Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network

Long, Zhenhua; Bai, Mingliang; Ren, Minghao; Liu, Jinfu; Yu, Daren

doi:10.1016/j.energy.2023.127068

Cited by 9 publications

(4 citation statements)

References 50 publications

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“…"Computer vision" and "cameras" are closely related, pointing to the feeding of visual data into robotic systems for improved perception and decision-making. In the same way, "global positioning system" and "indoor positioning systems" and "localization" coupling reflects the significance of precise positioning in the operational efficiency of robotics, especially in navigation and mapping [79][80][81][82]. Found clusters around "control theory," "estimation," and "intelligent robots" suggest a change of attention towards the cognitive aspect of robotics [75,77,.…”

Section: Resultsmentioning

confidence: 99%

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Obaideen,

AlShabi,

Bettayeb

et al. 2024

Disruptive Technologies in Information Sciences VIII

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This paper gives a bibliometric summary of Unscented Kalman Filter (UKF) in AI-infused robotics, highlighting its role in unifying control and cognition. Using a systematic approach that includes literature collection from IEEE Xplore, Web of Science and Google Scholar, rigorous screening and selection, and VOSviewer for a comprehensive bibliometric analysis. This analysis reports major trends, primary contributors and central themes, highlighting UKF's pivotal role in improving robotics cognitive and control capacities. The study emphasizes the universally used UKF in many fields of robotics, i.e. in navigation and mapping, sensor fusion, and state estimation, as one of its principal developers, which illustrates its vital role in promoting robotic autonomy and intelligence. The integration of findings from the bibliometric analysis thus not only presents the current state of research but also identifies possible future research directions, highlighting the increasing unification of control theories and cognitive processes in robotics. This research adds to the body of knowledge by delivering a comprehensive map of the UKF application. In this light, the UKF will be able to penetrate AI-infused robotics, the future of robotic developments will rely on the deep fusion of control and cognition facilitated by UKF and alike.

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Section: Resultsmentioning

confidence: 99%

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Obaideen,

AlShabi,

Bettayeb

et al. 2024

Disruptive Technologies in Information Sciences VIII

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“…This improvement is not a little one but a huge step forward, making possible the detailed exploration of physical phenomena with the help of polarization properties. KF's importance lies in various sectors like environmental monitoring [12][13][14][15][16][17][18][19][20][21][22][23][24][25], weather forecast [26][27][28][29][30][31][32][33][34][35][36][37], defense surveillance [38][39][40][41][42][43][44][45][46][47], and autonomous navigation systems . Its capacity to draw clear information even from messy real-world data improving the precision and reliability of measurements has made it a key tool for boosting real-time decision-making abilities [71][72][73][74][75][76][77][78][79][80][81]…”

Section: Introductionmentioning

confidence: 99%

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

Obaideen,

AlShabi,

Gadsden

et al. 2024

Polarization: Measurement, Analysis, and Remote Sensing XVI

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This paper presents a comprehensive study on the evolution, applications, and impact of the Polarimetric Kalman Filter (PKF) in the fields of signal processing and remote sensing. By employing a methodological framework that integrates literature collection, screening and selection, bibliometric analysis, and synthesis, we aim to elucidate the PKF's contributions and potential research trajectories within its applications. Our findings highlight the PKF's significant role in enhancing data assimilation, improving predictive accuracy, and refining measurement techniques across various domains, particularly in meteorological research and environmental monitoring. The analysis reveals the interdisciplinary nature of PKF applications and its capability to merge theoretical advancements with practical implementations, underlining the importance of sophisticated signal processing algorithms in interpreting complex environmental data. The study also identifies emerging trends, such as the integration of PKF with machine learning and artificial intelligence, indicating future directions for research that promise to push the boundaries of current methodologies and applications. This paper underscores the PKF's pivotal role in advancing signal processing and remote sensing technologies, offering insights into its continuing development and the expanding scope of its applications.

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“…To address the problem of information loss in linearizing nonlinear systems, Liu et al [11] used the unscented KF to handle nonlinearity. To deal with high-order nonlinear systems, Long et al [12] used multilayer neural networks to map nonlinearity. The multilayer perceptron uses historical data to learn the dynamic representation of the observation model, replacing the rough approximation of the system state and measurement state in the KF.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

Li,

Wang,

et al. 2024

SDHM

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The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains. However, in real-world scenarios, accurate predictions are challenging due to various interferences. This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter (KF). The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments. By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals, it becomes possible to ascertain the aging status of the catenary. To improve prediction accuracy, a railway catenary aging prediction model is constructed by integrating the Takagi-Sugeno (T-S) fuzzy neural network (FNN) and KF. In this model, an adaptive training method is introduced, allowing the FNN to use fewer fuzzy rules. The inputs of the model include time, temperature, and historical displacement, while the output is the predicted displacement. Furthermore, the KF is enhanced by modifying its prior state estimate covariance and measurement error covariance. These modifications contribute to more accurate predictions. Lastly, a low-power experimental platform based on FPGA is implemented to verify the effectiveness of the proposed method. The test results demonstrate that the proposed method outperforms the compared method, showcasing its superior performance.

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Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network

Cited by 9 publications

References 50 publications

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

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