Markov Nonlinear System Estimation for Engine Performance Tracking

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Peng Wang ◽  
Robert X. Gao
Keyword(s):  
Particle Filtering ◽  
Estimation Method ◽  
Engine Performance ◽  
The State ◽  
Evolution Model ◽  
Estimation Accuracy ◽  
Engine Operation ◽  
Performance Tracking ◽  
Degradation Rates ◽  
State Evolution

This paper presents a joint state and parameter estimation method for aircraft engine performance degradation tracking. Contrast to previously reported techniques on state estimation that view parameters in the state evolution model as constants, the method presented in this paper treats parameters as time-varying variables to account for varying degradation rates at different stages of engine operation. Transition of degradation stages and estimation of parameters are performed by particle filtering (PF) under the Bayesian inference framework. To address the sample impoverishment problem due to discrete resampling, which is inherent to PF, a continuous resampling strategy has been proposed, with the goal to improve estimation accuracy of PF. The algorithm has shown to be able to detect abrupt fault inception based on the residuals between the estimated results from the state evolution model and actual measurements. The developed technique is evaluated using data generated from a turbofan engine model. Simulation of engine output parameters over a series of flights with both nominal degradation and abrupt fault types has been conducted, and error within 1% for performance tracking and degradation prediction has been shown. This demonstrates the effectiveness of the developed technique in fault detection and degradation tracking in aircraft engines.

scholarly journals Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiao Chen ◽  
Zaichen Zhang ◽  
Liang Wu ◽  
Jian Dang
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Mimo Systems ◽  
Beam Steering ◽  
Estimation Method ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce 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. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

Mechanisms effecting the state, evolution and transition of the planetary scale monsoon

1977 ◽  
Vol 115 (5-6) ◽  
pp. 1463-1491 ◽  
Author(s):  
Peter J. Webster ◽  
Lang Chou ◽  
Ka Ming Lau
Keyword(s):  
The State ◽  
Planetary Scale ◽  
State Evolution

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

scholarly journals A Micro Swing Rotor Engine and the Preliminary Study of Its Thermodynamic Characteristics

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2684
Author(s):  
Chen Xia ◽  
Zhiguang Zhang ◽  
Guoping Huang ◽  
Tong Zhou ◽  
Jianhua Xu
Keyword(s):  
Power Systems ◽  
Thermal State ◽  
Engine Performance ◽  
Thermodynamic Characteristics ◽  
Practical Significance ◽  
Maximum Efficiency ◽  
Design Guideline ◽  
Engine Operation ◽  
Low Frequencies ◽  
Gap Height

The rapid progress in microelectromechanical system technology has facilitated great developments in micro heat engines, however, defects in these engines still need to be overcome. Thus, a novel four-stroke micro swing rotor engine (MSRE) that features the advantages of Wankel engine and the micro swing engine is proposed in this study. Kinematic and thermodynamic analyses of the MSRE were performed, and a preliminary experimental research was conducted. In the MSRE, the driver assembly was used to dominate the movements of the two rotors. By the design guideline adaptable to the engine operation principle, the volume of each chamber will vary in a sine-like waveform, which was validated by kinematic analyses. Then, a prototype in mesoscale was designed and fabricated. In consideration of the leakage effect, the thermodynamic characteristics of this engine were numerically investigated. Results indicate that the engine thermodynamic processes were seriously affected by leakage flow, especially when working at low frequencies. Gap height and operation frequency were the two dominant factors that affected engine performance. Under a certain gap height, the MSRE had to work at a specific frequency range and the corresponding optical values exist for engine efficiency and power. With a 20 μm height, the MSRE reached the maximum efficiency of 23.62% at 55 Hz and the maximum power of 3442 W at 95 Hz. Feasibility of the engine was further verified by an experimental test on the operation characteristics, including the cold state test with pressured air blow and the combustion test for engine operation at thermal state. This research lays a good foundation for future development of MSRE, which is of great practical significance for the progress in micro power systems.

scholarly journals Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879087 ◽  
Author(s):  
Lin Zhou ◽  
Qianxiang Yu ◽  
Daozhi Liu ◽  
Ming Li ◽  
Shukai Chi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Data Storage ◽  
Wireless Sensors ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Vibration Signal ◽  
Estimation Accuracy ◽  
Offshore Structure ◽  
Vibration Signals ◽  
Reconstructed Signal

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

Adaptive calibration of Diesel engine injection for minimising fuel consumption with constrained NOx emissions in actual driving missions

2020 ◽  
pp. 146808742091880
Author(s):  
José Manuel Luján ◽  
Benjamín Pla ◽  
Pau Bares ◽  
Varun Pandey
Keyword(s):  
Diesel Engine ◽  
Fuel Consumption ◽  
Time Window ◽  
Transition Probability ◽  
Estimation Method ◽  
Engine Performance ◽  
Driving Cycle ◽  
Vehicle Speed ◽  
Adaptive Calibration ◽  
Emission Limits

This article proposes a method for fuel minimisation of a Diesel engine with constrained [Formula: see text] emission in actual driving mission. Specifically, the methodology involves three developments: The first is a driving cycle prediction tool which is based on the space-variant transition probability matrix obtained from an actual vehicle speed dataset. Then, a vehicle and an engine model is developed to predict the engine performance depending on the calibration for the estimated driving cycle. Finally, a controller is proposed which adapts the start-of-injection calibration map to fulfil the [Formula: see text] emission constraint while minimising the fuel consumption. The calibration is adapted during a predefined time window based on the predicted engine performance on the estimated cycle and the difference between the actual and the constraint on engine [Formula: see text] emissions. The method assessment was done experimentally in the engine test set-up. The engine performace using the method is compared with the state-of-the-art static calibration method for different [Formula: see text] emission limits on real driving cycles. The online implementation of the method shows that the fuel consumption can be reduced by 3%–4% while staying within the emission limits, indicating that the estimation method is able to capture the main driving cycle characterstics.

scholarly journals An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

2013 ◽  
Vol 135 (7) ◽  
Author(s):  
Donald L. Simon ◽  
Jeffrey B. Armstrong
Keyword(s):  
Kalman Filter ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Fault Isolation ◽  
Performance Estimation ◽  
Estimation Accuracy ◽  
Fault Diagnostics ◽  
Tuning Parameters ◽  
Performance Deterioration ◽  
Health Parameters

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.

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