Articles de revista
http://hdl.handle.net/2117/1125
2024-03-28T23:32:13Z
2024-03-28T23:32:13Z
Non-intrusive condition monitoring based on event detection and functional data clustering
Bermeo Ayerbe, Miguel Ángel
Ocampo-Martínez, Carlos
Díaz Rozo, Javier
http://hdl.handle.net/2117/405014
2024-03-25T01:34:52Z
2024-03-20T12:33:55Z
Non-intrusive condition monitoring based on event detection and functional data clustering
Bermeo Ayerbe, Miguel Ángel; Ocampo-Martínez, Carlos; Díaz Rozo, Javier
Implementing monitoring electricity consumption strategies in industrial environments provides improvements in both the maintenance process and energy efficiency. The contribution of this work is an industry-oriented non-intrusive load monitoring approach based on an unsupervised algorithm, encompassing a method of event detection, functional data clustering, and condition monitoring. With this method, multiple devices can be monitored by only one electric meter in industrial environments, enhancing the early detection of anomalies and energy inefficiencies. The proposed approach presents a robust event detection to deal with different industrial contexts due to its intuitive parameters, which allows adapting the detection to be more sensitive or to filter out higher noise variations. Unlike other feature-based clusterings, the proposed functional data clustering enables high-precision identification of transient state patterns, characterizing specific shapes for each pattern to properly cluster them, and provides higher reliability during load detection. Thus, this load detection segments the power consumption to extract transient states and identify which load acts on each event based on functional data clusters. By detecting when loads start to consume, the proposed energy disaggregation extracts the frequency spectrum of each device from the aggregate current consumption, which is used in a condition monitoring strategy to track the spectrum behavior of each device. In this way, the condition of multiple loads can be monitored using a single electric meter, whose information can be relevant to accurately schedule maintenance interventions and detect anomalies or inefficiencies early. The proposed approach was validated in three industrial contexts: The load detection accuracy was verified in an industrial testbed, giving a precision higher than 99% for monitoring five devices. The second and third industrial scenarios validate the accuracy of the proposed condition monitoring method. The last scenario was carried out at Bilbao airport to track the condition of multiple conveyor belts of a baggage handling system located at check-in. As a result, the degradation trend of three sets of conveyor belts was monitored.
2024-03-20T12:33:55Z
Bermeo Ayerbe, Miguel Ángel
Ocampo-Martínez, Carlos
Díaz Rozo, Javier
Implementing monitoring electricity consumption strategies in industrial environments provides improvements in both the maintenance process and energy efficiency. The contribution of this work is an industry-oriented non-intrusive load monitoring approach based on an unsupervised algorithm, encompassing a method of event detection, functional data clustering, and condition monitoring. With this method, multiple devices can be monitored by only one electric meter in industrial environments, enhancing the early detection of anomalies and energy inefficiencies. The proposed approach presents a robust event detection to deal with different industrial contexts due to its intuitive parameters, which allows adapting the detection to be more sensitive or to filter out higher noise variations. Unlike other feature-based clusterings, the proposed functional data clustering enables high-precision identification of transient state patterns, characterizing specific shapes for each pattern to properly cluster them, and provides higher reliability during load detection. Thus, this load detection segments the power consumption to extract transient states and identify which load acts on each event based on functional data clusters. By detecting when loads start to consume, the proposed energy disaggregation extracts the frequency spectrum of each device from the aggregate current consumption, which is used in a condition monitoring strategy to track the spectrum behavior of each device. In this way, the condition of multiple loads can be monitored using a single electric meter, whose information can be relevant to accurately schedule maintenance interventions and detect anomalies or inefficiencies early. The proposed approach was validated in three industrial contexts: The load detection accuracy was verified in an industrial testbed, giving a precision higher than 99% for monitoring five devices. The second and third industrial scenarios validate the accuracy of the proposed condition monitoring method. The last scenario was carried out at Bilbao airport to track the condition of multiple conveyor belts of a baggage handling system located at check-in. As a result, the degradation trend of three sets of conveyor belts was monitored.
Nonlinear observer for online concentration estimation in vanadium flow batteries based on half-cell voltage measurements
Puleston, Thomas Paul
Cecilia Piñol, Andreu
Costa Castelló, Ramon
Serra, Maria
http://hdl.handle.net/2117/404918
2024-03-25T01:43:53Z
2024-03-19T11:14:50Z
Nonlinear observer for online concentration estimation in vanadium flow batteries based on half-cell voltage measurements
Puleston, Thomas Paul; Cecilia Piñol, Andreu; Costa Castelló, Ramon; Serra, Maria
This paper presents a nonlinear observer to estimate the active species concentrations in vanadium flow batteries. To conduct the estimation, the observer relies only on current, flow rate and two half-cell voltage measurements. In contrast to previous works in the field, the proposed observer is capable to deal simultaneously with two significant and challenging conditions: (1) a not necessarily high flow rate, which results in different concentrations for tanks and cells, and (2) presence of crossover and oxidation side reactions, that result in imbalance between the electrolytes on the positive and negative sides of the system. The stability and convergence of the observer are formally demonstrated using a Lyapunov analysis and subsequently validated through comprehensive computer simulations. Finally, utilising the information provided by the observer, a strategy to independently regulate the flow rate of each electrolyte based on their individual state of charge is developed.
2024-03-19T11:14:50Z
Puleston, Thomas Paul
Cecilia Piñol, Andreu
Costa Castelló, Ramon
Serra, Maria
This paper presents a nonlinear observer to estimate the active species concentrations in vanadium flow batteries. To conduct the estimation, the observer relies only on current, flow rate and two half-cell voltage measurements. In contrast to previous works in the field, the proposed observer is capable to deal simultaneously with two significant and challenging conditions: (1) a not necessarily high flow rate, which results in different concentrations for tanks and cells, and (2) presence of crossover and oxidation side reactions, that result in imbalance between the electrolytes on the positive and negative sides of the system. The stability and convergence of the observer are formally demonstrated using a Lyapunov analysis and subsequently validated through comprehensive computer simulations. Finally, utilising the information provided by the observer, a strategy to independently regulate the flow rate of each electrolyte based on their individual state of charge is developed.
Scheduling inland waterway transport vessels and locks using a switching max-plus-linear systems approach
Segovia Castillo, Pablo
Pesselse, Mike
van den Boom, Ton
Reppa, Vasso
http://hdl.handle.net/2117/404429
2024-03-18T01:57:10Z
2024-03-13T12:26:28Z
Scheduling inland waterway transport vessels and locks using a switching max-plus-linear systems approach
Segovia Castillo, Pablo; Pesselse, Mike; van den Boom, Ton; Reppa, Vasso
This paper considers the inland waterborne transport (IWT) problem, and presents a scheduling approach for inland vessels and locks to generate optimal vessel and lock timetables. The scheduling strategy is designed in the switching max-plus-linear (SMPL) systems framework, as these are characterized by a number of features that make them well suited to represent the IWT problem. In particular, the resulting model is linear in the max-plus algebra, and SMPL systems can switch between modes, an interesting feature due to the presence of vessel routing and ordering constraints in the model. Moreover, SMPL systems can be transformed into mixed-integer linear programming (MILP) problems, for which efficient solvers are available. Finally, a realistic case study is used to test the approach and assess its effectiveness.
2024-03-13T12:26:28Z
Segovia Castillo, Pablo
Pesselse, Mike
van den Boom, Ton
Reppa, Vasso
This paper considers the inland waterborne transport (IWT) problem, and presents a scheduling approach for inland vessels and locks to generate optimal vessel and lock timetables. The scheduling strategy is designed in the switching max-plus-linear (SMPL) systems framework, as these are characterized by a number of features that make them well suited to represent the IWT problem. In particular, the resulting model is linear in the max-plus algebra, and SMPL systems can switch between modes, an interesting feature due to the presence of vessel routing and ordering constraints in the model. Moreover, SMPL systems can be transformed into mixed-integer linear programming (MILP) problems, for which efficient solvers are available. Finally, a realistic case study is used to test the approach and assess its effectiveness.
Optimal control of hybrid photovoltaic/thermal water system in solar panels using the linear parameter varying approach
Jamaaoui, Faycel
Puig Cayuela, Vicenç
Ayadi, Mounir
http://hdl.handle.net/2117/404397
2024-03-24T06:10:24Z
2024-03-13T10:54:32Z
Optimal control of hybrid photovoltaic/thermal water system in solar panels using the linear parameter varying approach
Jamaaoui, Faycel; Puig Cayuela, Vicenç; Ayadi, Mounir
During photovoltaic (PV) conversion in solar panels, a part of the solar radiation is not converted to electricity by the cells, producing heat that could increase their temperature. This increase in temperature deteriorates the performance of the PV panel. In this paper, a hybrid PV/thermal (PV/T) water system is proposed to mitigate this problem. This system combines a PV panel and a thermal collector. In this paper, we focused on the modeling and control of this hybrid system in the linear parameter varying (LPV) framework. An optimal linear quadratic regulator (LQR) is proposed to control the PV cell temperature around an optimal value that maximises electricity generation. Since the system model is nonlinear, an optimal LQR gain-scheduling state-feedback control approach based on an LPV representation of the nonlinear model is designed using the Linear Matrix Inequality (LMI) method. The goal is to obtain the maximum electrical power for each solar panel. Since a reduced number of sensors is available, an LPV Kalman filter is also proposed to estimate the system states required by the state-feedback controller. The obtained results in a laboratory setup in simulation are used to assess the proposed approach, showing promise in terms of control performance of the PV/T system.
2024-03-13T10:54:32Z
Jamaaoui, Faycel
Puig Cayuela, Vicenç
Ayadi, Mounir
During photovoltaic (PV) conversion in solar panels, a part of the solar radiation is not converted to electricity by the cells, producing heat that could increase their temperature. This increase in temperature deteriorates the performance of the PV panel. In this paper, a hybrid PV/thermal (PV/T) water system is proposed to mitigate this problem. This system combines a PV panel and a thermal collector. In this paper, we focused on the modeling and control of this hybrid system in the linear parameter varying (LPV) framework. An optimal linear quadratic regulator (LQR) is proposed to control the PV cell temperature around an optimal value that maximises electricity generation. Since the system model is nonlinear, an optimal LQR gain-scheduling state-feedback control approach based on an LPV representation of the nonlinear model is designed using the Linear Matrix Inequality (LMI) method. The goal is to obtain the maximum electrical power for each solar panel. Since a reduced number of sensors is available, an LPV Kalman filter is also proposed to estimate the system states required by the state-feedback controller. The obtained results in a laboratory setup in simulation are used to assess the proposed approach, showing promise in terms of control performance of the PV/T system.
Model predictive control of urban drainage systems considering uncertainty
Lorenz Svensen, Jan
Sun, Congcong
Cembrano Gennari, Gabriela
Puig Cayuela, Vicenç
http://hdl.handle.net/2117/404007
2024-03-11T00:15:05Z
2024-03-08T13:37:01Z
Model predictive control of urban drainage systems considering uncertainty
Lorenz Svensen, Jan; Sun, Congcong; Cembrano Gennari, Gabriela; Puig Cayuela, Vicenç
This brief contributes to the application of model predictive control (MPC) to address the combined sewer overflow (CSO) problem in urban drainage systems (UDSs) with uncertainty. In UDS, dealing with uncertainty in rain forecast and dynamic models is crucial due to the possible impact on the UDS control performance. Two different MPC approaches are considered: tube-based MPC (T-MPC) and chance-constrained MPC (CC-MPC), which represent uncertainty in deterministic and stochastic manners, respectively. This brief presents how to apply T-MPC to UDS, by establishing a mathematical relation with CC-MPC, and a rigorous mathematical comparison. Based on simulations using the Astlingen benchmark UDS, the strengths and weaknesses of the performance of T-MPC and CC-MPC in UDS were compared. Differences in the involved mathematical computations have also been analyzed. Moreover, the comparison in performance also indicates the applicability of each MPC approach in different uncertainty scenarios.
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
2024-03-08T13:37:01Z
Lorenz Svensen, Jan
Sun, Congcong
Cembrano Gennari, Gabriela
Puig Cayuela, Vicenç
This brief contributes to the application of model predictive control (MPC) to address the combined sewer overflow (CSO) problem in urban drainage systems (UDSs) with uncertainty. In UDS, dealing with uncertainty in rain forecast and dynamic models is crucial due to the possible impact on the UDS control performance. Two different MPC approaches are considered: tube-based MPC (T-MPC) and chance-constrained MPC (CC-MPC), which represent uncertainty in deterministic and stochastic manners, respectively. This brief presents how to apply T-MPC to UDS, by establishing a mathematical relation with CC-MPC, and a rigorous mathematical comparison. Based on simulations using the Astlingen benchmark UDS, the strengths and weaknesses of the performance of T-MPC and CC-MPC in UDS were compared. Differences in the involved mathematical computations have also been analyzed. Moreover, the comparison in performance also indicates the applicability of each MPC approach in different uncertainty scenarios.
A multilayer control strategy for the Calais canal
Segovia Castillo, Pablo
Puig Cayuela, Vicenç
Duviella, Eric
http://hdl.handle.net/2117/403798
2024-03-11T00:21:54Z
2024-03-06T10:00:47Z
A multilayer control strategy for the Calais canal
Segovia Castillo, Pablo; Puig Cayuela, Vicenç; Duviella, Eric
This article presents the design of a control strategy for the Calais canal, a navigation canal located in a lowland area in northern France that is affected by tides. Moreover, the available actuators are discrete-valued and the hierarchy of operational objectives is time-varying. All these circumstances render water level regulation of the Calais canal a challenging problem. In view of this situation, the design of the overall control architecture is divided into a sequence of structured tasks, which are distributed among layers. The upper layer determines the current operating mode based on the analysis of several environmental and operational aspects. Information regarding the current mode is taken into account at the intermediate layer to select the appropriate optimization-based control problem, which is solved using lexicographic minimization. The optimal control setpoints are determined and sent to the lower layer, where scheduling problems are solved to select low-level control actions from a finite set to minimize the mismatch with respect to the optimal setpoints. Different realistic simulation scenarios are tested to demonstrate the effectiveness of the proposed approach.
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
2024-03-06T10:00:47Z
Segovia Castillo, Pablo
Puig Cayuela, Vicenç
Duviella, Eric
This article presents the design of a control strategy for the Calais canal, a navigation canal located in a lowland area in northern France that is affected by tides. Moreover, the available actuators are discrete-valued and the hierarchy of operational objectives is time-varying. All these circumstances render water level regulation of the Calais canal a challenging problem. In view of this situation, the design of the overall control architecture is divided into a sequence of structured tasks, which are distributed among layers. The upper layer determines the current operating mode based on the analysis of several environmental and operational aspects. Information regarding the current mode is taken into account at the intermediate layer to select the appropriate optimization-based control problem, which is solved using lexicographic minimization. The optimal control setpoints are determined and sent to the lower layer, where scheduling problems are solved to select low-level control actions from a finite set to minimize the mismatch with respect to the optimal setpoints. Different realistic simulation scenarios are tested to demonstrate the effectiveness of the proposed approach.
An integrated design method for active fault diagnosis and control
Wang, Jing
Lv, Xueyan
Meng, Zhou
Puig Cayuela, Vicenç
http://hdl.handle.net/2117/403770
2024-03-11T00:18:46Z
2024-03-06T08:08:56Z
An integrated design method for active fault diagnosis and control
Wang, Jing; Lv, Xueyan; Meng, Zhou; Puig Cayuela, Vicenç
The injection of an auxiliary input signal for active fault diagnosis may cause the change of system control performance in closed-loop operation. This paper presents a novel integrated design method for active fault diagnosis and tracking control in order to facilitate the detection of incipient faults, meanwhile ensuring the normal operation of the closed-loop system. First, a zonotopic filter is generated based on radius minimization method to estimate the uncertain bound set of systems states. Then, an auxiliary input signal is designed to separate the reachable sets of the system in healthy and faulty operation situations. Generally, the auxiliary input signal is required to be designed minimizing the system closed-loop performance degradation. In this paper, a tracking controller is designed altogether with the auxiliary input signal to make the system output track a given reference. An optimal index is proposed to minimize simultaneously the energy of the auxiliary input signal and the output tracking error. Next, a fault detection logic is provided based on checking whether the closed-loop system state belongs to healthy zonotope. Finally, the feasibility and advantages of this work are verified by means of a numerical example.
2024-03-06T08:08:56Z
Wang, Jing
Lv, Xueyan
Meng, Zhou
Puig Cayuela, Vicenç
The injection of an auxiliary input signal for active fault diagnosis may cause the change of system control performance in closed-loop operation. This paper presents a novel integrated design method for active fault diagnosis and tracking control in order to facilitate the detection of incipient faults, meanwhile ensuring the normal operation of the closed-loop system. First, a zonotopic filter is generated based on radius minimization method to estimate the uncertain bound set of systems states. Then, an auxiliary input signal is designed to separate the reachable sets of the system in healthy and faulty operation situations. Generally, the auxiliary input signal is required to be designed minimizing the system closed-loop performance degradation. In this paper, a tracking controller is designed altogether with the auxiliary input signal to make the system output track a given reference. An optimal index is proposed to minimize simultaneously the energy of the auxiliary input signal and the output tracking error. Next, a fault detection logic is provided based on checking whether the closed-loop system state belongs to healthy zonotope. Finally, the feasibility and advantages of this work are verified by means of a numerical example.
Incipient fault diagnosis and trend prediction in nonlinear closed-loop systems with Gaussian and non-Gaussian noise
Safaeipour, Hossein
Forouzanfar, Mehdi
Puig Cayuela, Vicenç
Birgani, Pezhman Taghipour
http://hdl.handle.net/2117/403728
2024-03-11T00:23:53Z
2024-03-05T12:53:40Z
Incipient fault diagnosis and trend prediction in nonlinear closed-loop systems with Gaussian and non-Gaussian noise
Safaeipour, Hossein; Forouzanfar, Mehdi; Puig Cayuela, Vicenç; Birgani, Pezhman Taghipour
This paper proposes a methodology for incipient fault diagnosis and the corresponding trend prediction in nonlinear closed-loop systems considering stochastic Gaussian and non-Gaussian uncertainties. The proposed approach is based on the use of the particle filtering technique for estimating the system states and outputs. From these estimations, the residual signals would be generated through a mathematical filtering and augmentation technique, allowing the incipient fault estimation that is evaluated using the designed fixed and adaptive thresholds that consider system uncertainties. In this way, the fault detection performance is improved but also the false detection and false alarm problems are comprehensively addressed. Moreover, the augmented Gauss–Newton identification method is used for the incipient fault trend prediction. Finally, to evaluate the effectiveness of the proposed approach, the incipient fault diagnosis in the heat transfer unit built in the nonlinear closed-loop continuous stirred-tank reactor (CSTR) system is used. Besides, the confusion matrix is employed to assess the results from a quantitative point of view.
2024-03-05T12:53:40Z
Safaeipour, Hossein
Forouzanfar, Mehdi
Puig Cayuela, Vicenç
Birgani, Pezhman Taghipour
This paper proposes a methodology for incipient fault diagnosis and the corresponding trend prediction in nonlinear closed-loop systems considering stochastic Gaussian and non-Gaussian uncertainties. The proposed approach is based on the use of the particle filtering technique for estimating the system states and outputs. From these estimations, the residual signals would be generated through a mathematical filtering and augmentation technique, allowing the incipient fault estimation that is evaluated using the designed fixed and adaptive thresholds that consider system uncertainties. In this way, the fault detection performance is improved but also the false detection and false alarm problems are comprehensively addressed. Moreover, the augmented Gauss–Newton identification method is used for the incipient fault trend prediction. Finally, to evaluate the effectiveness of the proposed approach, the incipient fault diagnosis in the heat transfer unit built in the nonlinear closed-loop continuous stirred-tank reactor (CSTR) system is used. Besides, the confusion matrix is employed to assess the results from a quantitative point of view.
A supervisory control scheme for uncertain constrained time-delay discrete-time linear systems
Ahmadzadeh, Hamid Reza
Aghaei, Shahram
Puig Cayuela, Vicenç
http://hdl.handle.net/2117/403727
2024-03-11T00:16:11Z
2024-03-05T12:32:22Z
A supervisory control scheme for uncertain constrained time-delay discrete-time linear systems
Ahmadzadeh, Hamid Reza; Aghaei, Shahram; Puig Cayuela, Vicenç
In this paper, a two-layer supervisory control scheme is proposed for discrete-time linear systems with state/input constraints, including multiple state time-delays, parametric uncertainties, and exogenous disturbance. The inner control layer is designed to achieve robust H8 tracking performance considering the delays as extra states but neglecting the input/state constraints. On the other hand, in the outer control layer, a command governor (CG) is designed to robustly guarantee the satisfaction of state and input constraints by computing a minimal Robust Positively Invariant (mRPI). To this end, the CG manipulates reference inputs by generating the nearest admissible value to be applied to the closed-loop system in both transient and steady-state response through the computation of the Maximal Output Admissible Set (MOAS). Finally, the validity of the proposed scheme is assessed in simulation using a numerical example and a continuous stirred tank reactor (CSTR) system.
2024-03-05T12:32:22Z
Ahmadzadeh, Hamid Reza
Aghaei, Shahram
Puig Cayuela, Vicenç
In this paper, a two-layer supervisory control scheme is proposed for discrete-time linear systems with state/input constraints, including multiple state time-delays, parametric uncertainties, and exogenous disturbance. The inner control layer is designed to achieve robust H8 tracking performance considering the delays as extra states but neglecting the input/state constraints. On the other hand, in the outer control layer, a command governor (CG) is designed to robustly guarantee the satisfaction of state and input constraints by computing a minimal Robust Positively Invariant (mRPI). To this end, the CG manipulates reference inputs by generating the nearest admissible value to be applied to the closed-loop system in both transient and steady-state response through the computation of the Maximal Output Admissible Set (MOAS). Finally, the validity of the proposed scheme is assessed in simulation using a numerical example and a continuous stirred tank reactor (CSTR) system.
Robust MPC-RG for an autonomous racing vehicle considering obstacles and the battery state of charge
Samada Rigo, Sergio Emil
Puig Cayuela, Vicenç
Nejjari Akhi-Elarab, Fatiha
http://hdl.handle.net/2117/403711
2024-03-11T00:23:32Z
2024-03-05T09:24:37Z
Robust MPC-RG for an autonomous racing vehicle considering obstacles and the battery state of charge
Samada Rigo, Sergio Emil; Puig Cayuela, Vicenç; Nejjari Akhi-Elarab, Fatiha
The design of a controller able to deal with uncertainties and physical constraints plays an essential role in fast and complex systems. Then, a reference governor approach based on model predictive control (MPC-RG) for an autonomous racing vehicle is proposed. The MPC-RG guarantees constraint satisfaction and recursive feasibility online while including obstacle avoidance capability and energy-aware management by solving a multi-objective optimization problem. In particular, a trade-off between maximizing the longitudinal velocity and the state of charge of the vehicle’s battery, as well as minimizing the variation of control actions is adopted. Moreover, the proposed MPC-RG is combined with a state-feedback linear quadratic regulator (LQR) and a Kalman filter (KF) to compensate for modeling errors and exogenous disturbances, as well as to estimate the unmeasured lateral velocity. In fact, for control and estimation purposes, a data-driven Takagi–Sugeno (TS) model trained by an adaptive neuro-fuzzy inference network is used. The performance of the developed approach is assessed in simulations using a well-known case study based on a scale RC electric car.
2024-03-05T09:24:37Z
Samada Rigo, Sergio Emil
Puig Cayuela, Vicenç
Nejjari Akhi-Elarab, Fatiha
The design of a controller able to deal with uncertainties and physical constraints plays an essential role in fast and complex systems. Then, a reference governor approach based on model predictive control (MPC-RG) for an autonomous racing vehicle is proposed. The MPC-RG guarantees constraint satisfaction and recursive feasibility online while including obstacle avoidance capability and energy-aware management by solving a multi-objective optimization problem. In particular, a trade-off between maximizing the longitudinal velocity and the state of charge of the vehicle’s battery, as well as minimizing the variation of control actions is adopted. Moreover, the proposed MPC-RG is combined with a state-feedback linear quadratic regulator (LQR) and a Kalman filter (KF) to compensate for modeling errors and exogenous disturbances, as well as to estimate the unmeasured lateral velocity. In fact, for control and estimation purposes, a data-driven Takagi–Sugeno (TS) model trained by an adaptive neuro-fuzzy inference network is used. The performance of the developed approach is assessed in simulations using a well-known case study based on a scale RC electric car.