Research Interests:
Research Interests:
Research Interests:
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... 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.
Research Interests:
Research Interests:
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... 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.
Research Interests:
Research Interests:
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... 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.
Research Interests:
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... 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 ...
Research Interests:
Research Interests:
Research Interests:
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... 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.
Research Interests:
Research Interests:
Research Interests:
9 Declaration 10 Copyright 11 Acknowledgements 12 Publications During PhD Study 13 Notation 15 List of Abbreviations 16 Dedication 17
Research Interests:
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... 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
Research Interests:
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... 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...
Research Interests:
— 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... 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...
Research Interests:
Elements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However,... more
Elements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However, these maintenance actions are performed by a separate organization to those undertaking fleet management operations , leading to significant uncertainty in the maintenance state of the asset. The uncertainty surrounding maintenance actions impacts prognostic efficacy. In this paper, we adopt Bayesian on-line change point detection to detect the com-pressor washing events. Then, the event detection information is used as an input to a prognostic algorithm, advising an update to the estimation of remaining useful life. To illustrate the capability of the approach, we demonstrated our on-line Bayesian change detection algorithms on synthetic and real aircraft engine service data, in order to identify the compres-sor washing events for a gas turbine and...
Knowledge management continues to be characterized by strong contextual application with diversity of techniques, tools and applications which practitioners far and wide seem to agree and adopt. However, when it comes to its philosophical... more
Knowledge management continues to be characterized by strong contextual application with diversity of techniques, tools and applications which practitioners far and wide seem to agree and adopt. However, when it comes to its philosophical distinctness, it is yet to achieve something as seemingly easy as a common definition. There is significant agreement on fluidity and methods of application but limited consensus on philosophical interpretation. Furthermore, that we know what it is, acknowledge its impact, functional relevance and yet cannot articulate a common methodology points to what this paper terms an ‘intellectual paradox’. An intellectual paradox is the phenomenon whereby professionals and academics acknowledge a concept, practice it, write about it, and promote its relevance individually but as a collective lack a consensus on exactly what it is. This paper seeks to explore this phenomenon in detail and to propose a philosophical framework. It further explores the role of ...
Research Interests:
In this paper a novel collaborative fault tolerant control scheme is presented. To simplify the presentation, only two collaborative subsystems are considered where the state space model is used. To diagnose the faults, adaptive... more
In this paper a novel collaborative fault tolerant control scheme is presented. To simplify the presentation, only two collaborative subsystems are considered where the state space model is used. To diagnose the faults, adaptive diagnostic observers are used respectively, and the adaptive tuning rule for estimating the faults have been obtained. Based upon the fault diagnosis, a fault tolerant control strategy has been proposed which is capable of controlling a healthy subsystem so that the two systems can still deliver a common working goal. A simulated example is given and desired results have been obtained.
Research Interests:
Zakwan Skaf is with the IVHM Centre, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK. Tel: +44 (0)1234 752324; Email: z.skaf@cranfield.ac.uk Prognostics is an essential part of condition-based maintenance (CBM), described as... more
Zakwan Skaf is with the IVHM Centre, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK. Tel: +44 (0)1234 752324; Email: z.skaf@cranfield.ac.uk Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle health management (IVHM) system that leads to improved safety and reliability. A vast amount of research has been presented in the literature to develop prognostics models that are able to predict a system’s RUL. These models can be broadly categorised into experience-based models, data-driven models and physics-based models. Therefore, careful consideration needs to be given to selecting which prognostics model to take forward and apply for each real application. Currently, developing reliable prognostics models in real life is challenging for various reasons, such as the design complexity associated with a system, the high uncertainty and it...
Research Interests:
Filtration of contaminant is an essential part of engineering processes in industry. Clogging of filtration medium is one of the primary failure modes in many application areas leading to reduced performance and efficiency. Imitation of... more
Filtration of contaminant is an essential part of engineering processes in industry. Clogging of filtration medium is one of the primary failure modes in many application areas leading to reduced performance and efficiency. Imitation of real life clogging scenarios in laboratory conditions is not an easy task to perform. This paper presents a data collection methodology for filter clogging phenomena in a laboratory based fuel system rig and filter clogging prognostic rig. The fuel system laboratory test-bed represents an Unmanned Aerial Vehicle (UAV) fuel system and its associated electrical power supply, control system and sensing capabilities. It is specifically designed in order to replicate a number of component degradation faults with high degree of accuracy and repeatability. The fuel system rig can produce benchmark datasets to demonstrate and examine the fuel system failures. Filter clogging failure in the fuel system rig is simulated using a direct proportional valve (DPV) ...
Elements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However,... more
Elements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However, these maintenance actions are performed by a separate organization to those undertaking fleet management operations, leading to significant uncertainty in the maintenance state of the asset. The uncertainty surrounding maintenance actions impacts prognostic efficacy. In this paper, we adopt Bayesian on-line change point detection to detect the compressor washing events. Then, the event detection information is used as an input to a prognostic algorithm, advising an update to the estimation of remaining useful life. To illustrate the capability of the approach, we demonstrated our on-line Bayesian change detection algorithms on synthetic and real aircraft engine service data, in order to identify the compressor washing events for a gas turbine and th...
This paper presents a comparative study of different control design methods applied to a nuclear reactor model. A nuclear reactor temperature controller is designed using the H-infinity (H∞) control. This advanced controller is compared... more
This paper presents a comparative study of different control design methods applied to a nuclear reactor model. A nuclear reactor temperature controller is designed using the H-infinity (H∞) control. This advanced controller is compared with a conventional Proportional–Integral–Derivative controller (PID) controller and a traditional optimal controller design using Linear Quadratic Gaussian (LQG) method.
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... 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...
Research Interests:
Research Interests:
Research Interests:
This article introduces the concept of condition monitoring (CM) and summarizes various techniques used for CM across the industrial sectors. The techniques include visual inspection, performance monitoring, vibration condition... more
This article introduces the concept of condition monitoring (CM) and summarizes various techniques used for CM across the industrial sectors. The techniques include visual inspection, performance monitoring, vibration condition monitoring, vibration condition monitoring, lubricant oil analysis, acoustic emission testing, temperature monitoring, motor current signature analysis, and ultrasound emission. The article describes the evolution of condition-based maintenance in CM. It also describes the basics of integrated vehicle health management, a capability that enables a number of maintenance philosophies. The article concludes with a discussion on various condition monitoring in industrial sectors, including condition-monitoring techniques in nuclear power plants, road condition monitoring, and condition monitoring in wind turbines.
Accurate fault diagnosis and prognosis can significantly reduce maintenance costs, increase the safety and availability of engineering systems that have become increasingly complex. It has been observed that very limited researches have... more
Accurate fault diagnosis and prognosis can significantly reduce maintenance costs, increase the safety and availability of engineering systems that have become increasingly complex. It has been observed that very limited researches have been reported on fault diagnosis where multi-component degradation are presented. This is essentially a challenging Complex Systems problem where features multiple components interacting simultaneously and nonlinearly with each other and its environment on multiple levels. Even the degradation of a single component can lead to a misidentification of the fault severity level. This paper introduces a new test rig to simulate the multi-component degradation of the aircraft fuel system. A machine learning-based data analytical approach based on the classification of clustering features from both time and frequency domains is proposed. The scope of this framework is the identification of the location and severity of not only the system fault but also the ...
Research Interests:
This paper proposes a reasoning framework to diagnose faults at the vehicle level in a complex machine like an aircraft. The current focus of Integrated Vehicle Health Management (IVHM) is on diagnosing and prognosing faults at the... more
This paper proposes a reasoning framework to diagnose faults at the vehicle level in a complex machine like an aircraft. The current focus of Integrated Vehicle Health Management (IVHM) is on diagnosing and prognosing faults at the component and subsystem levels; only a few IVHM systems consider the interaction between the systems. To diagnose faults at the vehicle level, an IVHM System needs a framework that recognizes the causal relationships between systems and the likelihood of fault propagation between them. The framework should also possess an element of reasoning to assess data from all systems, to assign priorities, and to resolve ambiguities. The Framework for Aerospace VEhicle Reasoning (FAVER) that is proposed in this paper uses a digital twin of the aircraft systems to emulate functioning of the aircraft and to simulate the effect of fault propagation due to systems interactions. FAVER applies reasoning that can handle fault signatures from multiple systems in the form o...
The creation, capturing, using and sharing of knowledge is based on data. The rate of data creation, collection, and elicitation through wide range experiments, simulations and measurements is rapidly increasing within Integrated Vehicle... more
The creation, capturing, using and sharing of knowledge is based on data. The rate of data creation, collection, and elicitation through wide range experiments, simulations and measurements is rapidly increasing within Integrated Vehicle Health Management (IVHM). In addition, Knowledge Management (KM), data abstraction, analyses, storage and accessibility challenges persist, resulting in loss of knowledge and increased costs. This growth in the creation of research data, algorithms, technical papers, reports and logs, requires both a strategy and tool to address these challenges. A Data Life Cycle Model (DLCM) ensures the efficient and effective abstraction and management of both data and knowledge outputs. IVHM which depend heavily on high-quality data to perform data-driven, model-based and hybrid computational analysis of asset health. IVHM Centre does not yet have a systematic and coherent approach to its data management. The absence of a DLCM means that valuable knowledge might...
Safety enhancement is a major goal of the aviation industry owing to the predicted increase in air travel. There is also the need to prevent fatalities, increase reliability and reduce monetary costs suffered as a result of delays and... more
Safety enhancement is a major goal of the aviation industry owing to the predicted increase in air travel. There is also the need to prevent fatalities, increase reliability and reduce monetary costs suffered as a result of delays and accidents that still occur. Accidents today are complex as a result of many causal factors acting alone but more often as a combination with other contributing factors. In tackling this trend, proactive measures have been put in place to find hazardous combinations that occur during flights in order to mitigate them before accidents occur. Flight Anomaly Detection (AD) methods are aimed at highlighting abnormal occurrences of a flight, that are different from the norm. As an improvement on the current state-of-the-art method, previous works have proposed different AD techniques for detection of previously unknown flight risks such as component faults, aircraft operational inefficiencies and some abnormal crew behaviour. However, current AD methods indi...
Research Interests:
Research Interests:
Research Interests:
ABSTRACT In this paper, a novel collaborative fault tolerant control FTC scheme has been presented. The scheme based on a constant reference output regulation controller and intelligent algorithm to detect and diagnose fault as well as... more
ABSTRACT In this paper, a novel collaborative fault tolerant control FTC scheme has been presented. The scheme based on a constant reference output regulation controller and intelligent algorithm to detect and diagnose fault as well as reconfigure the control rules. To reduce the cost of fault diagnoses time and observer complexity, the FTC algorithm has been introduced without fault detection and diagnose observers. The FTC algorithm has been developed based on the residuals and the first order derivative. In addition, to simplify the presentation, only two linearised collaborative subsystems two inverted robots joints arms and two inverted pendulum wheels cart are considered where the position of the second arm of robot has been diagnosed to compensate it through the tuning rules. However, an illustrated example is implemented and studied which reflects that the performance of the two subsystems can be delivering a common working goal through a new fault tolerant control strategy.
Research Interests:
... Zakwan.Skaf-2@postgrad.manchester.ac.uk, Tel: +44(0)161 306 4655. ... MB Menhaj and MB Ghofiani, "Robust optimal self-tuning regulator of nuclear reactors, " First conference of applications of physics and nuclear science in... more
... Zakwan.Skaf-2@postgrad.manchester.ac.uk, Tel: +44(0)161 306 4655. ... MB Menhaj and MB Ghofiani, "Robust optimal self-tuning regulator of nuclear reactors, " First conference of applications of physics and nuclear science in medical and industry, Amir Kabir university of ...