Publications – Primavera Project

PrimaVera Demonstrators

Infrastructure Demonstrator – PrimaVera is developing a web app that showcases research applications in diagnostics, prognostics, and maintenance policy optimization and logistics. The work-in-progress web application can be visited via the link above.

List of scientific publications

2025

Lisandro Arturo Jimenez-Roa: Reliability and Maintenance for Engineering Systems: Fault Trees, Degradation Modelling and Maintenance Optimisation. 2025, ISBN: 978-90-365-6407-6. (Type: PhD Thesis | Abstract | Links | BibTeX | Tags: Deep Reinforcement Learning, Degradation Modelling, Fault tree analysis, Maintenance optimization, Prognostics and Health Management)

@phdthesis{Jimenez-Roa2025,

title = {Reliability and Maintenance for Engineering Systems: Fault Trees, Degradation Modelling and Maintenance Optimisation},

author = {Lisandro Arturo Jimenez-Roa},

url = {https://primavera-project.com/wp-content/uploads/2025/01/phd_thesis_lisandro.pdf, Download the manuscript PDF

https://books.ipskampprinting.nl/thesis/668430-Roa/, Consult the interactive book},

isbn = {978-90-365-6407-6},

year = {2025},

date = {2025-02-07},

urldate = {2025-02-07},

abstract = {Modern infrastructures, machines, and manufacturing processes require effective management through sustainable policies under constrained resources, where deter- mining when and how to intervene becomes crucial. The Prognostics and Health Management (PHM) paradigm provides a systematic framework for leveraging data collection and computational models, supporting the management of virtually any engineering component or system. This dissertation delves into three key aspects of PHM: Reliability Modelling, Markov Process-based Prognostics, and Maintenance Optimisation. Data-driven techniques are crucial in these areas, enhancing the automation of model development and deployment. Part I centres on Reliability Modelling, specifically the automatic inference of Fault Tree (FT) models. Traditionally, graph-based models like FTs are manually constructed through iterative collaboration between system experts and FT modellers. However, this manual approach is prone to human error and may result in incomplete models. With the increasing availability of data, methodologies that attempt to automate this process, discover patterns and reduce dependency on manual intervention have gained significant relevance. Thus, in Part I of this dissertation, we focus on how to obtain efficient and compact Fault Tree models from failure datasets in a robust and scalable manner. For this matter, we propose, for the first time, using Multi-Objective Evolutionary Algorithms (MOEAs) to automatically infer FT models and cast the optimisation as a multi-objective task. This resulted in the FT-MOEA algorithm (Chapter 2), focusing on three optimisation metrics, including FT size and accuracy-related metrics. FT-MOEA consistently produced compact FT structures, but faced scalability issues. To address this, we developed the SymLearn toolchain (Chapter 3), which uses a ‘divide-and-conquer’ approach by identifying modules and symmetries in the failure dataset, breaking the inference problem into smaller tasks. Additionally, to improve robustness and scalability, the FT-MOEA-CM extension (Chapter 4) includes additional metrics from the confusion matrix. Our approaches in Part I of this dissertation contribute to automating FT model construction, revealing compact structures. These consistent structures can help uncover relationships between basic and intermediate events, providing valuable insights for asset managers to improve reliability modelling. Part II focuses on Markov Process-based Prognostics, specifically the stochastic deterioration modelling of sewer mains. Sewer systems are critical to social welfare but pose significant challenges due to their extensive scale, slow degradation, and limited capacity to monitor the entire network. Accurate modelling of the deterioration profile is crucial for optimising inspections and maintenance, thereby enhancing the reliability and availability of the networks. Various deterioration models are discussed in the literature, ranging from physics-based to data-driven approaches, each with distinct advantages and limitations. In Part II of this dissertation, we address how and to what extent it is possible to accurately model Multi-State Deterioration with applications in sewer mains. For this, we focus on Markov chains, widely used to model stochastic sequences through states and transitions. Since the 1990s, they have been applied to represent damage severity levels in sewer mains using inspection data from Closed Circuit Television cameras. Nonetheless, further evaluation of their assumptions and properties is required. We present a case study of a Dutch sewer network (Chapter 5), starting with Discrete-Time Markov Chains for deterioration modelling and examining two Markov chain structures. Given challenges such as interval-censored data, advanced analysis was necessary. In Chapter 6, we use the Turnbull estimator for non-parametric analysis to establish a ground truth. Although both homogeneous and inhomogeneous-time Markov chains are employed for sewer mains deterioration, no prior studies have compared their performance on the same dataset. Chapter 6 addresses this by demonstrating that inhomogeneous-time Markov chains are more versatile at capturing non-linear stochastic behaviour, while also highlighting issues like overfitting that reduce predictive accuracy. Part II provides a real-world case study, emphasising the need to critically evaluate modelling assumptions to enhance deterioration modelling of sewer mains using Markov chains. Finally, Part III focuses on Maintenance Optimisation of sewer mains, where obtaining optimal maintenance policies for such large-scale systems is a complex task. This complexity arises, among others, from the system’s scale and simplifications in the deterioration model. Among the various techniques available, Reinforcement Learning (RL) approaches remain largely unexplored for devising strategic main- tenance actions in sewer mains. Thus, in Part III of this dissertation, we focus on how to devise optimal maintenance strategies for components with Multi-State Deterioration such as sewer mains using Deep Reinforcement Learning.In Chapter 7, we frame the sequential decision-making problem using Deep Reinforcement Learning (DRL) for component-level maintenance of sewer mains. This framework considers damage severity levels, testing different deterioration model assumptions and evaluating their impact on maintenance policy. Our results show that agent-based policies outperformed heuristics by learning optimal sequences of maintenance actions. Part III provides evidence that DRL-based techniques o!er a flexible framework with the potential to improve heuristics and support maintenance decision-making for sewer mains. However, training these models to achieve the desired behaviour remains a challenging task.

keywords = {Deep Reinforcement Learning, Degradation Modelling, Fault tree analysis, Maintenance optimization, Prognostics and Health Management},

pubstate = {published},

tppubtype = {phdthesis}

}

Modern infrastructures, machines, and manufacturing processes require effective management through sustainable policies under constrained resources, where deter- mining when and how to intervene becomes crucial. The Prognostics and Health Management (PHM) paradigm provides a systematic framework for leveraging data collection and computational models, supporting the management of virtually any engineering component or system. This dissertation delves into three key aspects of PHM: Reliability Modelling, Markov Process-based Prognostics, and Maintenance Optimisation. Data-driven techniques are crucial in these areas, enhancing the automation of model development and deployment. Part I centres on Reliability Modelling, specifically the automatic inference of Fault Tree (FT) models. Traditionally, graph-based models like FTs are manually constructed through iterative collaboration between system experts and FT modellers. However, this manual approach is prone to human error and may result in incomplete models. With the increasing availability of data, methodologies that attempt to automate this process, discover patterns and reduce dependency on manual intervention have gained significant relevance. Thus, in Part I of this dissertation, we focus on how to obtain efficient and compact Fault Tree models from failure datasets in a robust and scalable manner. For this matter, we propose, for the first time, using Multi-Objective Evolutionary Algorithms (MOEAs) to automatically infer FT models and cast the optimisation as a multi-objective task. This resulted in the FT-MOEA algorithm (Chapter 2), focusing on three optimisation metrics, including FT size and accuracy-related metrics. FT-MOEA consistently produced compact FT structures, but faced scalability issues. To address this, we developed the SymLearn toolchain (Chapter 3), which uses a ‘divide-and-conquer’ approach by identifying modules and symmetries in the failure dataset, breaking the inference problem into smaller tasks. Additionally, to improve robustness and scalability, the FT-MOEA-CM extension (Chapter 4) includes additional metrics from the confusion matrix. Our approaches in Part I of this dissertation contribute to automating FT model construction, revealing compact structures. These consistent structures can help uncover relationships between basic and intermediate events, providing valuable insights for asset managers to improve reliability modelling. Part II focuses on Markov Process-based Prognostics, specifically the stochastic deterioration modelling of sewer mains. Sewer systems are critical to social welfare but pose significant challenges due to their extensive scale, slow degradation, and limited capacity to monitor the entire network. Accurate modelling of the deterioration profile is crucial for optimising inspections and maintenance, thereby enhancing the reliability and availability of the networks. Various deterioration models are discussed in the literature, ranging from physics-based to data-driven approaches, each with distinct advantages and limitations. In Part II of this dissertation, we address how and to what extent it is possible to accurately model Multi-State Deterioration with applications in sewer mains. For this, we focus on Markov chains, widely used to model stochastic sequences through states and transitions. Since the 1990s, they have been applied to represent damage severity levels in sewer mains using inspection data from Closed Circuit Television cameras. Nonetheless, further evaluation of their assumptions and properties is required. We present a case study of a Dutch sewer network (Chapter 5), starting with Discrete-Time Markov Chains for deterioration modelling and examining two Markov chain structures. Given challenges such as interval-censored data, advanced analysis was necessary. In Chapter 6, we use the Turnbull estimator for non-parametric analysis to establish a ground truth. Although both homogeneous and inhomogeneous-time Markov chains are employed for sewer mains deterioration, no prior studies have compared their performance on the same dataset. Chapter 6 addresses this by demonstrating that inhomogeneous-time Markov chains are more versatile at capturing non-linear stochastic behaviour, while also highlighting issues like overfitting that reduce predictive accuracy. Part II provides a real-world case study, emphasising the need to critically evaluate modelling assumptions to enhance deterioration modelling of sewer mains using Markov chains. Finally, Part III focuses on Maintenance Optimisation of sewer mains, where obtaining optimal maintenance policies for such large-scale systems is a complex task. This complexity arises, among others, from the system’s scale and simplifications in the deterioration model. Among the various techniques available, Reinforcement Learning (RL) approaches remain largely unexplored for devising strategic main- tenance actions in sewer mains. Thus, in Part III of this dissertation, we focus on how to devise optimal maintenance strategies for components with Multi-State Deterioration such as sewer mains using Deep Reinforcement Learning.In Chapter 7, we frame the sequential decision-making problem using Deep Reinforcement Learning (DRL) for component-level maintenance of sewer mains. This framework considers damage severity levels, testing different deterioration model assumptions and evaluating their impact on maintenance policy. Our results show that agent-based policies outperformed heuristics by learning optimal sequences of maintenance actions. Part III provides evidence that DRL-based techniques o!er a flexible framework with the potential to improve heuristics and support maintenance decision-making for sewer mains. However, training these models to achieve the desired behaviour remains a challenging task.