Ph.D. in Ingegneria Meccanica , 35th cycle (2019-2022)
Ph.D. obtained in 2023
Dissertation:
Artificial Intelligence Tools Applied to the Machine Fault Diagnosis of the Rotor-Bearing System (Abstract)
Tutors:
Cristiana Delprete Eugenio BrusaProfile
Research topic
Digitalized Virtual Engineering and Testing of Lean Rotating Systems
Research interests
Biography
He holds a PhD in Mechanical Engineering from Politecnico di Torino (2023), specializing in the application of Artificial Intelligence techniques for the diagnosis and predictive maintenance of rotating systems, with a focus on rolling element bearings. His experimental work contributed to the development of dedicated diagnostic datasets, covering vibration acquisition, accelerometer deployment, and condition monitoring protocol design. His expertise bridges mechanical engineering and AI methods. On the mechanical side, he has experience in structural and dynamic modelling using finite element analysis (Ansys), CFD simulation, and fluid-structure interaction, particularly for hydrodynamic lubrication systems and journal bearings. From 2023 to 2024 he was a Research Assistant at the DIMEAS department of Politecnico di Torino, working on synthetic data generation via generative networks and on LLM-based applications for industrial data analysis, gaining experience in time series and signal analysis for condition monitoring. Since 2024 he has been a fixed-term Researcher at DIMEAS, continuing his work on predictive maintenance, generative AI, and advanced condition monitoring. He has developed several open source projects in the field of agentic AI for industrial applications, including AI agents for machinery diagnosis and maintenance, and Model Context Protocol (MCP)-based integrations for connecting analytical tools with LLMs. In 2025 and 2026 he was a Visiting Researcher at the Universitat Politècnica de Catalunya and the Universitat Politècnica de València, working on advanced condition monitoring and fault diagnosis of electrical machines through vibration, current, and magnetic flux analysis, with anomaly detection approaches. He has strong data science and machine learning skills applied to industrial contexts: development of machine learning and deep learning models for machinery diagnostics and prognostics, noisy dataset management, signal processing in MATLAB, and Python-based development, supported by advanced AI tools for coding and algorithm prototyping. In 2026 he was awarded a Master in Business Administration (MBA), complementing his technical background with expertise in leadership, entrepreneurship, and business strategy, with a focus on AI applications in industrial settings and data-driven business models.
Scientific branch
(Area 0009 - Industrial and information engineering)
Awards and Honors
Teaching
Collegi of the degree programmes
- Collegio di Ingegneria Meccanica, Aerospaziale e dell'Autoveicolo. Componente invitato
Teachings
Master of Science
- Machine design. A.A. 2019/20, INGEGNERIA MECCANICA (MECHANICAL ENGINEERING). Collaboratore del corso
- Machine design. A.A. 2020/21, INGEGNERIA MECCANICA (MECHANICAL ENGINEERING). Collaboratore del corso
- Machine design. A.A. 2021/22, INGEGNERIA MECCANICA (MECHANICAL ENGINEERING). Collaboratore del corso
- Costruzione di macchine. A.A. 2022/23, INGEGNERIA MECCANICA. Collaboratore del corso
Bachelor of Science
- Elementi di costruzione di macchine. A.A. 2022/23, INGEGNERIA MECCANICA. Collaboratore del corso
Research
Research groups
Supervised PhD students
- Luca Giraudo. Programme in Ingegneria Meccanica (cycle 41, 2025-in progress)
Research subject: Industrial rotors: mathematical models and simulation, damage diagnosis/prognosis algorithms, empirical testing of the bearing-rotor system.
Multi-body analysis / Analisi multi-body Vibrations and rotordynamics / Vibrazioni e dinamica dei rotori Machine design / Progetto di macchine
Publications
Publications by type
PoliTO co-authors
Works published during the Ph.D. View all publications in Porto@Iris
- Brusa, Eugenio; Cibrario, Luca; Delprete, Cristiana; Di Maggio, Luigi Gianpio (2023)
Explainable AI for Machine Fault Diagnosis: Understanding Features' Contribution in Machine Learning Models for Industrial Condition Monitoring. In: APPLIED SCIENCES, vol. 13. ISSN 2076-3417
Contributo su Rivista - Di Maggio, Luigi Gianpio (2023)
Artificial Intelligence Tools Applied to the Machine Fault Diagnosis of the Rotor-Bearing System. relatore: DELPRETE, CRISTIANA; BRUSA, EUGENIO; , 35. XXXV Ciclo, P.: 213
Doctoral Thesis - Brusa, Eugenio; Delprete, Cristiana; Di Maggio, Luigi Gianpio (2023)
Eigen-spectrograms: An interpretable feature space for bearing fault diagnosis based on artificial intelligence and image processing. In: MECHANICS OF ADVANCED MATERIALS AND STRUCTURES, pp. 1-13. ISSN 1537-6494
Contributo su Rivista - Di Maggio, Luigi Gianpio (2023)
Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification. In: SENSORS, vol. 23. ISSN 1424-8220
Contributo su Rivista - DI MAGGIO, LUIGI GIANPIO; Brusa, Eugenio; Delprete, Cristiana (2023)
Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks. In: APPLIED SCIENCES, vol. 13. ISSN 2076-3417
Contributo su Rivista - Giorio, L.; Di Maggio, L.; Gastaldi, C.; Brusa, E.; Delprete, C. (2023)
Modellazione numerica per la progettazione in regime dinamico accoppiato fluido-struttura di cuscinetti a film d'olio per laminatoi. In: AIAS2023 - 52° Convegno, Genova, 6-8 Settembre 2023, pp. 1-10
Contributo in Atti di Convegno (Proceeding) - Di Maggio, Luigi Gianpio; Brusa, Eugenio; Delprete, Cristiana (2023)
Intelligenza Artificiale Generativa per la produzione di dati sintetici nella diagnosi di macchine rotanti. In: AIAS2023 - 52° Convegno, Genova, 6-9/9/2023
Contributo in Atti di Convegno (Proceeding) - DI MAGGIO, LUIGI GIANPIO (2022)
Applicazione di algoritmi di Intelligenza Artificiale per il monitoraggio di cuscinetti volventi. In: Convegno AIAS2022, Padova, 7-10/9/2022
Contributo in Atti di Convegno (Proceeding) - Brusa, E.; Delprete, C.; Giorio, L.; Di Maggio, L. G.; Zanella, V. (2022)
Design of an Innovative Test Rig for Industrial Bearing Monitoring with Self-Balancing Layout. In: MACHINES, vol. 10. ISSN 2075-1702
Contributo su Rivista - Brusa, E; Bruzzone, F; Delprete, C; Di Maggio, L; Rosso, C (2021)
Envelope analysis applied to non-Hertzian contact simulations in damaged roller bearings. In: The 49th AIAS Conference, Genoa, 2-5 september. ISSN 1757-8981
Contributo in Atti di Convegno (Proceeding) - Brusa, E.; Delprete, C.; Di Maggio, L. G. (2021)
Deep transfer learning for machine diagnosis: From sound and music recognition to bearing fault detection. In: APPLIED SCIENCES, vol. 11. ISSN 2076-3417
Contributo su Rivista - Delprete, C.; Maggio, L. G.; Sesana, R. (2021)
Theory of critical distances: A discussion on concepts and applications. In: PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS. PART C, JOURNAL OF MECHANICAL ENGINEERING SCIENCE, vol. 235, pp. 5695-5708. ISSN 0954-4062
Contributo su Rivista - Brusa, E.; Bruzzone, F.; Delprete, C.; Di Maggio, L. G.; Rosso, C. (2020)
Health indicators construction for damage level assessment in bearing diagnostics: A proposal of an energetic approach based on envelope analysis. In: APPLIED SCIENCES, vol. 10, pp. 1-24. ISSN 2076-3417
Contributo su Rivista - Brusa, E.; Bruzzone, F.; Delprete, C.; Di Maggio, L.; Rosso, C. (2020)
Valutazione dell’effetto della dimensione del difetto nella risposta vibrazionale di un cuscinetto a rulli. In: XLIX Conv. Naz. AIAS, On line, 2-5 settembre 2020
Contributo in Atti di Convegno (Proceeding) - Brusa, Eugenio; Bruzzone, Fabio; Delprete, Cristiana; Di Maggio, Luigi Gianpio; Rosso, ... (2020)
A proposal of a technique for correlating defect dimensions to vibration amplitude in bearing monitoring. In: 5th European Conference of the Prognostics and Health Management Society, Virtual conference, 27-31 July 2020, pp. 1-14. ISBN: 978-1-936263-32-5
Contributo in Atti di Convegno (Proceeding)