Mehrdad Saif - Profile on Academia.edu (original) (raw)

Papers by Mehrdad Saif

Proceedings of the 2004 American Control Conference, 2004

In this paper, we study the well posedness of observer-based fault detection filters using the th... more In this paper, we study the well posedness of observer-based fault detection filters using the theory of singular perturbation. By proper scaling of the fault maps, it is shown that the ill-conditioning of the residual fault projector can be alleviated. This allows the construction of an approximate projector in terms of the projectors for the slow and fast subsystems.

IFAC-PapersOnLine, 2020

A switching adaptive control algorithm for automating connected vehicles in a rigid platoon patte... more A switching adaptive control algorithm for automating connected vehicles in a rigid platoon pattern is proposed here. A second-order nonlinear model for the follower vehicles running on the highways is adopted and it is assumed that the parameters of the vehicles's model, including the mass, aerodynamic drag and tire drag, are fully unknown and their values cannot be used in arriving at the control laws. Furthermore, some uncertainties and external perturbations are added to the model to consider the effects of always present modeling errors, un-modeled dynamics and external time varying perturbations on the vehicles. Besides, control input variations are inserted into the nonlinear model of the platoon to represent actuator fluctuations. Subsequently, a robust adaptive control scheme is established so that the asymptotic stability of each vehicle in the platoon is guaranteed, and this is demonstrated using the Lyapunov stability criterion. A novel spacing error variable is also introduced to achieve the global string stability for the whole platoon. Following a comprehensive mathematical analysis, a computer simulation example is presented to illustrate the effectiveness as well as the performance of the proposed control system.

Advances in Difference Equations, Jul 22, 2020

Nonlinearities, such as dead-zone, backlash, hysteresis, and saturation, are common in the mechan... more Nonlinearities, such as dead-zone, backlash, hysteresis, and saturation, are common in the mechanical and mechatronic systems' components and actuators. Hence, an effective control strategy should take into account such nonlinearities which, if unaccounted for, may cause serious response problems and might even result in system failure. Input saturation is one of the most common nonlinearities in practical control systems. So, this article introduces a novel adaptive variable structure control strategy for nonlinear Caputo fractional-order systems despite the saturating inputs. Owing to the complex nature of the fractional-order systems and lack of proper identification strategies for such systems, this research focuses on the canonic systems with complete unknown dynamics and even those with model uncertainties and external noise. Using mathematical stability theory and adaptive control strategy, a simple stable integral sliding mode control is proposed. The controller will be shown to be effective against actuator saturation as well as unknown characteristics and system uncertainties. Finally, two case studies, including a mechatronic device, are considered to illustrate the effectiveness and practicality of the proposed controller in the applications.

Fault Tolerant Control of Rhine-Meuse Delta Water System: A Performance Assessment Based Approach

An occurrence of potential faults/hazardous situations could jeopardize the safety and reliabilit... more An occurrence of potential faults/hazardous situations could jeopardize the safety and reliability of complex dynamical systems. A new Fault Tolerant Control (FTC) methodology capable of preventing floods in the land areas close to the Rhine-Meuse Delta water system is proposed in this paper. The Delta water network is a Large-Scale System (LSS) with many barriers and sluices and is of enormous economic importance to Europe. Floods in this water network have damaged the system and cities around it. Thus, control of this complex water system is necessary. To monitor this complex system and detect any anomalies in a timely fashion, a fault diagnosis method using a Control Performance Index (CPI) is proposed for this large-scale water system. After fault diagnosis is performed, a switching mode control is devised to prevent potential flood situations. The switching controller is self-modified via the performance index information. Simulation tests are performed using experimental data from the aforementioned water system to examine the effectiveness of the suggested FTC method in comparison with the FTC using historical benchmark performance assessment method and the current controller of this water network system.

Sensors, Mar 30, 2022

Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a... more Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers.

Enhanced COVID-19 Detection by chest x-ray images using transfer learning-based extracted deep features and information fusion

2023 International Conference on Control, Automation and Diagnosis (ICCAD)

One of the essential factors to limit the spreading of COVID-19 is an early and accurate diagnosi... more One of the essential factors to limit the spreading of COVID-19 is an early and accurate diagnosis. Chest X-rays (CXRs) imaging is a common approach to identify COVID19, owing to its ability to detect the respiratory problem as a major symptom of COVID-19 and its public access even in third-world countries. A robust and efficient classification by an intelligent computer-aided model plays a prominent role in facilitating this procedure. In this work, a fusion strategy using Transfer Learning (TL) on a Deep Convolutional Neural Network (DCNN), optimized Ensemble Decision Tree (EDT) and Support Vector Machine (SVM) is introduced to classify the positive and negative COVID-19 cases through using Chest X-rays (CXRs) images. First, a ResNet50 approach is applied to perform a direct classification and to extract deep features. Next, Principal Component Analysis (PCA) is employed on the extracted deep features from the ResNet50 to establish new reduced and uncorrelated feature space. Then, these features are forwarded to SVM and EDT for classification. Hyperparameters of SVM and EDT are optimized by Bayesian Optimization (BO) algorithm. In the last step, Majority Voting (MV) is employed to integrate the classification results and identify COVID19. The main benefit of the proposed COVID19 detection scheme is that the deep features automatically capture COVID19 patterns and improve detection efficiency. In addition, the integrated information from various optimized approaches enhances the classification accuracy and leads to more robust and reliable results.

A New Hybrid Supervisory Control System for Cabinet-Type Firebox Furnaces

IEEE Transactions on Automation Science and Engineering

In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control Sy... more In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control System (SCS) are proposed to improve the efficiency, safety, availability, and control capabilities of industrial furnaces. The main components of ICS are process control systems and advanced control systems that consist of overheating protection and load control. New soft sensors are designed as a combination of Laguerre filters and an artificial neural network to estimate the surface temperature of the furnace’s tubes, which allows the protection system to adjust fuel flow rate via overriding commands. Model-based fault detection systems are developed to detect faults in the combustion system and fouling in the furnace tubes and prepare features for the supervisory system. The supervisory control system is responsible for interfering between different components, evaluating the situation, and decision-making based on the unit status and process conditions. An intuitionistic fuzzy inference system is employed as the core of the supervisory controller to tolerate disturbance and faults by switching the control modes. Test studies using experimental data of the furnace indicate the capability of the proposed monitoring and control system to operate in various loading situations and recover the system from abnormal conditions.

Sensors

This paper investigates the problem of false data injection attack (FDIA) detection in microgrids... more This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it p...

An Overview of the State of the Art in Aircraft Prognostic and Health Management Strategies

IEEE Transactions on Instrumentation and Measurement

Fault Tolerant Control of Rhine-Meuse Delta Water System: A Performance Assessment Based Approach

2019 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET)

An occurrence of potential faults/hazardous situations could jeopardize the safety and reliabilit... more An occurrence of potential faults/hazardous situations could jeopardize the safety and reliability of complex dynamical systems. A new Fault Tolerant Control (FTC) methodology capable of preventing floods in the land areas close to the Rhine-Meuse Delta water system is proposed in this paper. The Delta water network is a Large-Scale System (LSS) with many barriers and sluices and is of enormous economic importance to Europe. Floods in this water network have damaged the system and cities around it. Thus, control of this complex water system is necessary. To monitor this complex system and detect any anomalies in a timely fashion, a fault diagnosis method using a Control Performance Index (CPI) is proposed for this large-scale water system. After fault diagnosis is performed, a switching mode control is devised to prevent potential flood situations. The switching controller is self-modified via the performance index information. Simulation tests are performed using experimental data from the aforementioned water system to examine the effectiveness of the suggested FTC method in comparison with the FTC using historical benchmark performance assessment method and the current controller of this water network system.

Maximum Power Point Tracker (MPPT) for Photovoltaic Power Systems-A Systematic Literature Review

2018 European Control Conference (ECC), 2018

Photovoltaic (PV) as a renewable source of energy plays a significant role in generating electric... more Photovoltaic (PV) as a renewable source of energy plays a significant role in generating electricities in the industry and distributed consumers. The output power of the PV device is highly nonlinear which is dependent on I-P and V-P characteristics of the device and also irradiation conditions. Therefore, many research works have been performed to optimize the performance and obtain maximum power from the PV panels. This paper provides a brief literature review on maximum power point tracker (MPPT) for the PV panels. For this purpose, the PV circuit structure with its mathematics model is presented. Then, recent publications on various design methodologies are reviewed.

New Condition-Based Monitoring and Fusion Approaches With a Bounded Uncertainty for Bearing Lifetime Prediction

IEEE Sensors Journal, 2022

Condition Monitoring (CM) is an essential element of securing reliable operating conditions of Wi... more Condition Monitoring (CM) is an essential element of securing reliable operating conditions of Wind Turbines (WT) in a wind farm. CM helps optimize maintenance by providing Remaining Useful Life (RUL) forecast. However, the expected RUL is not often reliable due to uncertainty associated with the prediction horizon. In this paper, we employ high-level fusion methods to expect the RUL of WT bearings. For this purpose, various features are extracted by vibration signals to capture deterioration paths. Then, a Bayesian algorithm is utilized to determine RUL for each selected feature. Eventually, high-level fusion schemes, including Hurwicz, Choquet integral, Ordered Weighted Averaging operator, are employed to integrate RUL numbers and lessen associated uncertainty in the prediction horizons. Besides, a pessimistic fusion strategy is driven to obtain a bounded uncertainty for the worst RUL prediction. The fusion methods are assessed by ten-year vibration signals of Canadian wind farms. Experimental results confirm accurate results with bounded uncertainty for high-level fusion approaches.

IEEE Transactions on Industrial Electronics, 2022

This paper deals with the problem of controller design for DC microgrids that feed constant power... more This paper deals with the problem of controller design for DC microgrids that feed constant power loads. To design the proposed controller, first by the use of the exact feedback linearization approach, the linear model of Brunovsky's canonical representation of the system has been obtained to address the nonlinearity problem of the system. Then, the desired control technique is developed by a combination of sliding mode and backstepping control approaches in which a nonlinear disturbance observer is utilized to estimate the disturbance. The overall stability of the system is analyzed based on the Lyapunov approach. A suitable and practical sliding surface is one of the controller strengths that allow the bus voltage to track the reference voltage with high accuracy and fast transient response. Finally, to prove the mentioned claims, an experimental setup has been constructed and the proposed controller is implemented. The experimental results have been analyzed and error analysis is performed. The results confirm the superiority of the proposed controller compared to state-of-the-art controllers.

A New Fault Diagnosis Approach for Heavy-Duty Gas Turbines

IEEE/ASME Transactions on Mechatronics, 2022

Effective fault detection, estimation, and iso6 lation are essential for the safety and reliabili... more Effective fault detection, estimation, and iso6 lation are essential for the safety and reliability of gas tur7 bines. In this article, a hybrid fault detection and isolation 8 (FDI) approach is presented for condition monitoring of 9 heavy-duty gas turbines. First, nonlinear dynamical models 10 are constructed using an orthonormal basis function and 11 an adaptive neuro-fuzzy inference system through exper12 imental data. Following that, a fuzzy inference system is 13 employed to estimate the fault severity. For this aim, fuzzy 14 rules are extracted from the fault patterns by defining ap15 propriate features. Later, various faults are isolated using 16 an ensemble decision tree classifier. The proposed non17 linear modeling compensates for disturbances and uncer18 tainty in the system and leads to adaptive thresholds for 19 fault detection, which reduces the false alarm rate. More20 over, the proposed FDImethod brings high accuracy in fault 21 estimation by properly modeling a bounded uncertainty us22 ing the adaptive threshold. Experimental data are applied 23 to validate the gas turbine model. Test results indicate that 24 the proposed hybrid FDI method via adaptive threshold 25 overwhelms the other FDImethods, where the misclassified data are 5.6%.

A Distributed Fault Detection And Isolation Method For Multifunctional Spoiler System

2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS), 2018

The increasing complexity of aircraft subsystems and control structure invoke new fault diagnosis... more The increasing complexity of aircraft subsystems and control structure invoke new fault diagnosis methodologies for these vehicles. Multifunctional spoiler (MFS) is an essential part of an aircraft spoiler control system that can be easily deteriorated due to faults which could consequently compromise the safety of the aircraft. The MFS consists of several components with highly nonlinear dynamics. This paper presents a new fault detection and isolation (FDI) system using dynamic neural networks (DNN) to deal with incipient faults at their early stages. For this purpose, an intelligent distributed FDI framework consisting of three DNNs is employed for generating residual set in the system to observe any discrepancy in the states of the system. Furthermore, the dynamic structure of the designed neural networks helps the observers tackle the non-linearity of the system and provides the fault isolation in the whole operating range. Simulation results are conducted to demonstrate the ability and effectiveness of the proposed FDI system.

Multi-Feature Fusion Approach for Epileptic Seizure Detection From EEG Signals

IEEE Sensors Journal, 2021

In this article, a new fusion scheme based on the Dempster–Shafer Evidence Theory (DSET) is intro... more In this article, a new fusion scheme based on the Dempster–Shafer Evidence Theory (DSET) is introduced for Epileptic Seizure Detection (ESD) in brain disorders. Firstly, various features in temporal, spectral, and temporal-spectral domains are extracted from Electroencephalogram (EEG) signals. Afterward, a Correlation analysis via the Pearson Correlation Coefficient (PCC) is conducted on the extracted features to select and remove highly correlated features. It leads to the second feature set with about half numbers of the first feature set. Next, three separate filter-type feature selection techniques, including Relief-F (RF), Compensation Distance Evaluation Technique (CDET), and Fisher Score (FS), are conducted to this second feature set for ranking features. Following that, a feature fusion is engaged by the DSET through the individual feature ranking results to generate high qualified feature sets. Indeed, the DSET-based feature fusion is devoted to enhancing the feature selection confidence using the least superb ranked features. In the classification stage, an Ensemble Decision Tree (EDT) classifier, along with two common validation procedures, including hold out and 10-fold cross-validation, is appropriated to classify the selected features from the EEG signals as normal, pre-ictal (epileptic background), and ictal (epileptic seizure) classes. Finally, several test scenarios are investigated using experimental data of Bonn University to evaluate the proposed ESD performance. Moreover, a comparison with other research works on the same dataset and classes is accomplished. The obtained results indicate the effectiveness of the proposed feature fusion approach and superior accuracy compared to the traditional methods.

IEEE Systems Journal, 2021

Background: To contribute to the current debate as to the relative influences of dietary intake a... more Background: To contribute to the current debate as to the relative influences of dietary intake and physical activity on the development of adiposity in community-based children. Methods: Participants were 734 boys and girls measured at age 8, 10 and 12 years for percent body fat (dual emission x-ray absorptiometry), physical activity (pedometers, accelerometers); and dietary intake (1 and 2-day records), with assessments of pubertal development and socioeconomic status. Results: Cross-sectional relationships revealed that boys and girls with higher percent body fat were less physically active, both in terms of steps per day and moderate and vigorous physical activity (both sexes p,0.001 for both measures). However, fatter children did not consume more energy, fat, carbohydrate or sugar; boys with higher percent body fat actually consumed less carbohydrate (p = 0.01) and energy (p = 0.05). Longitudinal analysis (combined data from both sexes) was weaker, but supported the cross-sectional findings, showing that children who reduced their PA over the four years increased their percent body fat (p = 0.04). Relationships in the 8 year-olds and also in the leanest quartile of all children, where adiposity-related underreporting was unlikely, were consistent with those of the whole group, indicating that underreporting did not influence our findings. Conclusions: These data provide support for the premise that physical activity is the main source of variation in the percent body fat of healthy community-based Australian children. General community strategies involving dietary intake and physical activity to combat childhood obesity may benefit by making physical activity the foremost focus of attention.

Critical Wind Turbine Components Prognostics: A Comprehensive Review

IEEE Transactions on Instrumentation and Measurement, 2020

As wind energy is becoming a significant utility source, minimizing the operation and maintenance... more As wind energy is becoming a significant utility source, minimizing the operation and maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to fossil fuels. Wind turbines (WTs) are subject to unexpected failures due to operational and environmental conditions, aging, and so on. An accurate estimation of time to failures assures reliable power production and lower maintenance costs. In recent years, a notable amount of research has been undertaken to propose prognosis techniques that can be employed to forecast the remaining useful life (RUL) of wind farm assets. This article provides a recent literature review on modeling developments for the RUL prediction of critical WT components, including physics-based, artificial intelligence (AI)-based, stochastic-based, and hybrid prognostics. In particular, the pros and cons of the prognosis models are investigated to assist researchers in selecting proper models for certain applications of WT RUL forecast. Our comprehensive review has revealed that hybrid methods are now the leading and most accurate tools for WT failure predictions over individual hybrid components. Strong candidates for future research include the consideration of variable operating environments, component interaction, physics-based prognostics, and the Bayesian application in the development of WT prognosis methods.

Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings Under Varying Operating Conditions

IEEE Transactions on Industrial Informatics, 2020

The failure progression of wind turbine bearings comprises of multiple degraded health states due... more The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm.

IEEE/ASME Transactions on Mechatronics, 2020

Proceedings of the 2004 American Control Conference, 2004

In this paper, we study the well posedness of observer-based fault detection filters using the th... more In this paper, we study the well posedness of observer-based fault detection filters using the theory of singular perturbation. By proper scaling of the fault maps, it is shown that the ill-conditioning of the residual fault projector can be alleviated. This allows the construction of an approximate projector in terms of the projectors for the slow and fast subsystems.

IFAC-PapersOnLine, 2020

A switching adaptive control algorithm for automating connected vehicles in a rigid platoon patte... more A switching adaptive control algorithm for automating connected vehicles in a rigid platoon pattern is proposed here. A second-order nonlinear model for the follower vehicles running on the highways is adopted and it is assumed that the parameters of the vehicles's model, including the mass, aerodynamic drag and tire drag, are fully unknown and their values cannot be used in arriving at the control laws. Furthermore, some uncertainties and external perturbations are added to the model to consider the effects of always present modeling errors, un-modeled dynamics and external time varying perturbations on the vehicles. Besides, control input variations are inserted into the nonlinear model of the platoon to represent actuator fluctuations. Subsequently, a robust adaptive control scheme is established so that the asymptotic stability of each vehicle in the platoon is guaranteed, and this is demonstrated using the Lyapunov stability criterion. A novel spacing error variable is also introduced to achieve the global string stability for the whole platoon. Following a comprehensive mathematical analysis, a computer simulation example is presented to illustrate the effectiveness as well as the performance of the proposed control system.

Advances in Difference Equations, Jul 22, 2020

Nonlinearities, such as dead-zone, backlash, hysteresis, and saturation, are common in the mechan... more Nonlinearities, such as dead-zone, backlash, hysteresis, and saturation, are common in the mechanical and mechatronic systems' components and actuators. Hence, an effective control strategy should take into account such nonlinearities which, if unaccounted for, may cause serious response problems and might even result in system failure. Input saturation is one of the most common nonlinearities in practical control systems. So, this article introduces a novel adaptive variable structure control strategy for nonlinear Caputo fractional-order systems despite the saturating inputs. Owing to the complex nature of the fractional-order systems and lack of proper identification strategies for such systems, this research focuses on the canonic systems with complete unknown dynamics and even those with model uncertainties and external noise. Using mathematical stability theory and adaptive control strategy, a simple stable integral sliding mode control is proposed. The controller will be shown to be effective against actuator saturation as well as unknown characteristics and system uncertainties. Finally, two case studies, including a mechatronic device, are considered to illustrate the effectiveness and practicality of the proposed controller in the applications.

Fault Tolerant Control of Rhine-Meuse Delta Water System: A Performance Assessment Based Approach

An occurrence of potential faults/hazardous situations could jeopardize the safety and reliabilit... more An occurrence of potential faults/hazardous situations could jeopardize the safety and reliability of complex dynamical systems. A new Fault Tolerant Control (FTC) methodology capable of preventing floods in the land areas close to the Rhine-Meuse Delta water system is proposed in this paper. The Delta water network is a Large-Scale System (LSS) with many barriers and sluices and is of enormous economic importance to Europe. Floods in this water network have damaged the system and cities around it. Thus, control of this complex water system is necessary. To monitor this complex system and detect any anomalies in a timely fashion, a fault diagnosis method using a Control Performance Index (CPI) is proposed for this large-scale water system. After fault diagnosis is performed, a switching mode control is devised to prevent potential flood situations. The switching controller is self-modified via the performance index information. Simulation tests are performed using experimental data from the aforementioned water system to examine the effectiveness of the suggested FTC method in comparison with the FTC using historical benchmark performance assessment method and the current controller of this water network system.

Sensors, Mar 30, 2022

Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a... more Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers.

Enhanced COVID-19 Detection by chest x-ray images using transfer learning-based extracted deep features and information fusion

2023 International Conference on Control, Automation and Diagnosis (ICCAD)

One of the essential factors to limit the spreading of COVID-19 is an early and accurate diagnosi... more One of the essential factors to limit the spreading of COVID-19 is an early and accurate diagnosis. Chest X-rays (CXRs) imaging is a common approach to identify COVID19, owing to its ability to detect the respiratory problem as a major symptom of COVID-19 and its public access even in third-world countries. A robust and efficient classification by an intelligent computer-aided model plays a prominent role in facilitating this procedure. In this work, a fusion strategy using Transfer Learning (TL) on a Deep Convolutional Neural Network (DCNN), optimized Ensemble Decision Tree (EDT) and Support Vector Machine (SVM) is introduced to classify the positive and negative COVID-19 cases through using Chest X-rays (CXRs) images. First, a ResNet50 approach is applied to perform a direct classification and to extract deep features. Next, Principal Component Analysis (PCA) is employed on the extracted deep features from the ResNet50 to establish new reduced and uncorrelated feature space. Then, these features are forwarded to SVM and EDT for classification. Hyperparameters of SVM and EDT are optimized by Bayesian Optimization (BO) algorithm. In the last step, Majority Voting (MV) is employed to integrate the classification results and identify COVID19. The main benefit of the proposed COVID19 detection scheme is that the deep features automatically capture COVID19 patterns and improve detection efficiency. In addition, the integrated information from various optimized approaches enhances the classification accuracy and leads to more robust and reliable results.

A New Hybrid Supervisory Control System for Cabinet-Type Firebox Furnaces

IEEE Transactions on Automation Science and Engineering

In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control Sy... more In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control System (SCS) are proposed to improve the efficiency, safety, availability, and control capabilities of industrial furnaces. The main components of ICS are process control systems and advanced control systems that consist of overheating protection and load control. New soft sensors are designed as a combination of Laguerre filters and an artificial neural network to estimate the surface temperature of the furnace’s tubes, which allows the protection system to adjust fuel flow rate via overriding commands. Model-based fault detection systems are developed to detect faults in the combustion system and fouling in the furnace tubes and prepare features for the supervisory system. The supervisory control system is responsible for interfering between different components, evaluating the situation, and decision-making based on the unit status and process conditions. An intuitionistic fuzzy inference system is employed as the core of the supervisory controller to tolerate disturbance and faults by switching the control modes. Test studies using experimental data of the furnace indicate the capability of the proposed monitoring and control system to operate in various loading situations and recover the system from abnormal conditions.

Sensors

This paper investigates the problem of false data injection attack (FDIA) detection in microgrids... more This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it p...

An Overview of the State of the Art in Aircraft Prognostic and Health Management Strategies

IEEE Transactions on Instrumentation and Measurement

Fault Tolerant Control of Rhine-Meuse Delta Water System: A Performance Assessment Based Approach

2019 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET)

An occurrence of potential faults/hazardous situations could jeopardize the safety and reliabilit... more An occurrence of potential faults/hazardous situations could jeopardize the safety and reliability of complex dynamical systems. A new Fault Tolerant Control (FTC) methodology capable of preventing floods in the land areas close to the Rhine-Meuse Delta water system is proposed in this paper. The Delta water network is a Large-Scale System (LSS) with many barriers and sluices and is of enormous economic importance to Europe. Floods in this water network have damaged the system and cities around it. Thus, control of this complex water system is necessary. To monitor this complex system and detect any anomalies in a timely fashion, a fault diagnosis method using a Control Performance Index (CPI) is proposed for this large-scale water system. After fault diagnosis is performed, a switching mode control is devised to prevent potential flood situations. The switching controller is self-modified via the performance index information. Simulation tests are performed using experimental data from the aforementioned water system to examine the effectiveness of the suggested FTC method in comparison with the FTC using historical benchmark performance assessment method and the current controller of this water network system.

Maximum Power Point Tracker (MPPT) for Photovoltaic Power Systems-A Systematic Literature Review

2018 European Control Conference (ECC), 2018

Photovoltaic (PV) as a renewable source of energy plays a significant role in generating electric... more Photovoltaic (PV) as a renewable source of energy plays a significant role in generating electricities in the industry and distributed consumers. The output power of the PV device is highly nonlinear which is dependent on I-P and V-P characteristics of the device and also irradiation conditions. Therefore, many research works have been performed to optimize the performance and obtain maximum power from the PV panels. This paper provides a brief literature review on maximum power point tracker (MPPT) for the PV panels. For this purpose, the PV circuit structure with its mathematics model is presented. Then, recent publications on various design methodologies are reviewed.

New Condition-Based Monitoring and Fusion Approaches With a Bounded Uncertainty for Bearing Lifetime Prediction

IEEE Sensors Journal, 2022

Condition Monitoring (CM) is an essential element of securing reliable operating conditions of Wi... more Condition Monitoring (CM) is an essential element of securing reliable operating conditions of Wind Turbines (WT) in a wind farm. CM helps optimize maintenance by providing Remaining Useful Life (RUL) forecast. However, the expected RUL is not often reliable due to uncertainty associated with the prediction horizon. In this paper, we employ high-level fusion methods to expect the RUL of WT bearings. For this purpose, various features are extracted by vibration signals to capture deterioration paths. Then, a Bayesian algorithm is utilized to determine RUL for each selected feature. Eventually, high-level fusion schemes, including Hurwicz, Choquet integral, Ordered Weighted Averaging operator, are employed to integrate RUL numbers and lessen associated uncertainty in the prediction horizons. Besides, a pessimistic fusion strategy is driven to obtain a bounded uncertainty for the worst RUL prediction. The fusion methods are assessed by ten-year vibration signals of Canadian wind farms. Experimental results confirm accurate results with bounded uncertainty for high-level fusion approaches.

IEEE Transactions on Industrial Electronics, 2022

This paper deals with the problem of controller design for DC microgrids that feed constant power... more This paper deals with the problem of controller design for DC microgrids that feed constant power loads. To design the proposed controller, first by the use of the exact feedback linearization approach, the linear model of Brunovsky's canonical representation of the system has been obtained to address the nonlinearity problem of the system. Then, the desired control technique is developed by a combination of sliding mode and backstepping control approaches in which a nonlinear disturbance observer is utilized to estimate the disturbance. The overall stability of the system is analyzed based on the Lyapunov approach. A suitable and practical sliding surface is one of the controller strengths that allow the bus voltage to track the reference voltage with high accuracy and fast transient response. Finally, to prove the mentioned claims, an experimental setup has been constructed and the proposed controller is implemented. The experimental results have been analyzed and error analysis is performed. The results confirm the superiority of the proposed controller compared to state-of-the-art controllers.

A New Fault Diagnosis Approach for Heavy-Duty Gas Turbines

IEEE/ASME Transactions on Mechatronics, 2022

Effective fault detection, estimation, and iso6 lation are essential for the safety and reliabili... more Effective fault detection, estimation, and iso6 lation are essential for the safety and reliability of gas tur7 bines. In this article, a hybrid fault detection and isolation 8 (FDI) approach is presented for condition monitoring of 9 heavy-duty gas turbines. First, nonlinear dynamical models 10 are constructed using an orthonormal basis function and 11 an adaptive neuro-fuzzy inference system through exper12 imental data. Following that, a fuzzy inference system is 13 employed to estimate the fault severity. For this aim, fuzzy 14 rules are extracted from the fault patterns by defining ap15 propriate features. Later, various faults are isolated using 16 an ensemble decision tree classifier. The proposed non17 linear modeling compensates for disturbances and uncer18 tainty in the system and leads to adaptive thresholds for 19 fault detection, which reduces the false alarm rate. More20 over, the proposed FDImethod brings high accuracy in fault 21 estimation by properly modeling a bounded uncertainty us22 ing the adaptive threshold. Experimental data are applied 23 to validate the gas turbine model. Test results indicate that 24 the proposed hybrid FDI method via adaptive threshold 25 overwhelms the other FDImethods, where the misclassified data are 5.6%.

A Distributed Fault Detection And Isolation Method For Multifunctional Spoiler System

2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS), 2018

The increasing complexity of aircraft subsystems and control structure invoke new fault diagnosis... more The increasing complexity of aircraft subsystems and control structure invoke new fault diagnosis methodologies for these vehicles. Multifunctional spoiler (MFS) is an essential part of an aircraft spoiler control system that can be easily deteriorated due to faults which could consequently compromise the safety of the aircraft. The MFS consists of several components with highly nonlinear dynamics. This paper presents a new fault detection and isolation (FDI) system using dynamic neural networks (DNN) to deal with incipient faults at their early stages. For this purpose, an intelligent distributed FDI framework consisting of three DNNs is employed for generating residual set in the system to observe any discrepancy in the states of the system. Furthermore, the dynamic structure of the designed neural networks helps the observers tackle the non-linearity of the system and provides the fault isolation in the whole operating range. Simulation results are conducted to demonstrate the ability and effectiveness of the proposed FDI system.

Multi-Feature Fusion Approach for Epileptic Seizure Detection From EEG Signals

IEEE Sensors Journal, 2021

In this article, a new fusion scheme based on the Dempster–Shafer Evidence Theory (DSET) is intro... more In this article, a new fusion scheme based on the Dempster–Shafer Evidence Theory (DSET) is introduced for Epileptic Seizure Detection (ESD) in brain disorders. Firstly, various features in temporal, spectral, and temporal-spectral domains are extracted from Electroencephalogram (EEG) signals. Afterward, a Correlation analysis via the Pearson Correlation Coefficient (PCC) is conducted on the extracted features to select and remove highly correlated features. It leads to the second feature set with about half numbers of the first feature set. Next, three separate filter-type feature selection techniques, including Relief-F (RF), Compensation Distance Evaluation Technique (CDET), and Fisher Score (FS), are conducted to this second feature set for ranking features. Following that, a feature fusion is engaged by the DSET through the individual feature ranking results to generate high qualified feature sets. Indeed, the DSET-based feature fusion is devoted to enhancing the feature selection confidence using the least superb ranked features. In the classification stage, an Ensemble Decision Tree (EDT) classifier, along with two common validation procedures, including hold out and 10-fold cross-validation, is appropriated to classify the selected features from the EEG signals as normal, pre-ictal (epileptic background), and ictal (epileptic seizure) classes. Finally, several test scenarios are investigated using experimental data of Bonn University to evaluate the proposed ESD performance. Moreover, a comparison with other research works on the same dataset and classes is accomplished. The obtained results indicate the effectiveness of the proposed feature fusion approach and superior accuracy compared to the traditional methods.

IEEE Systems Journal, 2021

Background: To contribute to the current debate as to the relative influences of dietary intake a... more Background: To contribute to the current debate as to the relative influences of dietary intake and physical activity on the development of adiposity in community-based children. Methods: Participants were 734 boys and girls measured at age 8, 10 and 12 years for percent body fat (dual emission x-ray absorptiometry), physical activity (pedometers, accelerometers); and dietary intake (1 and 2-day records), with assessments of pubertal development and socioeconomic status. Results: Cross-sectional relationships revealed that boys and girls with higher percent body fat were less physically active, both in terms of steps per day and moderate and vigorous physical activity (both sexes p,0.001 for both measures). However, fatter children did not consume more energy, fat, carbohydrate or sugar; boys with higher percent body fat actually consumed less carbohydrate (p = 0.01) and energy (p = 0.05). Longitudinal analysis (combined data from both sexes) was weaker, but supported the cross-sectional findings, showing that children who reduced their PA over the four years increased their percent body fat (p = 0.04). Relationships in the 8 year-olds and also in the leanest quartile of all children, where adiposity-related underreporting was unlikely, were consistent with those of the whole group, indicating that underreporting did not influence our findings. Conclusions: These data provide support for the premise that physical activity is the main source of variation in the percent body fat of healthy community-based Australian children. General community strategies involving dietary intake and physical activity to combat childhood obesity may benefit by making physical activity the foremost focus of attention.

Critical Wind Turbine Components Prognostics: A Comprehensive Review

IEEE Transactions on Instrumentation and Measurement, 2020

As wind energy is becoming a significant utility source, minimizing the operation and maintenance... more As wind energy is becoming a significant utility source, minimizing the operation and maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to fossil fuels. Wind turbines (WTs) are subject to unexpected failures due to operational and environmental conditions, aging, and so on. An accurate estimation of time to failures assures reliable power production and lower maintenance costs. In recent years, a notable amount of research has been undertaken to propose prognosis techniques that can be employed to forecast the remaining useful life (RUL) of wind farm assets. This article provides a recent literature review on modeling developments for the RUL prediction of critical WT components, including physics-based, artificial intelligence (AI)-based, stochastic-based, and hybrid prognostics. In particular, the pros and cons of the prognosis models are investigated to assist researchers in selecting proper models for certain applications of WT RUL forecast. Our comprehensive review has revealed that hybrid methods are now the leading and most accurate tools for WT failure predictions over individual hybrid components. Strong candidates for future research include the consideration of variable operating environments, component interaction, physics-based prognostics, and the Bayesian application in the development of WT prognosis methods.

Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings Under Varying Operating Conditions

IEEE Transactions on Industrial Informatics, 2020

The failure progression of wind turbine bearings comprises of multiple degraded health states due... more The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm.

IEEE/ASME Transactions on Mechatronics, 2020

SMC Conference 2019, 2019

This paper introduces a cyber-secure strategy for radar tracking systems. Two common cyber attack... more This paper introduces a cyber-secure strategy for radar tracking systems. Two common cyber attacks including denial-of-service (DoS) and false data injection (deception) attacks are investigated. The proposed secure control strategy consists of two subsystems: 1) an attack detection and isolation (ADI) subsystem, and 2) a resilient observer (RO) subsystem. The ADI subsystem is used to observe the state of the system using a bank of Kalman Filters and multi-sensor measurements. Then, residuals generated by local Kalman filters are used to isolate the cyber attacks. Afterward, ordered weighted averaging
(OWA) operator is utilized to drive a resilient observer to estimate the real correct value of variables such as position under cyber attacks. Weighting factors of the OWA operator are derived using the covariance matrix, and proof of convergence is provided. Simulation studies on a radar tracking system show that the proposed secure control strategy using multi-sensor fusion enhances the performance of the system and results in a more resilient control system against cyber attacks.