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© 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, In this paper, an assessment of two methods of uncertainty quantification in prognostics is undertaken. The two methods, the Inverse First Order Reliability Method (IFORM) and set-based reachability analysis for prognostics are considered. The IFORM approach permits the generation of confidence bounds that allows for the calculation of RUL values corresponding to the specified user-defined probability levels. On the other hand, uncertainty quantification can be achieved by means of set-based reachability analysis. A Zono-topic Kalman filter (ZKF) is proposed to take into account a damage-model such that at each propagation time, with the estimated state (degradation) and its uncertainty, a propagation of zonotopic sets can be produced. Coming from two different schools of thought, the statistical and set-based theory, both schemes are explored and tested on a case study in simulation., This work has been co-financed by the Spanish Research Agency (AEI) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00) and MASHED (TED2021-129927B-I00)., Peer Reviewed, Postprint (author's final draft)

(English) Maintenance is indispensable in every industry that uses machines and is inevitable regardless of the attention paid to it. The cumulative effect of its neglect or a non-optimized approach is a long or short-term shock on a company's growth. It, therefore, comes as no surprise, the overwhelming interest in the area of intelligent maintenance where comprehensive control units can detect, alert personnel, predict when a failure will occur, and manage faults to extend the life of components. Borne out of this is a new field of study, Prognostics and Health Management, which emerged only about two decades ago. This thesis contributes to this nascent field of study, and most importantly to a novel interest in the field of incorporating component prognostic information into control to manage the extent of influence degradation has on the efficient output of a plant. In sum, the thesis seeks to (1.) Contribute to component prognostics and how uncertainty can be efficiently handled and (2.) Promote the incorporation of prognostic information in a control scheme (i.e. Model Predictive Control). For prognostics, the thesis considers two critical components, a wind turbine blade composite material and an insulated gate bipolar transistor utilizing two different types of prognostic methods, the model and data-based methods respectively. Wind turbine blades are by far the most exposed component to damage predominately due to their level of mechanical activity in the turbine operation. Forces such as gyroscopic and gravity, debris in wind, and the effect of the stochastic nature of wind contribute to a gradual damaging effect culminating in a complete blade breakdown. Given that the blade material itself is innate, mathematical degradation equations dependent on material properties to predict the extent of the material damage in the absence of sensor information is used. Therefore, with a stiffness degradation algorithm aided by a zonotopic Kalman filter, the remaining, (Català) El manteniment és indispensable en tota indústria que utilitza màquines i és inevitable independentment de l'atenció que se li presti. L'efecte acumulat de la seva negligència o un enfocament no optimitzat és un xoc a llarg o curt termini en el creixement de l'empresa. Per tant, no sorprèn l'aclaparador interès en l'àmbit del manteniment intel·ligent on les unitats de control integrals poden detectar, alertar el personal, predir quan es produirà una fallada i gestionar les avaries per allargar la vida útil dels components. D'això neix un nou camp d'estudi, Pronòstic i gestió de la salut, que va sorgir fa només unes dues dècades. Aquesta tesi contribueix a aquest camp d'estudi naixent i, sobretot, a un nou interès en l'àrea d'incorporar informació pronòstica dels components al control per gestionar l'abast de la influència que té la degradació en la producció eficient d'una planta. En resum, la tesi pretén (1.) Contribuir al pronòstic dels components i com es pot gestionar de manera eficient la incertesa i (2.) Promoure la incorporació d'informació pronòstica en un esquema de control (és a dir, Model Predictive Control). Per al pronòstic, la tesi considera dos components crítics, un material compost de pales d'aerogenerador i un transistor bipolar de porta aïllada que utilitza dos tipus diferents de mètodes de pronòstic, el model i els mètodes basats en dades, respectivament. Les pales de l'aerogenerador són, amb diferència, el component més exposat a danys, principalment pel seu nivell d'activitat mecànica en el funcionament de la turbina. Forces com la giroscòpica i la gravetat, les deixalles del vent i l'efecte de la naturalesa estocàstica del vent contribueixen a un efecte danyós gradual que culmina amb una ruptura completa de la paleta. Atès que el material de la fulla en si és innat, s'utilitzen equacions de degradació matemàtiques que depenen de les propietats del material per predir l'abast del dany material en absència d'informació del sensor. Per t, Postprint (published version)

Maintenance is indispensable in every industry that uses machines and is inevitable regardless of the attention paid to it. The cumulative effect of its neglect or a non-optimized approach is a long or short-term shock on a company's growth. It, therefore, comes as no surprise, the overwhelming interest in the area of intelligent maintenance where comprehensive control units can detect, alert personnel, predict when a failure will occur, and manage faults to extend the life of components. Borne out of this is a new field of study, Prognostics and Health Management, which emerged only about two decades ago. This thesis contributes to this nascent field of study, and most importantly to a novel interest in the field of incorporating component prognostic information into control to manage the extent of influence degradation has on the efficient output of a plant. In sum, the thesis seeks to (1.) Contribute to component prognostics and how uncertainty can be efficiently handled and (2.) Promote the incorporation of prognostic information in a control scheme (i.e. Model Predictive Control). For prognostics, the thesis considers two critical components, a wind turbine blade composite material and an insulated gate bipolar transistor utilizing two different types of prognostic methods, the model and data-based methods respectively. Wind turbine blades are by far the most exposed component to damage predominately due to their level of mechanical activity in the turbine operation. Forces such as gyroscopic and gravity, debris in wind, and the effect of the stochastic nature of wind contribute to a gradual damaging effect culminating in a complete blade breakdown. Given that the blade material itself is innate, mathematical degradation equations dependent on material properties to predict the extent of the material damage in the absence of sensor information is used. Therefore, with a stiffness degradation algorithm aided by a zonotopic Kalman filter, the remaining useful lif

Recent years have seen a great deal of innovation in the field of systems prognostics and health management. However, even with these advancements, some pertinent issues related with uncertainty in remaining useful life predictions are still open for investigation. One such area of interest is on how to account for the distribution of these predictions such that all uncertainty sources are duly captured and represented. Practically, these uncertainty quantification procedures must be computationally feasible for real-life deployment and reflect real-life situations devoid of strong assumptions. This article thus, proposes a data-based prognostics technique that uses a set-based quantification of uncertainty based on the set-membership paradigm, the interval predictor approach. The methodology is applied in the framework of the Evolving Ellipsoidal Fuzzy Information Granule which has recently proven its potency in prognostics applications. As a case study, the method is tested on the prognostics of insulated bipolar transistors utilising an accelerated aging IGBT dataset from the NASA Ames Research Center., Peer Reviewed, Postprint (author's final draft)

The increasing application of power converter systems based on semiconductor devices such as Insulated-Gate Bipolar Transistors (IGBTs) has motivated the investigation of strategies for their prognostics and health management. However, physicsbased degradation modelling for semiconductors is usually complex and depends on uncertain parameters, which motivates the use of data-driven approaches. This paper addresses the problem of data-driven prognostics of IGBTs based on evolving fuzzy models learned from degradation data streams. The model depends on two classes of degradation features: one group of features that are very sensitive to the degradation stages is used as a premise variable of the fuzzy model, and another group that provides good trendability and monotonicity is used for the auto-regressive consequent of the fuzzy model for degradation prediction. This strategy allows obtaining interpretable degradation models, which are improved when more degradation data is obtained from the Unit Under Test (UUT) in real time. Furthermore, the fuzzy-based Remaining Useful Life (RUL) prediction is equipped with an uncertainty quantification mechanism to better aid decisionmakers. The proposed approach is then used for the RUL prediction considering an accelerated aging IGBT dataset from the NASA Ames Research Center., Postprint (published version)

This paper presents a model-based prognostics procedure using a zonotopic Kalman filter in tandem with a zonotopic set-based propagation of degradation, aiding in the quantification of uncertainties associated with prognostics. The prognostics procedure is then applied to the degradation of a wind turbine blade material subjected to a forecasted bounded set description of wind profile. To facilitate an online condition based implementation, an otherwise nonlinear based Kalman filter from the nonlinear wind turbine model is presented in a pseudolinear form, a polytopic linear parameter varying representation, decreasing computational cost and easing in the propagation of the positive invariant zonotopic uncertainty sets to a reachable set that triggers an end of life. Using this information of health, the remaining useful life with its associated uncertainties can be predicted., Peer Reviewed, Postprint (published version)

A robust economic model predictive control approach that takes into account the reliabilityof actuators in a network ispresented for the control of a drinking water network in the presence of uncertainties in the forecasted demands required forthe predictive control design. The uncertain forecasted demand on the nominal MPC may make the optimization processintractable or, to a lesser extent, degrade the controller performance. Thus, the uncertainty on demand is taken into accountand considered unknown but bounded in a zonotopic set. Based on this uncertainty description, a robust MPC is formulatedto ensure robust constraint satisfaction, performance, stability as well as recursive feasibility throughthe formulation ofan online tube-based MPC and an accompanying appropriate terminal set. Reliability is thenmodelled based on Bayesiannetworks, such that the resulting nonlinear function accommodated in the optimization setup is presented in a pseudo-linearform by means of a linear parameter varying representation, mitigating any additional computational expense thanks to theformulation as a quadratic optimization problem. With the inclusion of a reliability index to the economic dominant cost ofthe MPC, the network users’ requirements are met whilst ensuring improved reliability, therefore decreasing short and longterm operational costs for water utility operators. Capabilities of the designed controller are demonstrated with simulatedscenarios on the Barcelona drinking water network, Peer Reviewed, Postprint (published version)

This work proposes an economic model predictive control (EMPC) strategy in the linear parameter varying (LPV) framework for the control of dissolved oxygen concentrations in the aerated reactors of a wastewater treatment plant (WWTP). A reduced model of the complex nonlinear plant is represented in a quasi-linear parameter varying (qLPV) form to reduce computational burden, enabling the real-time operation. To facilitate the formulation of the time-varying parameters which are functions of system states, as well as for feedback control purposes, a moving horizon estimator (MHE) that uses the qLPV WWTP model is proposed. The control strategy is investigated and evaluated based on the ASM1 simulation benchmark for performance assessment. The obtained results applying the EMPC strategy for the control of the aeration system in the WWTP of Girona (Spain) show its effectiveness., This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020- 114244RB-I00), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014–2020 (ref. 001-P-001643 Looming Factory), and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482)., Peer Reviewed, Postprint (author's final draft)

This work proposes an economic model predictive control (EMPC) strategy in the linear parameter varying (LPV) framework for the control of dissolved oxygen concentrations in the aerated reactors of a wastewater treatment plant (WWTP). A reduced model of the complex nonlinear plant is represented in a quasi-linear parameter varying (qLPV) form to reduce computational burden, enabling the real-time operation. To facilitate the formulation of the time-varying parameters which are functions of system states, as well as for feedback control purposes, a moving horizon estimator (MHE) that uses the qLPV WWTP model is proposed. The control strategy is investigated and evaluated based on the ASM1 simulation benchmark for performance assessment. The obtained results applying the EMPC strategy for the control of the aeration system in the WWTP of Girona (Spain) show its effectiveness.

Khoury Boutros et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., Wind turbines generally operate under adverse conditions making them prone to relatively high failure rates. Due to the direct exposure of the blades to dynamic and cyclic loads of wind, the rotor and the blades unsurprisingly represent the most common major component damages of a wind turbine system, which is especially enhanced when located offshore. This paper presents a new model-based prognosis procedure based on a zonotopic Kalman filter (ZKF), which combines a physical model with observed data to assess the system degradation. Using this information and the model of the system, the end of life (EOL) and the remaining useful life (RUL) with its uncertainty can be predicted. The proposed prognostic method is applied to monitor the state of health of a wind turbine system specifically, its blades. The remaining useful life prediction will help in scheduling optimal maintenance and reducing the cost caused by wind turbine damage and unplanned shutdown., This work has been funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SCAV (ref. MINECO DPI2017- 88403-R), by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482) and by SMART Project (ref. num. EFA153/16 Interreg Cooperation Program POCTEFA 2014- 2020)., Peer Reviewed, Postprint (published version)

© 2020 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., In this paper, a robust economic model predictive controller is proposed for a water transport network. Considering that forecast of demand is required in the drinking water network (DWN) for future predictions of states in the MPC formulation, robustification of the proposed controller is undertaken considering demand uncertainties as unknown but bounded in a zonotopic set. Based on this uncertainty description, a robust MPC controller is designed that ensures that robust constraint satisfaction, stability and performance under uncertainties is guaranteed. The proposed approach is satisfactorily tested in a part of the Barcelona water transport network., Peer Reviewed, Postprint (author's final draft)

Wind turbine components are subject to considerable stress and fatigue due to extreme environmental conditions to which they are exposed to, especially when located offshore. Interest in the integration of control with system health monitoring has increased in recent years. The integration of a health management module with model predictive control (MPC) provides the wind turbine a mechanism to operate safely and optimize the trade-off between components’ life and energy production. In this paper, a health-aware LPV model predictive control approach for wind turbines is proposed. The proposed controller establishes a trade-off between the economic objective based on maximizing the energy production but at the same time taking into account the minimization of accumulated stress on the wind turbine blades. The controller uses an LPV model for dealing with the non-linearity of the wind turbine model and the inclusion of the stress model. The proposed approach is tested on a well-known wind turbine case., This work has been funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SCAV (ref. MINECO DPI2017-88403- R) and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482)., Peer Reviewed, Postprint (author's final draft)

A robust economic Model Predictive Control (EMPC) approach is presented in this paper for the control of a Drinking Water Network (DWN) albeit the presence of uncertainties in the forecasted demands required for the predictive control design. The uncertain forecasted demand on the nominal MPC has the possibility of rendering the optimization process infeasible or degrade the controller performance. In this paper, the uncertainty on demand is considered unknown but bounded in a zonotopic set. Based on this uncertainty description, a robust MPC is formulated to ensure robust constraint satisfaction, performance and stability of the MPC for DWN to meet user requirements whilst ensuring lower operational cost for water utility operators., Peer Reviewed, Postprint (author's final draft)