Ângelo Teixeira | Instituto Superior Técnico (original) (raw)

Papers by Ângelo Teixeira

International Journal of Forecasting, 2022

Abstract Despite the extensive amount of data generated and stored during the maintenance capacit... more Abstract Despite the extensive amount of data generated and stored during the maintenance capacity planning process, Maintenance, Repair, and Overhaul (MRO) organizations have yet to explore their full potential in forecasting the required capacity to face future and unprecedented maintenance interventions. This paper explores the integration of time series forecasting capabilities in a tool for maintenance capacity planning of complex product systems (CoPS), intended to value data that is routinely generated and stored, but often disregarded by MROs. State space formulations with multiplicative errors for the simple exponential smoothing (SES), Holt’s linear method (HLM), additive Holt-Winters (AHW), and multiplicative Holt-Winters (MHW) are assessed using real data, comprised of 171 maintenance projects collected from a major Portuguese aircraft MRO. A state space formulation of the MHW, selected using the bias-corrected Akaike information criterion (AICc), is integrated in a Decision Support System (DSS) for capacity planning with probabilistic inference capabilities and used to forecast the workload probability distribution of a future and unprecedent maintenance intervention. The developed tool is validated by comparing forecasted values with workloads of a particular maintenance intervention and with a model simulating current forecasting practices employed by MROs.

Volume 11B: Honoring Symposium for Professor Carlos Guedes Soares on Marine Technology and Ocean Engineering, 2018

A detail procedure to study mooring line strength reliability is presented. A fully coupled analy... more A detail procedure to study mooring line strength reliability is presented. A fully coupled analysis is carried out to get the mooring tensions of a deep water semi-submersible floating systems operated in 100 year wave condition in South China Sea. The ACER method is applied to predict the 3h extreme mooring tension, and the results are validated by global maximum method. The hydrodynamic sampling points are generated by Latin Hypercube Sampling technique. The 3h extreme mooring tension is calculated by the ACER method with 10 minutes fully coupled dynamic simulation for each sampling point. The Kriging meta model method is trained to predict 3h mooring extreme tension under the effects of random hydrodynamic drag coefficients. A reliability analysis is carried out by implementing Monte Carlo simulation with the random hydrodynamic drag coefficients and mooring breaking strength considered.

Reliability Engineering & System Safety, 2021

Abstract The paper proposes a probabilistic framework for assessing the risk of ships based on a ... more Abstract The paper proposes a probabilistic framework for assessing the risk of ships based on a hybrid approach and multiple data sources. A Bayes-based network learning approach uses data from the New Inspection Regime of the Paris MoU on Port State Control to characterise the relationships among risk parameters and uses these parameters to evaluate the ship static risk. Other data sources are used to develop a Bayesian Network model to assess the dynamic risk of the ship. The data is aggregated by Bayesian Network and Evidential Reasoning approaches to evaluate the overall risk of ships in coastal waters. The objective of the study is to develop a model to assess the risk of an individual ship by considering its static risk profile and the geographical-dependant risk factors related to the characteristics of the maritime traffic flow and other local characteristics that influence the navigational risk of the ship. The results show that the integrated approach is able to assess the overall risk of a ship based on multiple data sources, providing empirical evidence of using multiple data sources in risk analysis applications. Moreover, the developed model identifies the most critical circumstances and the key impact factors in the study waters, which can support decisions on risk prevention and mitigation measures and local maritime traffic management.

Reliability Engineering & System Safety, 2021

Reliability Engineering & System Safety, 2020

Abstract This paper proposes a probabilistic approach for characterising the static risk of indiv... more Abstract This paper proposes a probabilistic approach for characterising the static risk of individual ships based on Bayesian networks (BNs). The approach uses the Ship Risk Profile parameters of the New Inspection Regime of the Paris Memorandum of Understanding (MoU) on Port State Control (PSC), not as risk factors for ship selection in PSC inspections, but as risk variables for ship risk assessment and maritime traffic monitoring. The objectives of the proposed approach are threefold: the characterisation of the static risk profile of the maritime traffic crossing a given geographic area; the identification of the most likely circumstances under which a specific static risk profile is expected to occur; and the characterisation of the static risk profile of individual ships in the presence of incomplete information, such as that obtained from the Automatic Identification System. A dataset collected from the Paris MoU platform is used for the development of the BN model and its validity is assessed. A quantitative assessment for the predictive validity of the model is also conducted by a sensitivity analysis that shows the consistency of the model with the Ship Risk Profile criteria and with the results of other studies developed also from historical PSC inspection data.

Decision Support Systems, 2020

This paper proposes a decision support tool for maintenance capacity planning of complex product ... more This paper proposes a decision support tool for maintenance capacity planning of complex product systems. The tool-ForeSim-BI-addresses the problem faced by maintenance organizations in forecasting the workload of future maintenance interventions and in planning an adequate capacity to face that expected workload. Developed and implemented from a predictive analytics perspective in the particular context of a Portuguese aircraft maintenance organization, the tool integrates four main modules: (1) a forecasting module used to predict future and unprecedented maintenance workloads from historical data; (2) a Bayesian inference module used to transform prior workload forecasts, resulting from the forecasting module, into predictive forecasts after observations on the maintenance interventions being predicted become available; (3) a simulation module used to characterize the forecasted total workloads through sets of random variables, including maintenance work types, maintenance work phases, and maintenance work skills; and (4) a Bayesian network module used to combine the simulated workloads with historical data through probabilistic inference. A linear programming model is also developed to improve the efficiency of the decision-making process supported by Bayesian networks. The tool uses real industrial data, comprising 171 aircraft maintenance projects collected at the host organization, and is validated by comparing its results with real observations of a given maintenance intervention to which predictions were made and with a model simulating current forecasting practices employed in industry. Significantly more accurate forecasts have been obtained with the proposed tool, resulting in an important cost saving potential for maintenance organizations. practice [1]. Al-Fares and Duffuaa [7] divide these approaches between deterministic and stochastic. Applied to maintenance contexts, examples of the former include the work of Dijkstra et al. [8], in which a decision support system based on optimization models is developed to determine the size and composition of aircraft maintenance teams, or the work of Yan et al. [9], in which a mathematical programming model is developed considering multiple types of maintenance certificates. An example of the latter include the work of De Bruecker et al. [10], that addresses an aircraft maintenance workforce problem with mixed integer linear programming and heuristic techniques, or the work of Kurz [11], that presents an aircraft engine overhaul capacity planning problem modelled as a queuing network. Not specifically directed at maintenance, De Bruecker et al. [12] presents an extensive literature review regarding workforce planning problems. As recognized by Al-Fares and Duffuaa [7], forecasting is an essential component of the capacity planning process, as it allows MROs

SIMULATION, 2018

The paper addresses repairable multi-unit systems with a series–parallel configuration for which ... more The paper addresses repairable multi-unit systems with a series–parallel configuration for which maintenance strategies are modeled by generalized stochastic Petri nets (GSPN) with predicates coupled with Monte Carlo simulation. Four maintenance strategies consisting of basic periodic preventive and corrective maintenance, and both combined with opportunistic maintenance (OM) strategies, are considered. Failure and repair distributions of the system components are independent, and repairs are considered to be perfect. Times to failure of degraded components follow a Weibull distribution with increasing failure rate over time. The maintenance strategies are optimized so as to minimize the total maintenance costs of the system while maximizing availability. A comparison is drawn between OM and non-OM. The aim is to show that GSPN with predicates, in combination with Monte Carlo simulation, is a powerful, flexible, efficient, and intuitive approach for modeling and optimizing practical...

TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 2019

This paper proposes an approach for identifying and characterizing shipping routes using informat... more This paper proposes an approach for identifying and characterizing shipping routes using information contained in Automatic Identification System messages broadcasted by ships and recorded by the coastal Vessel Traffic Service centre. The approach consists of using historical Automatic Identification System data to build a graph, where nodes are cells of a grid covering the geographical area being studied and the weights of directional edges are inversely related to ship movements between cells. Based on this graph, the Dijkstra algorithm is used to identify a potential safe route, assumed to be the most used route by ships between two locations. A second graph is created simultaneously, with the same nodes and edges, but with edge weights equal to the average speed of transitions between cells, thus allowing the determination of the average speed profile for any possible path within the graph. The proposed approach is applied to two scenarios: an approach to the port of Lisbon and the entry through the fairway to a RO-RO terminal in the port of Setubal in Portugal. http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 13 Number 3

International Journal of Production Economics, 2019

This paper proposes a framework for the qualitative and quantitative characterization of maintena... more This paper proposes a framework for the qualitative and quantitative characterization of maintenance work to support Maintenance, Repair, and Overhaul (MRO) organizations in performing capacity planning and scheduling. A quantitative assessment based on 372 maintenance projects collected at a Portuguese aircraft MRO confirms that a significant part of the maintenance work is stochastic in nature, given the amount of unscheduled maintenance. The proposed framework, entitled FRamework for Aircraft Maintenance Estimation (FRAME), is intended to allow MROs in managing this uncertainty throughout the maintenance planning process and comprises for that end a set of requirements for data treatment and a method for data analysis. The established requirements address important shortcomings found in the collected data that prevented the use of maintenance data for capacity planning and scheduling as is. The developed method for data analysis, entitled 3-Dimensional Maintenance Data Analysis (3D-MDA), is based on a space-time-skill coordinate system in which indicators are calculated from historical data to comprehensively characterize the expected maintenance work. Space refers to the aircraft work zone where maintenance is performed, time refers to the project work phase when maintenance is performed, and skill refers to the type of technicians required for maintenance to be performed. The established coordinates address the limitations of reviewed techniques by allowing accurate estimations of required resources for capacity planning and an extended range of constraints for maintenance scheduling. Being generic in nature, FRAME is applicable to maintenance in other industries, or even to other activities with due adaptations.

Computers & Industrial Engineering, 2018

Highlights The capacity planning problem faced by aircraft MRO companies is described. Bayesian n... more Highlights The capacity planning problem faced by aircraft MRO companies is described. Bayesian networks to address the capacity planning problem are developed. A validation process for Bayesian networks is proposed. Examples of the applicability of the developed Bayesian networks are presented.

Reliability Engineering & System Safety, 2018

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A time-variant fatigue reliability assessment model for welded joints is proposed.  The PHI2 method is adopted to solve the time-variant problem.  The crack sizes are explicitly represented by response surface models.  Residual stress effects on time-variant fatigue reliability are significant.

Reliability Engineering & System Safety, 2016

This paper presents an approach that more adequately incorporates human factor considerations int... more This paper presents an approach that more adequately incorporates human factor considerations into quantitative risk analysis of ship operation. The focus is on the collision accident category, which is one of the main risk contributors in ship operation. The approach is based on the development of a Bayesian Network (BN) model that integrates elements from the Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr) and focuses on the calculation of the collision accident probability due to human error. The model takes into account the human performance in normal, abnormal and critical operational conditions and implements specific tasks derived from the analysis of the task errors leading to the collision accident category. A sensitivity analysis is performed to identify the most important contributors to human performance and ship collision. Finally, the model developed is applied to assess the collision risk of a feeder operating in Dover strait using the collision probability estimated by the developed BN model and an Event tree model for calculation of human, economic and environmental risks.

Reliability Engineering & System Safety, 2017

In the present paper an adaptive Kriging surrogate model with active refinement is proposed to so... more In the present paper an adaptive Kriging surrogate model with active refinement is proposed to solve component reliability analysis problems (i.e. with a single design point) with a reasonable limit for the dimensionality of the basic random variables space. The model uses an adaptive Kriging-based trust region method to search for the design point and predict the failure probability based on the first-order reliability method. This prediction is then verified or improved using Monte Carlo simulation with importance sampling based on a Kriging surrogate model built up iteratively around the design point using an active refinement algorithm. The usefulness of the proposed surrogate model in terms of accuracy and efficiency for practical engineering applications is shown with a numerical example involving an advanced nonlinear FEA structural model.

Green Energy and Technology, 2016

This chapter starts by shortly addressing the statistics of accidents and component failures of w... more This chapter starts by shortly addressing the statistics of accidents and component failures of wind turbine structures based on a comprehensive dataset publicly available. The distribution of the types of offshore wind turbine structures installed in European waters is given. The operation and maintenance of fixed structures foundations is discussed. Then, the failure data of main subassemblies of wind turbines are presented and discussed, followed by a description of available and important condition monitoring systems, techniques and methods for operation and maintenance of wind turbines. Finally, the knowledge on modelling, simulation and optimization of operation and maintenance actions of fixed offshore wind turbines is discussed as a basis for the application in the operation and maintenance of floating offshore wind turbines.

Advanced Ship Design for Pollution Prevention, 2010

Structure and Infrastructure Engineering, 2014

The use of structural reliability methods with implicit limit state functions (LSFs) shows the in... more The use of structural reliability methods with implicit limit state functions (LSFs) shows the increasing demand for efficient stochastic analysis tools, because the structural behaviour predictions are often obtained by finite element analysis. All stochastic mechanics problems can be solved by Monte Carlo simulation method, nevertheless, in most cases, at a prohibitively high computational cost. Several approximations can be achieved using first-order reliability method (FORM) and second-order reliability method and response surface methods. In this paper, a method that combines the FORM and Kriging interpolation models, as response surface, is proposed. The prediction accuracy of the Kriging response surface obtained from different sampling techniques is assessed, and the failure probability estimates calculated by the FORM using the classical second-order polynomial regression models and the Kriging interpolation models as surrogates of nonlinear LSFs are compared. The usefulness and efficiency of the reliability analysis using the Kriging response surface are demonstrated on the basis of existing results available in the literature and with an application problem of a stiffened plate structure with initial imperfections.

Reliability Engineering and System Safety, 2007

ABSTRACT The use of an integral measure of initial deflections of thin plates, based on the strai... more ABSTRACT The use of an integral measure of initial deflections of thin plates, based on the strain energy, has proved to be of importance in studying the influence of the imperfections on the strength of plates subjected to in-plane compression. The energy measure allows for a straightforward definition of compound modes and for a computational determination of the lower bound strength. The last quantity may be essential for assessment of plate design strength considering the imperfections as a random field.

Operation and maintenance (O&M) activities have a significant impact on the energy cost f... more Operation and maintenance (O&M) activities have a significant impact on the energy cost for offshore wind turbines. Analytical methods such as reliability block diagrams and Markov processes along with simulation approaches have been widely used in planning and optimizing O&M actions in industrial systems. Generalized stochastic Petri Nets (GSPN) with predicates coupled with Monte Carlo simulation (MCS) are applied in this paper to model the planning of O&M activities of an offshore wind turbine. The merits of GSPN in modeling complex, multi-state and multi-component systems are addressed. Three maintenance categories classified according to the size and weight of the components to be replaced and the logistics involved, such as vessels, maintenance crew and spares and, the associated delays and costs are included in the model. The weather windows for accessing the wind turbine are also modeled. Corrective maintenance based on replacements and age imperfect preventive maintenance are modeled and compared in terms of the wind turbine’s performance (e.g. availability and loss production) and of the O&M costs.

International Journal of Forecasting, 2022

Abstract Despite the extensive amount of data generated and stored during the maintenance capacit... more Abstract Despite the extensive amount of data generated and stored during the maintenance capacity planning process, Maintenance, Repair, and Overhaul (MRO) organizations have yet to explore their full potential in forecasting the required capacity to face future and unprecedented maintenance interventions. This paper explores the integration of time series forecasting capabilities in a tool for maintenance capacity planning of complex product systems (CoPS), intended to value data that is routinely generated and stored, but often disregarded by MROs. State space formulations with multiplicative errors for the simple exponential smoothing (SES), Holt’s linear method (HLM), additive Holt-Winters (AHW), and multiplicative Holt-Winters (MHW) are assessed using real data, comprised of 171 maintenance projects collected from a major Portuguese aircraft MRO. A state space formulation of the MHW, selected using the bias-corrected Akaike information criterion (AICc), is integrated in a Decision Support System (DSS) for capacity planning with probabilistic inference capabilities and used to forecast the workload probability distribution of a future and unprecedent maintenance intervention. The developed tool is validated by comparing forecasted values with workloads of a particular maintenance intervention and with a model simulating current forecasting practices employed by MROs.

Volume 11B: Honoring Symposium for Professor Carlos Guedes Soares on Marine Technology and Ocean Engineering, 2018

A detail procedure to study mooring line strength reliability is presented. A fully coupled analy... more A detail procedure to study mooring line strength reliability is presented. A fully coupled analysis is carried out to get the mooring tensions of a deep water semi-submersible floating systems operated in 100 year wave condition in South China Sea. The ACER method is applied to predict the 3h extreme mooring tension, and the results are validated by global maximum method. The hydrodynamic sampling points are generated by Latin Hypercube Sampling technique. The 3h extreme mooring tension is calculated by the ACER method with 10 minutes fully coupled dynamic simulation for each sampling point. The Kriging meta model method is trained to predict 3h mooring extreme tension under the effects of random hydrodynamic drag coefficients. A reliability analysis is carried out by implementing Monte Carlo simulation with the random hydrodynamic drag coefficients and mooring breaking strength considered.

Reliability Engineering & System Safety, 2021

Abstract The paper proposes a probabilistic framework for assessing the risk of ships based on a ... more Abstract The paper proposes a probabilistic framework for assessing the risk of ships based on a hybrid approach and multiple data sources. A Bayes-based network learning approach uses data from the New Inspection Regime of the Paris MoU on Port State Control to characterise the relationships among risk parameters and uses these parameters to evaluate the ship static risk. Other data sources are used to develop a Bayesian Network model to assess the dynamic risk of the ship. The data is aggregated by Bayesian Network and Evidential Reasoning approaches to evaluate the overall risk of ships in coastal waters. The objective of the study is to develop a model to assess the risk of an individual ship by considering its static risk profile and the geographical-dependant risk factors related to the characteristics of the maritime traffic flow and other local characteristics that influence the navigational risk of the ship. The results show that the integrated approach is able to assess the overall risk of a ship based on multiple data sources, providing empirical evidence of using multiple data sources in risk analysis applications. Moreover, the developed model identifies the most critical circumstances and the key impact factors in the study waters, which can support decisions on risk prevention and mitigation measures and local maritime traffic management.

Reliability Engineering & System Safety, 2021

Reliability Engineering & System Safety, 2020

Abstract This paper proposes a probabilistic approach for characterising the static risk of indiv... more Abstract This paper proposes a probabilistic approach for characterising the static risk of individual ships based on Bayesian networks (BNs). The approach uses the Ship Risk Profile parameters of the New Inspection Regime of the Paris Memorandum of Understanding (MoU) on Port State Control (PSC), not as risk factors for ship selection in PSC inspections, but as risk variables for ship risk assessment and maritime traffic monitoring. The objectives of the proposed approach are threefold: the characterisation of the static risk profile of the maritime traffic crossing a given geographic area; the identification of the most likely circumstances under which a specific static risk profile is expected to occur; and the characterisation of the static risk profile of individual ships in the presence of incomplete information, such as that obtained from the Automatic Identification System. A dataset collected from the Paris MoU platform is used for the development of the BN model and its validity is assessed. A quantitative assessment for the predictive validity of the model is also conducted by a sensitivity analysis that shows the consistency of the model with the Ship Risk Profile criteria and with the results of other studies developed also from historical PSC inspection data.

Decision Support Systems, 2020

This paper proposes a decision support tool for maintenance capacity planning of complex product ... more This paper proposes a decision support tool for maintenance capacity planning of complex product systems. The tool-ForeSim-BI-addresses the problem faced by maintenance organizations in forecasting the workload of future maintenance interventions and in planning an adequate capacity to face that expected workload. Developed and implemented from a predictive analytics perspective in the particular context of a Portuguese aircraft maintenance organization, the tool integrates four main modules: (1) a forecasting module used to predict future and unprecedented maintenance workloads from historical data; (2) a Bayesian inference module used to transform prior workload forecasts, resulting from the forecasting module, into predictive forecasts after observations on the maintenance interventions being predicted become available; (3) a simulation module used to characterize the forecasted total workloads through sets of random variables, including maintenance work types, maintenance work phases, and maintenance work skills; and (4) a Bayesian network module used to combine the simulated workloads with historical data through probabilistic inference. A linear programming model is also developed to improve the efficiency of the decision-making process supported by Bayesian networks. The tool uses real industrial data, comprising 171 aircraft maintenance projects collected at the host organization, and is validated by comparing its results with real observations of a given maintenance intervention to which predictions were made and with a model simulating current forecasting practices employed in industry. Significantly more accurate forecasts have been obtained with the proposed tool, resulting in an important cost saving potential for maintenance organizations. practice [1]. Al-Fares and Duffuaa [7] divide these approaches between deterministic and stochastic. Applied to maintenance contexts, examples of the former include the work of Dijkstra et al. [8], in which a decision support system based on optimization models is developed to determine the size and composition of aircraft maintenance teams, or the work of Yan et al. [9], in which a mathematical programming model is developed considering multiple types of maintenance certificates. An example of the latter include the work of De Bruecker et al. [10], that addresses an aircraft maintenance workforce problem with mixed integer linear programming and heuristic techniques, or the work of Kurz [11], that presents an aircraft engine overhaul capacity planning problem modelled as a queuing network. Not specifically directed at maintenance, De Bruecker et al. [12] presents an extensive literature review regarding workforce planning problems. As recognized by Al-Fares and Duffuaa [7], forecasting is an essential component of the capacity planning process, as it allows MROs

SIMULATION, 2018

The paper addresses repairable multi-unit systems with a series–parallel configuration for which ... more The paper addresses repairable multi-unit systems with a series–parallel configuration for which maintenance strategies are modeled by generalized stochastic Petri nets (GSPN) with predicates coupled with Monte Carlo simulation. Four maintenance strategies consisting of basic periodic preventive and corrective maintenance, and both combined with opportunistic maintenance (OM) strategies, are considered. Failure and repair distributions of the system components are independent, and repairs are considered to be perfect. Times to failure of degraded components follow a Weibull distribution with increasing failure rate over time. The maintenance strategies are optimized so as to minimize the total maintenance costs of the system while maximizing availability. A comparison is drawn between OM and non-OM. The aim is to show that GSPN with predicates, in combination with Monte Carlo simulation, is a powerful, flexible, efficient, and intuitive approach for modeling and optimizing practical...

TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 2019

This paper proposes an approach for identifying and characterizing shipping routes using informat... more This paper proposes an approach for identifying and characterizing shipping routes using information contained in Automatic Identification System messages broadcasted by ships and recorded by the coastal Vessel Traffic Service centre. The approach consists of using historical Automatic Identification System data to build a graph, where nodes are cells of a grid covering the geographical area being studied and the weights of directional edges are inversely related to ship movements between cells. Based on this graph, the Dijkstra algorithm is used to identify a potential safe route, assumed to be the most used route by ships between two locations. A second graph is created simultaneously, with the same nodes and edges, but with edge weights equal to the average speed of transitions between cells, thus allowing the determination of the average speed profile for any possible path within the graph. The proposed approach is applied to two scenarios: an approach to the port of Lisbon and the entry through the fairway to a RO-RO terminal in the port of Setubal in Portugal. http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 13 Number 3

International Journal of Production Economics, 2019

This paper proposes a framework for the qualitative and quantitative characterization of maintena... more This paper proposes a framework for the qualitative and quantitative characterization of maintenance work to support Maintenance, Repair, and Overhaul (MRO) organizations in performing capacity planning and scheduling. A quantitative assessment based on 372 maintenance projects collected at a Portuguese aircraft MRO confirms that a significant part of the maintenance work is stochastic in nature, given the amount of unscheduled maintenance. The proposed framework, entitled FRamework for Aircraft Maintenance Estimation (FRAME), is intended to allow MROs in managing this uncertainty throughout the maintenance planning process and comprises for that end a set of requirements for data treatment and a method for data analysis. The established requirements address important shortcomings found in the collected data that prevented the use of maintenance data for capacity planning and scheduling as is. The developed method for data analysis, entitled 3-Dimensional Maintenance Data Analysis (3D-MDA), is based on a space-time-skill coordinate system in which indicators are calculated from historical data to comprehensively characterize the expected maintenance work. Space refers to the aircraft work zone where maintenance is performed, time refers to the project work phase when maintenance is performed, and skill refers to the type of technicians required for maintenance to be performed. The established coordinates address the limitations of reviewed techniques by allowing accurate estimations of required resources for capacity planning and an extended range of constraints for maintenance scheduling. Being generic in nature, FRAME is applicable to maintenance in other industries, or even to other activities with due adaptations.

Computers & Industrial Engineering, 2018

Highlights The capacity planning problem faced by aircraft MRO companies is described. Bayesian n... more Highlights The capacity planning problem faced by aircraft MRO companies is described. Bayesian networks to address the capacity planning problem are developed. A validation process for Bayesian networks is proposed. Examples of the applicability of the developed Bayesian networks are presented.

Reliability Engineering & System Safety, 2018

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A time-variant fatigue reliability assessment model for welded joints is proposed.  The PHI2 method is adopted to solve the time-variant problem.  The crack sizes are explicitly represented by response surface models.  Residual stress effects on time-variant fatigue reliability are significant.

Reliability Engineering & System Safety, 2016

This paper presents an approach that more adequately incorporates human factor considerations int... more This paper presents an approach that more adequately incorporates human factor considerations into quantitative risk analysis of ship operation. The focus is on the collision accident category, which is one of the main risk contributors in ship operation. The approach is based on the development of a Bayesian Network (BN) model that integrates elements from the Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr) and focuses on the calculation of the collision accident probability due to human error. The model takes into account the human performance in normal, abnormal and critical operational conditions and implements specific tasks derived from the analysis of the task errors leading to the collision accident category. A sensitivity analysis is performed to identify the most important contributors to human performance and ship collision. Finally, the model developed is applied to assess the collision risk of a feeder operating in Dover strait using the collision probability estimated by the developed BN model and an Event tree model for calculation of human, economic and environmental risks.

Reliability Engineering & System Safety, 2017

In the present paper an adaptive Kriging surrogate model with active refinement is proposed to so... more In the present paper an adaptive Kriging surrogate model with active refinement is proposed to solve component reliability analysis problems (i.e. with a single design point) with a reasonable limit for the dimensionality of the basic random variables space. The model uses an adaptive Kriging-based trust region method to search for the design point and predict the failure probability based on the first-order reliability method. This prediction is then verified or improved using Monte Carlo simulation with importance sampling based on a Kriging surrogate model built up iteratively around the design point using an active refinement algorithm. The usefulness of the proposed surrogate model in terms of accuracy and efficiency for practical engineering applications is shown with a numerical example involving an advanced nonlinear FEA structural model.

Green Energy and Technology, 2016

This chapter starts by shortly addressing the statistics of accidents and component failures of w... more This chapter starts by shortly addressing the statistics of accidents and component failures of wind turbine structures based on a comprehensive dataset publicly available. The distribution of the types of offshore wind turbine structures installed in European waters is given. The operation and maintenance of fixed structures foundations is discussed. Then, the failure data of main subassemblies of wind turbines are presented and discussed, followed by a description of available and important condition monitoring systems, techniques and methods for operation and maintenance of wind turbines. Finally, the knowledge on modelling, simulation and optimization of operation and maintenance actions of fixed offshore wind turbines is discussed as a basis for the application in the operation and maintenance of floating offshore wind turbines.

Advanced Ship Design for Pollution Prevention, 2010

Structure and Infrastructure Engineering, 2014

The use of structural reliability methods with implicit limit state functions (LSFs) shows the in... more The use of structural reliability methods with implicit limit state functions (LSFs) shows the increasing demand for efficient stochastic analysis tools, because the structural behaviour predictions are often obtained by finite element analysis. All stochastic mechanics problems can be solved by Monte Carlo simulation method, nevertheless, in most cases, at a prohibitively high computational cost. Several approximations can be achieved using first-order reliability method (FORM) and second-order reliability method and response surface methods. In this paper, a method that combines the FORM and Kriging interpolation models, as response surface, is proposed. The prediction accuracy of the Kriging response surface obtained from different sampling techniques is assessed, and the failure probability estimates calculated by the FORM using the classical second-order polynomial regression models and the Kriging interpolation models as surrogates of nonlinear LSFs are compared. The usefulness and efficiency of the reliability analysis using the Kriging response surface are demonstrated on the basis of existing results available in the literature and with an application problem of a stiffened plate structure with initial imperfections.

Reliability Engineering and System Safety, 2007

ABSTRACT The use of an integral measure of initial deflections of thin plates, based on the strai... more ABSTRACT The use of an integral measure of initial deflections of thin plates, based on the strain energy, has proved to be of importance in studying the influence of the imperfections on the strength of plates subjected to in-plane compression. The energy measure allows for a straightforward definition of compound modes and for a computational determination of the lower bound strength. The last quantity may be essential for assessment of plate design strength considering the imperfections as a random field.

Operation and maintenance (O&M) activities have a significant impact on the energy cost f... more Operation and maintenance (O&M) activities have a significant impact on the energy cost for offshore wind turbines. Analytical methods such as reliability block diagrams and Markov processes along with simulation approaches have been widely used in planning and optimizing O&M actions in industrial systems. Generalized stochastic Petri Nets (GSPN) with predicates coupled with Monte Carlo simulation (MCS) are applied in this paper to model the planning of O&M activities of an offshore wind turbine. The merits of GSPN in modeling complex, multi-state and multi-component systems are addressed. Three maintenance categories classified according to the size and weight of the components to be replaced and the logistics involved, such as vessels, maintenance crew and spares and, the associated delays and costs are included in the model. The weather windows for accessing the wind turbine are also modeled. Corrective maintenance based on replacements and age imperfect preventive maintenance are modeled and compared in terms of the wind turbine’s performance (e.g. availability and loss production) and of the O&M costs.