Primavera Project

No more train delays, power outages, or failure of production machines? The PrimaVera project, funded by the Dutch National Research Agenda (NWA), represents a major step towards this goal. With predictive maintenance, or just-in-time maintenance (maintenance just before a system breaks down), the reliability of infrastructure and production resources can be increased and the costs of maintenance can be reduced.

Existing predictive maintenance techniques only work for small-scale systems and are difficult to scale up. Choices made in one place in the chain have an important influence on other processes in the chain. The choice of a certain type of sensors and measurements influences the type of predictions that can be made, and therefore also the quality of the predictions. That is why cross-level optimization methods are being developed within PrimaVera.

Latest publications

2025

Thom Badings: Robust Verification of Stochastic Systems. 2025, ISSN: 2950-2772. (Type: PhD Thesis | Abstract | Links | BibTeX)

@phdthesis{Badings2025,

title = {Robust Verification of Stochastic Systems},

author = {Thom Badings},

url = {https://primavera-project.com/wp-content/uploads/2025/02/Badings_Robust_Stochastic_Systems_PhD_Thesis.pdf, Download the manuscript PDF},

doi = {10.54195/9789493296909},

issn = {2950-2772},

year = {2025},

date = {2025-03-27},

urldate = {2025-03-27},

abstract = {Verifying that systems are safe and reliable is crucial in today’s world. For example, we want to prove that an autonomous drone will safely reach its target, or that a manufacturing system will not break down. Classical algorithms for verifying such properties often rely on a precise mathematical model of the system, for example, in the form of a Markov chain or a Markov decision process (MDP). Such Markov models are probabilistic transition systems and are ubiquitous in many areas, including control theory, artificial intelligence (AI), formal methods, and operations research. However, as systems become increasingly complex with more cyber-physical and AI components, uncertainty about the system’s behavior is inevitable. As a result, transition probabilities in Markov models are subject to uncertainty, rendering many existing analysis algorithms inapplicable. In this thesis, we fill this gap by developing novel verification methods for Markov models in the presence of uncertainty. To capture this uncertainty, we use parametric Markov models, where probabilities are described as functions over parameters. We study two perspectives on rendering verification methods robust against uncertainty: (1) set-bounded uncertainty, where only the set of possible parameter values is known and one can be robust in a worst-case sense, and (2) stochastic uncertainty, where the parameter values are described by a probability distribution and one can be robust in a probabilistic sense. By combining techniques from formal methods, AI, and control theory, our contributions span the following general problem settings: 1. We develop robust abstraction techniques for solving control problems for Markov models with continuous state and action spaces, and with set-bounded uncertain parameters. Based on the notion of probabilistic simulation relations, we show that such continuous control problems can be solved by only analyzing a finite-state abstraction, formalized as an MDP with sets of transition probabilities. 2. We present novel and scalable verification techniques for parametric Markov models with a prior distribution over the parameters. Our approaches are sampling-based, do not require any assumptions on the parameter distribution, and provide probably approximately correct (PAC) guarantees on the verification results. 3. We show that parametric models can be used to improve the sample efficiency of data-driven learning methods. We leverage tools from convex optimization to perform a sensitivity analysis, where we measure sensitivity in terms of partial derivatives of the polynomial function that describes a measure of interest. 4. We study continuous-time Markov chains where the initial state must be inferred from a set of (possibly uncertain) state observations. This setting is particularly relevant in runtime monitoring. We compute upper and lower bounds on reachability probabilities, conditioned on these observations. Our approach is based on a robust abstraction into an MDP with intervals of transition probabilities. In conclusion, we develop verification methods that reason over uncertainty without sacrificing guarantees. While dealing with uncertainty can be computationally expensive, providing such guarantees is crucial for designing safe and reliable systems with cyber-physical and AI components. The aim of this thesis is to contribute to a better understanding of dealing with uncertainty in stochastic verification problems.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Verifying that systems are safe and reliable is crucial in today’s world. For example, we want to prove that an autonomous drone will safely reach its target, or that a manufacturing system will not break down. Classical algorithms for verifying such properties often rely on a precise mathematical model of the system, for example, in the form of a Markov chain or a Markov decision process (MDP). Such Markov models are probabilistic transition systems and are ubiquitous in many areas, including control theory, artificial intelligence (AI), formal methods, and operations research. However, as systems become increasingly complex with more cyber-physical and AI components, uncertainty about the system’s behavior is inevitable. As a result, transition probabilities in Markov models are subject to uncertainty, rendering many existing analysis algorithms inapplicable. In this thesis, we fill this gap by developing novel verification methods for Markov models in the presence of uncertainty. To capture this uncertainty, we use parametric Markov models, where probabilities are described as functions over parameters. We study two perspectives on rendering verification methods robust against uncertainty: (1) set-bounded uncertainty, where only the set of possible parameter values is known and one can be robust in a worst-case sense, and (2) stochastic uncertainty, where the parameter values are described by a probability distribution and one can be robust in a probabilistic sense. By combining techniques from formal methods, AI, and control theory, our contributions span the following general problem settings: 1. We develop robust abstraction techniques for solving control problems for Markov models with continuous state and action spaces, and with set-bounded uncertain parameters. Based on the notion of probabilistic simulation relations, we show that such continuous control problems can be solved by only analyzing a finite-state abstraction, formalized as an MDP with sets of transition probabilities. 2. We present novel and scalable verification techniques for parametric Markov models with a prior distribution over the parameters. Our approaches are sampling-based, do not require any assumptions on the parameter distribution, and provide probably approximately correct (PAC) guarantees on the verification results. 3. We show that parametric models can be used to improve the sample efficiency of data-driven learning methods. We leverage tools from convex optimization to perform a sensitivity analysis, where we measure sensitivity in terms of partial derivatives of the polynomial function that describes a measure of interest. 4. We study continuous-time Markov chains where the initial state must be inferred from a set of (possibly uncertain) state observations. This setting is particularly relevant in runtime monitoring. We compute upper and lower bounds on reachability probabilities, conditioned on these observations. Our approach is based on a robust abstraction into an MDP with intervals of transition probabilities. In conclusion, we develop verification methods that reason over uncertainty without sacrificing guarantees. While dealing with uncertainty can be computationally expensive, providing such guarantees is crucial for designing safe and reliable systems with cyber-physical and AI components. The aim of this thesis is to contribute to a better understanding of dealing with uncertainty in stochastic verification problems.

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)

@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 = {},

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.

Bram Ton; Niek Tempert; Danny Plass: Immersive visualisation of point cloud data of railway environments. In: 2024 10th International Conference on Virtual Reality (ICVR), IEEE, 2025, ISBN: 979-8-3503-6423-1. (Type: Proceedings Article | Links | BibTeX)

2024

Sabari Nathan Anbalagan: SPECTRUM: Towards Self-aware Industrial IoT Systems. 2024, ISBN: 978-90-365-6335-2. (Type: PhD Thesis | Abstract | Links | BibTeX)

@phdthesis{Anbalagan2024,

title = {SPECTRUM: Towards Self-aware Industrial IoT Systems},

author = {Sabari Nathan Anbalagan},

doi = {https://doi.org/10.3990/1.9789036563352},

isbn = {978-90-365-6335-2},

year  = {2024},

date = {2024-12-03},

urldate = {2024-12-03},

abstract = {The Fourth Industrial Revolution (4IR) has sparked remarkable technological advancements, particularly in integrating the Internet of Things (IoT) into manufacturing industries, known as the Industrial Internet of Things (IIoT). This integration holds potential for revolutionizing industrial processes and boosting human progress through advanced applications within factory settings.



However, the diverse Quality of Service (QoS) requirements of 4IR applications, including data rate, latency, reliability, power efficiency, scalability, and mobility, present significant challenges. The IIoT network must support these diverse requirements while operating in harsh and dynamic industrial indoor environments characterized by metallic infrastructure, thick concrete pillars, and complex pipe networks. Additionally, environmental fluctuations like temperature and humidity, process dynamics such as spinning and smelting, and the mobility of operators and indoor vehicles contribute to the dynamic nature of these environments. Achieving the diverse QoS requirements in such challenging conditions necessitates intelligent and versatile solutions.



The latest telecommunications standard, 5G, holds promise as a solution to these challenges. This thesis addresses these challenges by focusing on the optimization of industrial use cases for 5G-based IIoT systems. It begins with an exploration of the significance and applications of 4IR in manufacturing industries, identifying the potential of 5G technology augmented by varying levels of Artificial Intelligence (AI) to meet the diverse demands of IIoT networks. The problem statement emphasizes the necessity for intelligent methodologies that adapt to the dynamic nature of industrial environments while supporting a wide range of industrial applications.

The thesis proposes a multifaceted approach to tackle these challenges, incorporating varying levels of context awareness within IIoT systems. Chapters delve into the evolution of IIoT systems, presenting a framework for assessing system maturity and providing practical guidance for implementation. The development of heuristics-based, coarse-grained, and fine-grained Channel Quality Prediction (CQP) techniques is explored, offering insights into optimizing wireless communication parameters for diverse 5G use cases. Simulations and analyses demonstrate the efficacy of these methodologies in enhancing throughput, resource utilization, and power efficiency across various industrial scenarios.



In conclusion, this thesis contributes to the advancement of wireless communication optimization in industrial settings, offering practical solutions and insights for realizing the potential of 4IR initiatives. By addressing a spectrum of challenges, requirements and use case scenarios, the research offers insights for future 5G and 6G releases.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

The Fourth Industrial Revolution (4IR) has sparked remarkable technological advancements, particularly in integrating the Internet of Things (IoT) into manufacturing industries, known as the Industrial Internet of Things (IIoT). This integration holds potential for revolutionizing industrial processes and boosting human progress through advanced applications within factory settings.

However, the diverse Quality of Service (QoS) requirements of 4IR applications, including data rate, latency, reliability, power efficiency, scalability, and mobility, present significant challenges. The IIoT network must support these diverse requirements while operating in harsh and dynamic industrial indoor environments characterized by metallic infrastructure, thick concrete pillars, and complex pipe networks. Additionally, environmental fluctuations like temperature and humidity, process dynamics such as spinning and smelting, and the mobility of operators and indoor vehicles contribute to the dynamic nature of these environments. Achieving the diverse QoS requirements in such challenging conditions necessitates intelligent and versatile solutions.

The latest telecommunications standard, 5G, holds promise as a solution to these challenges. This thesis addresses these challenges by focusing on the optimization of industrial use cases for 5G-based IIoT systems. It begins with an exploration of the significance and applications of 4IR in manufacturing industries, identifying the potential of 5G technology augmented by varying levels of Artificial Intelligence (AI) to meet the diverse demands of IIoT networks. The problem statement emphasizes the necessity for intelligent methodologies that adapt to the dynamic nature of industrial environments while supporting a wide range of industrial applications.
The thesis proposes a multifaceted approach to tackle these challenges, incorporating varying levels of context awareness within IIoT systems. Chapters delve into the evolution of IIoT systems, presenting a framework for assessing system maturity and providing practical guidance for implementation. The development of heuristics-based, coarse-grained, and fine-grained Channel Quality Prediction (CQP) techniques is explored, offering insights into optimizing wireless communication parameters for diverse 5G use cases. Simulations and analyses demonstrate the efficacy of these methodologies in enhancing throughput, resource utilization, and power efficiency across various industrial scenarios.

In conclusion, this thesis contributes to the advancement of wireless communication optimization in industrial settings, offering practical solutions and insights for realizing the potential of 4IR initiatives. By addressing a spectrum of challenges, requirements and use case scenarios, the research offers insights for future 5G and 6G releases.

Ragnar Hans Eggertsson: Advances in Asset Management: Maintenance Optimization under Incomplete Information and Sustainable Technology Selection. 2024, ISBN: 978-90-386-6159-9. (Type: PhD Thesis | Links | BibTeX)

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