Emanuele Petriconi

Emanuele's research focuses on advancing digital twin technologies for real-world applications, with a particular emphasis on diagnostic algorithms for model updating and decision-making algorithms for fleet maintenance optimisation, improving efficiency and reliability in industrial and operational contexts.

• Teaching assistant

  Digital Twin for Health and Usage Monitoring

  MSc Mechanical Engineering · 5 CFU · 1st semester · Milano Bovisa

  Lecturer: Claudio Sbarufatti

  Course sheet

Courses of 2026/2027, from the Politecnico’s course sheets. All the group’s courses

• SHMFleet MonitoringMachine LearningWind Turbines

  Population-Based Monitoring for Wind Turbine Structural Health

  This thesis investigates Population-Based Structural Health Monitoring (PBSHM) applied to wind turbines using publicly available datasets from the literature. The activity focuses on fleets of turbines operating under varying environmental and operational conditions, exploiting signals such as vibration, SCADA, or load measurements.

  Details and apply
• Digital TwinsPHMAIReinforcement LearningUAVSWARM

  Reinforcement Learning for Fleet O&M Optimization in UAV Systems

  This thesis investigates the application of Reinforcement Learning (RL) to Operation and Maintenance (O&M) optimization in UAV fleets within a parametric simulation environment. The activity focuses on developing adaptive decision-making policies that optimise fleet-level performance under varying mission requirements and operational constraints.A flexible simulation framework is used to model different fleet configurations, enabling the training and evaluation of RL agents across multiple scenarios. An interactive interface allows direct comparison between RL-based decisions and user-driven strategies. The objective is to assess the capability of RL to improve key performance indicators such as fleet availability, mission efficiency, and robustness to changing conditions. Particular emphasis is placed on scalability, interpretability, and generalisation across configurations.

  Details and apply
• Digital TwinsAIReinforcement LearningBatteriesUAVSWARM

  Reinforcement Learning for Battery-Aware Fleet Management in UAV Systems

  This thesis investigates the application of Reinforcement Learning (RL) to the operational management of battery usage in UAV fleets. The activity focuses on learning decision policies that optimise fleet availability and mission continuity by managing the state-of-charge of individual UAVs.

  Details and apply
• Digital TwinsPHMEnginesTURBOMON

  Development of an engine digital-twin

  Optimising maintenance policies is a complex task pushing towards a shift from programmed maintenance to condition-based maintenance. An engine digital twin is thus suitable to monitor and keep track of the health condition throughout the lifespan of the engine, enabling the prediction of engine health state by implementing diagnostic and prognostic algorithms enabling conditions-based maintenance policies.

  Details and apply
• Digital TwinsPHMAIReinforcement LearningEnginesTURBOMON

  Engine fleet logistic digital twin for life cycle management optimisation

  Optimising maintenance policies is a complex task pushing towards a shift from programmed maintenance to condition-based maintenance. This task is fundamental when dealing with aircraft fleets, developing a Discrete Event Simulator (DES) to simulate numerous fleet lifecycles and implement Reinforcement Learning algorithms for automatic decision making.

  Details and apply
• Digital TwinsPHMAIReinforcement LearningWind Turbines

  Reinforcement Learning for O&M Optimization in Wind Turbine Fleets

  This thesis investigates the application of Reinforcement Learning (RL) to Operation and Maintenance (O&M) optimization in wind turbine fleets using publicly available datasets from the literature. The activity focuses on learning decision policies that optimise turbine availability and energy production under varying environmental and operational conditions.

  Details and apply
• SHMDigital TwinsiFEMMachine Learning

  Displacement Field Reconstruction Using the iFEM Methodology on a Rotating Shaft for Imbalance Detection

  Building on strain data, this research aims to develop advanced iFEM methods for damage and imbalance identification by integrating physics-based models with machine learning techniques. The study will also incorporate pre-extrapolation methods to enhance prediction accuracy. Furthermore, adaptive strategies will be designed to accommodate varying shaft boundary conditions, ensuring robust and reliable full-field displacement reconstruction under realistic operational scenarios.

  Details and apply
• SHMPHMMachine LearningHelicoptersSAMAS 2

  Data-based monitoring for impact/corrosion damage identification in helicopters

  In the framework of SAMAS 2, flight tests with real helicopters are performed to evaluate the health status through a specifically designed Structural Health Monitoring and Prognosis system. The data from the flight tests are used to develop diagnostic algorithms capable of identifying damage and the degradation level of the structure.

  Details and apply
• Quantum ComputingNumerical Modelling

  Development of quantum algorithms for Engineering (FEM)

  Quantum computing is a novel, promising technology that may have a great impact also in the Engineering field. Nowadays, it is in its first development stages, while the potential and applicability of such technology still have to be determined. This thesis project aims to study the latest innovations in terms of quantum computing and reproduce common traditional algorithms with quantum ones. The goal is to reproduce an FEM algorithm in a simple scenario and evaluate possible extensions to more complex ones, considering the actual technological limitations.

  Details and apply
• AIMachine LearningQuantum Computing

  Development of quantum machine learning (QML) for diagnostics

  Quantum computing is a novel, promising technology that may have a great impact also in the Engineering field. Nowadays, it is in its first development stages, while the potential and applicability of such technology still have to be determined. This thesis project aims to study the latest innovations in terms of quantum computing and reproduce common traditional algorithms with quantum ones. The goal is to develop a Quantum Machine Learning (QML) algorithm to perform damage detection on dataset already available in our lab.

  Details and apply

All thesis topics