated data-set shows, that the Kalman-filter produces more reliable time-variant granger causality indices than the RLS. Conclusion: The high-dimensional AR model provides an improved neurophysiological interpretation of the computed interaction networks. Kalman filter based algorithms are applicable for such high-dimensional analysis. One of the advantages of the Kalman filter based algorithms is the multiplicity to design the course of the estimated time-variant AR-parameters. This is impossible with just one single forgetting factor like RLS provides. Acknowledgments: This study was in part supported by the Federal Ministry of Education and Research (Bernstein Group, 01GQ0703; application), the German Research Council (DFG Wi 1166/9-1 Gamma; method's development) and the COST Action BM0601 NEUROMATH. Conference: NeuroMath COST Action BM0601: Neurodynamic Insight into Functional Connectivity, Cognition, and Consciousness, Dubrovnik, Croatia, 27 Mar - 28 Mar, 2010. Presentation Type: Poster Presentation Topic: Posters Citation: Milde T, Leistritz L, Weiss T and Witte H (2010). Estimation of time-variant, multivariate AR-models with application to interaction analysis in the human brain. Front. Neurosci. Conference Abstract: NeuroMath COST Action BM0601: Neurodynamic Insight into Functional Connectivity, Cognition, and Consciousness. doi: 10.3389/conf.fnins.2010.05.00030 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 24 Jul 2010; Published Online: 24 Jul 2010. * Correspondence: Thomas Milde, Friedrich Schiller University, Institute of Medical Statistics Computer Sciences and Documentation, Jena, Germany, thomas.milde@mti.uni-jena.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Thomas Milde Lutz Leistritz Thomas Weiss Herbert Witte Google Thomas Milde Lutz Leistritz Thomas Weiss Herbert Witte Google Scholar Thomas Milde Lutz Leistritz Thomas Weiss Herbert Witte PubMed Thomas Milde Lutz Leistritz Thomas Weiss Herbert Witte Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Gas turbine gas path diagnosis under transient operating conditions: A steady state performance model based local optimization approach

Research on improved dynamic load identification method based on Kalman filter under noise interference

Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine performance and the strong coupling of fault characteristics make it challenging to develop accurate and efficient gas path diagnosis methods. To address these issues, this study proposes a novel digital twin framework for aeroengines that achieves the digitalization of physical systems. The mechanism model is constructed at the component level. The data-driven model is built using a particle swarm optimization–extreme gradient boosting algorithm (PSO-XGBoost). These two models are fused using the low-rank multimodal fusion method (LWF) and combined with the sparse stacked autoencoder (SSAE) to form a digital twin framework of the engine for performance diagnosis. Compared to methods that are solely based on mechanism or data, the proposed digital twin framework can effectively use mechanism and data information to improve the accuracy and reliability. The research results show that the proposed digital twin framework has an error rate of 0.125% in predicting gas path parameters and has a gas path fault diagnosis accuracy of 98.6%. Considering that the degradation cost of a typical flight mission for only one aircraft engine after 3000 flight cycles is approximately USD 209.5, the proposed method has good economic efficiency. This framework can be used to improve engine reliability, availability, and efficiency, and has significant value in engineering applications.

An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.

In the control system application, the existence of noise measurement may impact on the performance degradation. The noise measurement of the sensor is produced due to several reasons, such as the low specification, external signal disturbances, and the complexity of measured state. Therefore, it should be avoided to achieve the good control performance. One of the solutions is by designing a signal filter. In this paper, the design of Kalman Filter (KF) algorithm for ultrasonic range sensor is presented. KF algorithm is designed to overcome the existence of noise measurement on the sensor. The type of ultrasonic range sensor used is HC-SR04 which is capable to detect the distance from 2 cm to 400 cm. The discrete KF algorithm is implemented using ATMega 328p microcontroller on Arduino Uno board. The algorithm is then tested with different three covariance values of process noise. The test result shows that the KF algorithm is able to reduce the measurement noise of the ultrasonic sensor. The analysis of variance conducted shows that the smaller value of covariance matrix of the process and measured noises, the better filtering process performed. However, this results in a longer generated response time. Thus, an optimization is required to obtain the best filtering performance.

The electrification of the transport sector increases the demand for a battery management system that allows the Li-ion battery to work in a sophisticated way to reach its maximum. The main focus of the battery management system is the estimation of the battery’s state of charge (SOC) which is an indicator to determine the driving range of an electric vehicle. However, battery modeling has a direct influence on SOC estimation. In this article, the equivalent circuit model (ECM) and extended Kalman filter (EKF) algorithm are combined for SOC estimation of the Li-ion battery. Since the performance of Kalman filter algorithms strictly depends on noise covariance matrices, incorrect values reduce the accuracy and make divergence in state estimation. Therefore, it is motivated to analyze the impact of process noise and measurement noise covariance matrix values on SOC estimation using Kalman filter algorithms. Ten different combinations of noise covariance matrices are employed to analyze the effect of these matrices on SOC estimation. As a result, the tuning of the filter is critical for improvement in state estimation regardless of the type of the battery model and filtering technique. The matrix values are chosen to take into account the convergence rate and estimation error. To validate the effectiveness of the chosen noise covariance matrices, the simulation results of the ECM based EKF estimation method is superimposed with the discharge curves available in the data sheet. The simulation results show minimal estimation error and a good convergence rate for tuned EKF.

An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.