UKACC International Conference on CONTROL 2010, 2010
In this paper, a new fault tolerant controller (FTC) algorithm for general stochastic nonlinear s... more In this paper, a new fault tolerant controller (FTC) algorithm for general stochastic nonlinear systems is studied. Different from the existing FTC methods, the measured information is the probability density functions (PDFs) of the system output rather than its value, where the radial basis functions (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings, so that the problem is transformed into a nonlinear FTC problem subject to the weight dynamical systems. The main objective of FTC requires detecting the occurrence of faults and maintaining the performance of the system in the presence of faults at a satisfying level. The FTC design consists of two steps. The first step is fault detection and diagnosis (FDD), which can produce an alarm when there is a fault in the system and also locate which component has a fault. The second step is to adapt the controller to the faulty case so that the system is able to achieve its target. A linear matrix inequality (LMI) based feasible FTC method is applied such that the fault can be detected and diagnosed. An illustrated example is included to demonstrate the use of control algorithm, and satisfactory results have been obtained.
2022 Advances in Science and Engineering Technology International Conferences (ASET), 2022
Solar energy is renewable, clean, and friendly to the environment. Utilizing solar energy is a ma... more Solar energy is renewable, clean, and friendly to the environment. Utilizing solar energy is a major step toward reducing global warming because it reduces pollution. The smart city concept presents a novel idea for renewable energy, such as Photovoltaic (PV) technologies. The smart campus is one of the areas of focus in smart cities. In this context, the smart campus is a term used to refer to the teaching environment and application service systems, where dynamic interaction between people and the surrounding service develops intelligent teaching, learning, and campus life environment. However, some researchers refer to the smart campus to replace the current energy sources with more sustainable and environmentally friendly solutions. This paper presents an overview of a smart green campus's concept by integrating the concepts of green energy generation and smart system application. This would enhance the building efficiency, utilize more renewable energy technology and advanced digital solution, minimize the environmental impact and operation cost. This paper uses the Higher Colleges of Technology (HCT) campus in Sharjah Men campus (SMC) as a use case study to demonstrate the vision of the smart green campus. The key areas of the campus considered in the study are campus building, streets and outdoor areas, and campus services. The proposed concept of a smart green campus will focus on the IoT-enabled sensor devices proposed to each potential application in the campus. The proposed vision of the smart green campus serves the community better by providing different innovative systems for the people and facilitating the country's development. Furthermore, the vision caters to the core infrastructure of the campus, such as the buildings, the roads, and the Mosque, while providing its members with a decent quality of life, a clean and sustainable environment, and innovative systems. The case study shows a 63.7% saving in electricity when using solar energy to generate electricity and implementing the innovative applications to the smart green campus. Also, it shows a reduction in the emission and carbon dioxide CO2 released into the air as a direct result of electricity generation to 0.02.
Abstract Aircraft fault detection and prediction is a critical element of preventing failures, re... more Abstract Aircraft fault detection and prediction is a critical element of preventing failures, reducing maintenance costs, and increasing fleet availability. This paper considers a problem of rare failure prediction in the context of aircraft predictive maintenance. It presents a novel approach of predicting extremely rare failures, based on combining two deep learning techniques, auto-encoder (AE) and Bidirectional Gated Recurrent Unit (BGRU) network. AE is modified and trained to detect rare failure, and the result from AE is fed into the BGRU to predict the next occurrence of failure. The applicability of the proposed approach is evaluated using real-world test cases of log-based warning and failure messages obtained from the aircraft central maintenance system fleet database and the records of maintenance history. The proposed AE-BGRU model is compared with other similar deep learning methods, the proposed approach is 25% better in precision, 14% in the recall, and 3% in G-mean. The result also shows robustness in predicting failure within a defined useful period.
2010 8th World Congress on Intelligent Control and Automation, 2010
AbstractIn this paper, a new algorithm for an adaptive Proportional-Integrator (PI)controller fo... more AbstractIn this paper, a new algorithm for an adaptive Proportional-Integrator (PI)controller for nonlinear systems subjected to stochastic non-Gaussian disturbance is studied. The minimum entropy control is applied to decrease the closed-loop tracking error under an iterative ...
This paper presents a comparative study of six different linear observers. The studied observers ... more This paper presents a comparative study of six different linear observers. The studied observers are Luenberger Observer, Kalman (Filter) Observer, Unknown Input Observer, Augmented Robust Observer, High Gain Observer and Sensitive High Gain Observer. A Matlab simulation of a DC motor model is undertaken to verify the performance of the designed observers. The Comparisons were carried out different conditions in terms of white noise as disturbance, where the Probability Density Function (PDF) of estimated residuals has been used. For additive fault only the amplitude of residuals has been considered. The simulation results are given to show and compare the effectiveness of these observers on the speed of the servo DC motor.
2010 8th World Congress on Intelligent Control and Automation, 2010
This paper studies and compares three nonlinear observers (Nonlinear Lyapunov Observer (NLO), Lip... more This paper studies and compares three nonlinear observers (Nonlinear Lyapunov Observer (NLO), Lipschitz Observer (LIO) and Partial Lipschitz Observer (PLIO)) applied to nonlinear model of the DC servo motor. The considered criteria of computations for white noise is the amplitude of the residual and the estimated shape of residual and error probability density functions (PDF) which is estimated by Kernel
The use of aircraft operation logs to develop a data-driven model to predict probable failures th... more The use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is eval...
Deep learning approaches are continuously achieving state-of-the-art performance in aerospace pre... more Deep learning approaches are continuously achieving state-of-the-art performance in aerospace predictive maintenance modelling. However, the data imbalance distribution issue is still a challenge. It causes performance degradation in predictive models, resulting in unreliable prognostics which prevents predictive models from being widely deployed in realtime aircraft systems. The imbalanced classification problem arises when the distribution of the classes present in the datasets is not uniform, such that the total number of instances in a class is significantly lower than those belonging to the other classes. It becomes more challenging when the imbalance ratio is extreme. This paper proposes a deep learning approach using rescaled Long Short Term Memory (LSTM) modelling for predicting aircraft component replacement under imbalanced dataset constraint. The new approach modifies prediction of each class using rescale weighted cross-entropy loss, which controls the weight of majority...
— A new design of a fault tolerant control (FTC)-based an adaptive, fixed-structure PI controller... more — A new design of a fault tolerant control (FTC)-based an adaptive, fixed-structure PI controller, with constraints on the state vector for nonlinear discrete-time system subject to stochastic non-Gaussian disturbance is studied. The objective of the reliable control algorithm scheme is to design a control signal such that the actual probability density function (PDF) of the system is made as close as possible to a desired PDF, and make the tracking performance converge to zero, not only when all components are functional but also in case of admissible faults. A Linear Matrix Inequality (LMI)-based FTC method is presented to ensure that the fault can be estimated and compensated for. A radial basis function (RBF) neural network is used to approximate the output PDF of the system. Thus, the aim of the output PDF control will be a RBF weight control with an adaptive tuning of the basis function parameters. The key issue here is to divide the control horizon into a number of equal time...
UKACC International Conference on CONTROL 2010, 2010
In this paper, a new fault tolerant controller (FTC) algorithm for general stochastic nonlinear s... more In this paper, a new fault tolerant controller (FTC) algorithm for general stochastic nonlinear systems is studied. Different from the existing FTC methods, the measured information is the probability density functions (PDFs) of the system output rather than its value, where the radial basis functions (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings, so that the problem is transformed into a nonlinear FTC problem subject to the weight dynamical systems. The main objective of FTC requires detecting the occurrence of faults and maintaining the performance of the system in the presence of faults at a satisfying level. The FTC design consists of two steps. The first step is fault detection and diagnosis (FDD), which can produce an alarm when there is a fault in the system and also locate which component has a fault. The second step is to adapt the controller to the faulty case so that the system is able to achieve its target. A linear matrix inequality (LMI) based feasible FTC method is applied such that the fault can be detected and diagnosed. An illustrated example is included to demonstrate the use of control algorithm, and satisfactory results have been obtained.
2022 Advances in Science and Engineering Technology International Conferences (ASET), 2022
Solar energy is renewable, clean, and friendly to the environment. Utilizing solar energy is a ma... more Solar energy is renewable, clean, and friendly to the environment. Utilizing solar energy is a major step toward reducing global warming because it reduces pollution. The smart city concept presents a novel idea for renewable energy, such as Photovoltaic (PV) technologies. The smart campus is one of the areas of focus in smart cities. In this context, the smart campus is a term used to refer to the teaching environment and application service systems, where dynamic interaction between people and the surrounding service develops intelligent teaching, learning, and campus life environment. However, some researchers refer to the smart campus to replace the current energy sources with more sustainable and environmentally friendly solutions. This paper presents an overview of a smart green campus's concept by integrating the concepts of green energy generation and smart system application. This would enhance the building efficiency, utilize more renewable energy technology and advanced digital solution, minimize the environmental impact and operation cost. This paper uses the Higher Colleges of Technology (HCT) campus in Sharjah Men campus (SMC) as a use case study to demonstrate the vision of the smart green campus. The key areas of the campus considered in the study are campus building, streets and outdoor areas, and campus services. The proposed concept of a smart green campus will focus on the IoT-enabled sensor devices proposed to each potential application in the campus. The proposed vision of the smart green campus serves the community better by providing different innovative systems for the people and facilitating the country's development. Furthermore, the vision caters to the core infrastructure of the campus, such as the buildings, the roads, and the Mosque, while providing its members with a decent quality of life, a clean and sustainable environment, and innovative systems. The case study shows a 63.7% saving in electricity when using solar energy to generate electricity and implementing the innovative applications to the smart green campus. Also, it shows a reduction in the emission and carbon dioxide CO2 released into the air as a direct result of electricity generation to 0.02.
Abstract Aircraft fault detection and prediction is a critical element of preventing failures, re... more Abstract Aircraft fault detection and prediction is a critical element of preventing failures, reducing maintenance costs, and increasing fleet availability. This paper considers a problem of rare failure prediction in the context of aircraft predictive maintenance. It presents a novel approach of predicting extremely rare failures, based on combining two deep learning techniques, auto-encoder (AE) and Bidirectional Gated Recurrent Unit (BGRU) network. AE is modified and trained to detect rare failure, and the result from AE is fed into the BGRU to predict the next occurrence of failure. The applicability of the proposed approach is evaluated using real-world test cases of log-based warning and failure messages obtained from the aircraft central maintenance system fleet database and the records of maintenance history. The proposed AE-BGRU model is compared with other similar deep learning methods, the proposed approach is 25% better in precision, 14% in the recall, and 3% in G-mean. The result also shows robustness in predicting failure within a defined useful period.
2010 8th World Congress on Intelligent Control and Automation, 2010
AbstractIn this paper, a new algorithm for an adaptive Proportional-Integrator (PI)controller fo... more AbstractIn this paper, a new algorithm for an adaptive Proportional-Integrator (PI)controller for nonlinear systems subjected to stochastic non-Gaussian disturbance is studied. The minimum entropy control is applied to decrease the closed-loop tracking error under an iterative ...
This paper presents a comparative study of six different linear observers. The studied observers ... more This paper presents a comparative study of six different linear observers. The studied observers are Luenberger Observer, Kalman (Filter) Observer, Unknown Input Observer, Augmented Robust Observer, High Gain Observer and Sensitive High Gain Observer. A Matlab simulation of a DC motor model is undertaken to verify the performance of the designed observers. The Comparisons were carried out different conditions in terms of white noise as disturbance, where the Probability Density Function (PDF) of estimated residuals has been used. For additive fault only the amplitude of residuals has been considered. The simulation results are given to show and compare the effectiveness of these observers on the speed of the servo DC motor.
2010 8th World Congress on Intelligent Control and Automation, 2010
This paper studies and compares three nonlinear observers (Nonlinear Lyapunov Observer (NLO), Lip... more This paper studies and compares three nonlinear observers (Nonlinear Lyapunov Observer (NLO), Lipschitz Observer (LIO) and Partial Lipschitz Observer (PLIO)) applied to nonlinear model of the DC servo motor. The considered criteria of computations for white noise is the amplitude of the residual and the estimated shape of residual and error probability density functions (PDF) which is estimated by Kernel
The use of aircraft operation logs to develop a data-driven model to predict probable failures th... more The use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is eval...
Deep learning approaches are continuously achieving state-of-the-art performance in aerospace pre... more Deep learning approaches are continuously achieving state-of-the-art performance in aerospace predictive maintenance modelling. However, the data imbalance distribution issue is still a challenge. It causes performance degradation in predictive models, resulting in unreliable prognostics which prevents predictive models from being widely deployed in realtime aircraft systems. The imbalanced classification problem arises when the distribution of the classes present in the datasets is not uniform, such that the total number of instances in a class is significantly lower than those belonging to the other classes. It becomes more challenging when the imbalance ratio is extreme. This paper proposes a deep learning approach using rescaled Long Short Term Memory (LSTM) modelling for predicting aircraft component replacement under imbalanced dataset constraint. The new approach modifies prediction of each class using rescale weighted cross-entropy loss, which controls the weight of majority...
— A new design of a fault tolerant control (FTC)-based an adaptive, fixed-structure PI controller... more — A new design of a fault tolerant control (FTC)-based an adaptive, fixed-structure PI controller, with constraints on the state vector for nonlinear discrete-time system subject to stochastic non-Gaussian disturbance is studied. The objective of the reliable control algorithm scheme is to design a control signal such that the actual probability density function (PDF) of the system is made as close as possible to a desired PDF, and make the tracking performance converge to zero, not only when all components are functional but also in case of admissible faults. A Linear Matrix Inequality (LMI)-based FTC method is presented to ensure that the fault can be estimated and compensated for. A radial basis function (RBF) neural network is used to approximate the output PDF of the system. Thus, the aim of the output PDF control will be a RBF weight control with an adaptive tuning of the basis function parameters. The key issue here is to divide the control horizon into a number of equal time...
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