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Personale

Leonardo Baldo

Dottorando in Ingegneria Meccanica , 38o ciclo (2022-2025)
Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS)

Profilo

Dottorato di ricerca

Titolo della tesi

An Industrial PHM Framework Aimed at Condition-Based Maintenance and Fleet Management of Advanced Jet Trainers

Argomento di ricerca

An Industrial PHM Framework Aimed at Condition-Based Maintenance and Fleet Management of Advanced Jet Trainers

Tutori

Keywords

Reliability, dia-/pro-gnostics / Affidabilità, dia-/pro-gnostica
Mechatronics and robotics / Meccatronica e robotica
Automation and control / Automazione e controllo
Mechanism analysis / Analisi dei meccanismi

Biografia

Leonardo Baldo is an Aerospace Engineer and PhD graduate in Mechanical Engineering from Politecnico di Torino, where his research focused on Prognostics and Health Management (PHM) for aerospace systems. His work lies at the intersection of mechanical and aerospace engineering, data-driven modeling, and intelligent maintenance, with a particular emphasis on developing practical solutions for safety-critical industrial applications. His research experience in the advanced monitoring of complex industrial systems covers a broad range of components and technologies, including electromechanical and electrohydraulic actuators, FBG-based sensing systems, inverters, gearboxes, and batteries.
His industrial PhD project addressed a strategic challenge for the aviation sector: transforming large volumes of operational aircraft data into actionable PHM capabilities for safety-critical flight control actuators, starting from legacy platforms. Developed in partnership between Politecnico di Torino and Leonardo S.p.A., and co-funded under the Italian Piano Nazionale di Ripresa e Resilienza (PNRR) within the EU’s NextGenerationEU framework, the project aligned with national and European priorities in digital transformation, innovation, and safety-critical infrastructure.
During his doctoral studies, he contributed to the advancement of PHM methodologies for flight control actuation systems, combining reliability engineering, machine learning, probabilistic methods, and condition-based maintenance strategies to address real-world challenges in complex engineering systems. Alongside his academic research, Leonardo gained industrial experience through collaborations with leading aerospace companies, where he worked on predictive maintenance and data-driven decision support for aircraft systems.

Competenze

Settori ERC

PE8_1 - Aerospace engineering
PE6_7 - Artificial intelligence, intelligent systems, natural language processing
PE8_7 - Mechanical engineering

SDG

Goal 9: Industry, Innovation, and Infrastructure
Goal 11: Sustainable cities and communities
Goal 13: Climate action

Premi e riconoscimenti

  • Vincitore del concorso "Giovane Talento dell'Innovazione Aeronautica", promosso dall’Associazione Pionieri dell’Aeronautica in occasione della ricorrenza del centenario dalla sua fondazione. (2023)
  • Best Paper Award - 17th Annual Conference of the Prognostics and Health Management Society - October 27 – 30, 2025, Bellevue (USA) (2025)

Didattica

Insegnamenti

Corso di laurea magistrale

MostraNascondi A.A. passati

Ricerca

Gruppi di ricerca

Pubblicazioni

Coautori PoliTO

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