Application of an Optimal Tuner Selection Approach for On-Board Self-Tuning Engine Models

Various Kalman filter approaches have been presented for performance estimation of aircraft engines provided that sensor measurement numbers are sufficient and all of them are available. However, it is difficult to collect all physical parameters along the gas path since the complex structure limits sensor installation, especially around the high-pressure turbine. The contribution of this article is to create a virtual sensor in the hot-section based on the component physical characteristics and aerothermodynamic theory and develop a dual hypothesis test strategy combined with a state estimator to track abrupt degradation of the engine component. A novel couple algorithm of the unscented Kalman filter using a virtual sensor is developed that has three primary advantages: (i) The performance degradation of four rotatory components can be estimated without sensor P43. (ii) The accuracy of state estimation using the virtual sensor is equivalent to that of sensor P43, and it adapts to the abrupt change of engine operating condition in the whole flight envelope. (iii) If the sensor can be installed in the future, it can be engaged to its analytical redundancy. Simulations are carried out on typical abrupt degradation datasets of aircraft engines, which demonstrate the superiority of anomaly detection of gas path component performance using virtual sensor measurements, especially hot-section components.

The satellite clock plays a key role in the global navigation satellite system (GNSS). The accuracy of GNSS and its applications depend on the quality of the satellite clock. Therefore, precisely estimating and predicting the satellite clock is an important issue in the fields of GNSS and its application. As an optimal estimation algorithm, Kalman filter has been used to estimate and predict the satellite clock. However, in a conventional Kalman filter algorithm, the noise covariance matrices of satellite clock need to be predetermined, which restricts its further applications since the noise covariance matrices, especially the process noise covariance matrix, are usually unknown in the real cases. With inappropriate noise covariance matrices, the state estimation of conventional Kalman filter is suboptimal. To cope with this problem, a new noise covariance matrix estimation method of Kalman filter is proposed, and then we apply it to the problem of satellite clock estimation and prediction. Considering the fact that the process noise covariance matrix depends on the unknown noise parameters, the problem of estimating process noise covariance matrix can be solved by estimating the unknown noise parameters. First, the correlation between the Kalman innovations is used to establish a linear relationship with the unknown noise parameters. Then the unknown parameters can be estimated by least-squares estimation. Finally, the satellite clock can be estimated and predicted with the estimated noise parameters. In the new method, no prior information about the noise parameters is needed. Even with some extreme prior noise parameters, the new method can also work very well and has good convergence properties. For comparison, we conduct two experiments using the new method and the adaptively robust Kalman filter with classified adaptive factors based on opening windows separately, both results are consistent with each other very well, which verifies the correctness and effectiveness of this new method.

We present a distributed observer for nonlinear systems, which can be used to estimate the state of a central plant with unknown parameters using a network of sensors that measure noisy linear combinations of the state. Our state estimator is based on the unscented Kalman filter and the dual Kalman filter method, which runs two Kalman filters in parallel for both state and parameter estimation. This allows us to improve estimates of the central plants parameters in order to improve state estimates, and vice versa. Furthermore, using results from stochastic stability analysis, we prove that under certain conditions the expected value of the proposed algorithm's estimation error is bounded. This result also shows us that as t goes to infinity, the error bound scales as a function of the largest eigenvalue of the sensor noise covariance matrix. Lastly, we demonstrate the ability of our state estimator to accurately track the state of nonlinear systems through simulations.

Adaptive model is the basis of engine fault diagnosis, performance monitoring, engine control, etc. This paper presents an improved kalman filter method which uses engine measurable parameters deviation to estimate the degradation parameters to correct the nominal model, and the acquisition and application of multiple kalman filter gain matrices in the whole flight envelope is analyzed. Simulation is carried out taking a civil engine as simulation object, the simulation results show that the method used in this paper to establish unmeasured parameters adaptive model can get the engine parameters better.

On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter

Background/Objectives: The performance of the speech enhancement techniques is examined by applying them to the speech signals corrupted by real world noise. Methods: An advanced coding Methodology is introduced to reduce the noisy signal using H-infinity filter. Here two compression techniques - Switch Split Vector Quantization (SSVQ) and Multi Stage Vector Quantization (MSVQ) are used. The enhancement techniques like Kalman Filter and Recursive Filter were assessed for similarities and differences with H-Infinity Filter and the outcomes are compared using signal to noise ratio which is likely to be affected on real world noise. Findings: In this paper using kalman filter, recursive filter, h-infinity filter plays vital role for comparing the various parameter characteristics. Kalman filter uses ordered steps that solve a mathematical problem. H-infinity filter differs from the normal changed Kalman filtering as it requires the knowledge of commotion parameters. H-infinity minimizes the estimation errors and thus obtains robustness and obtains the better results in enhancement. Application/Improvements: This study can be helpful for improving the intelligibility in speech signal and the same method can be implemented with different types of hybrid vector quantization techniques and may achieve better SNR and quality of the signal.

SummaryThis paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect.

This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R), inductance (L), and capacitance (C) values for Universiti Malaysia Pahang (UMP) short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.

The rainfall amount observed at a given location mostly depend on the cloud density, which can be quantified with the reflectivity values observed by meteorology weather radars. In this study, we aim to estimate the rainfall amount using a Kalman filter with radar reflectivity measurements. We first assume that the amount of rainfall observed at automatic weather observation stations (AWOSs) are elements of an unknown state vector and consider the Kalman filter process model as the true rainfall amounts observed at these AWOSs over time. For the measurement model of the Kalman filter, we use the radar reflectivity values observed at each AWOS location. For the execution of the Kalman filter, a number of rainfall amount and radar reflectivity value pairs are first required to learn the process and measurement models of the Kalman filter. The estimation performance of the proposed Kalman filter is then compared with empirical reflectivity (Z) - rainfall (R) relationships. Numerical results show that when the Kalman filter is executed with radar reflectivity measurements observed around a large number of AWOS locations, the mean squared errors of the Kalman filter rainfall estimates are smaller than the ones obtained with empirical ZR relationships.

Consider a scalar linear time-invariant system whose state is being estimated by an estimator using measurements from a single sensor. The sensor may be compromised by an attacker. The attacker is allowed to replace the measurement sequence by an arbitrary sequence. When the estimator uses this sequence, its estimate is degraded in the sense that the mean square error of this estimate is higher. The estimator monitors the received data to detect if an attack is in progress. The aim of the attacker is to degrade the estimate to the maximal possible amount while remaining undetected. By defining a suitable notion of stealthiness of the attacker, we characterize the trade-off between the fundamental limits of performance degradation that an attacker can induce versus its level of stealthiness. For various information patterns that characterize the information available at every time step to the attacker, we provide information theoretic bounds on the worst mean squared error of the state estimate that is possible and provide attacks that can achieve these bounds while allowing the attacker to remain stealthy even if the estimator uses arbitrary statistical ergodicity based tests on the received data.

Purpose This paper aims to propose a novel adaptive Kalman filter (APRKF) designed to accommodate the operational environment of maglev pipeline trains, enhancing positioning accuracy by mitigating the impact of noise and sensor errors. Design/methodology/approach This approach is built upon the Kalman filtering algorithm, using a limited-memory weighted method to derive adaptive factors, while establishing judgment thresholds to constrain the range of their variation. Findings A comparative analysis with the traditional Kalman filter and the Sage-Husa adaptive filter reveals that APRKF offers superior speed estimation performance while effectively mitigating error accumulation caused by speed integration. The results indicate that APRKF excels in both speed and position estimation, demonstrating its effectiveness in improving estimation accuracy. Originality/value This study proposes an innovative method for the velocity and position estimation system of maglev tube-track trains.

In this work the mobile robot localization problem in an unknown environment is faced and a new version of the Extended Kalman filter is proposed to estimate the robot position and orientation. This new filter uses a convex combination of two filters estimating the same state variables. The first filter is based on measurements provided by robot on board distance sensors while the second one uses out of board distance sensors measurements. The resulting “Mixed” Kalman filter is designed to emphasize the qualities and overcome the defects of each used sensor. The proposed fusing technique has been tested in a real experimental framework using the robot Khepera III. The algorithm has been contrasted with other Extended Kalman filters, based on the on board and on the out of board sensors measurements, yielding to encouraging results.

For Time-varying, Time-invariant, and steady-state systems, Kalman Filter can be implemented as a prediction algorithm, since it produces the state prediction and the corresponding prediction error covariance matrix via the state estimation and the corresponding estimation error covariance matrix. Lainiotis Filter is equivalent to Kalman Filter and can be used to compute the prediction. In this paper, for Time-varying, Time-invariant and steady state systems, estimation-free Prediction Algorithms are derived via Kalman and Lainiotis filters; they are equivalent and compute iteratively the prediction and the corresponding prediction error covariance matrix. The estimation and the corresponding estimation error covariance matrix are not needed and are not computed. The proposed estimation-free prediction algorithms are faster than the Kalman filter.

This paper proposes an improved hybrid Kalman filter(IHKF) for in-flight aircraft engine performance estimation. It’s a special hybrid structure of nonlinear on-board engine model(NOBEM) and piecewise linear Kalman filter(PWKF). The outputs of NOBEM are regarded as the baseline of PWKF, while its performance deterioration factors(PDF) regarded as the augmented state vector of PWKF are on-line estimated by the deviation of measured outputs, and fed back to NOBEM which can be on-line updated next time. In addition, the finite state machine logic of work mode has been established, and makes the IHKF work better. By this approach applied to a turbofan engine, a series of simulation results show that the model can estimate the real engine performance effectively within the whole flight envelope, different engine states and severe performance deterioration condition, which lays the foundation for intelligent engine control(IEC), performance seeking control(PSC) and in-flight fault detection, isolation and accommodation (FDIA).

Wind turbines are often grouped together for financial reasons, but due to wake development this usually results in decreased turbine lifetimes and power capture, and thereby an increased levelized cost of energy (LCOE). Wind farm control aims to minimize this cost by operating turbines at their optimal control settings. Most state-of-the-art control algorithms are open-loop and rely on low fidelity, static flow models. Closed-loop control relying on a dynamic model and state observer has real potential to further decrease wind's LCOE, but is often too computationally expensive for practical use. In this paper two time-efficient Kalman filter (KF) variants are outlined incorporating the medium fidelity, dynamic flow model “WindFarmSimulator” (WFSim). This model relies on a discretized set of Navier-Stokes equations in two dimensions to predict the flow in wind farms at low computational cost. The filters implemented are an Ensemble KF and an Approximate KF. Simulations in which a high fidelity simulation model represents the true wind farm show that these filters are 101 —102 times faster than a regular KF with comparable or better performance, correcting for wake dynamics that are not modeled in WFSim (noticeably, wake meandering and turbine hub effects). This is a first big step towards real-time closed-loop control for wind farms.