Alessandro Paolo Daga

Ph.D. in Ingegneria Meccanica , 31st cycle (2015-2018)

Ph.D. obtained in 2019

Dissertation:

Vibration Monitoring: Gearbox identification and faults detection (Abstract)

Tutors:

Luigi Garibaldi Alessandro Fasana

Profile

Research interests

Bearings (machine parts)
Digital signal processing
Machine condition monitoring
Machine diagnostics
Machine learning
Statistical learning
Vibration monitoring

Biography

Tracking the “signature” of a machine (the characteristic features of the machine extracted from the vibration signal, usually from accelerometers) evolving over time, it is possible to identify the main causes of vibration and preventively recognize damage and wear of the components, thus performing an accurate diagnosis. Two steps are fundamental: • Signal processing is exploited to highlight and extract damage-characteristic features. • Machine learning is employed to automatically infer anomalies in the vibration response from the extracted features. Such anomaly or novelty, in fact, can be put in relation to damage when confounding influences (i.e. different operational or environmental conditions) can be excluded or compensated. The aim is to create a reliable diagnostic system to be integrated into machine maintenance regimes so as to foster safety while, at the same time, saving on costs

Scientific branch

ING-IND/13 - APPLIED MECHANICS
(Area 0009 - Industrial and information engineering)

Awards and Honors

Teaching

Collegi of the degree programmes

Teachings

PhD

MostraNascondi A.A. passati

Master of Science

MostraNascondi A.A. passati

Bachelor of Science

MostraNascondi A.A. passati

Research

Research groups

Research projects

Projects funded by competitive calls

Publications

PoliTO co-authors

Last years publications

Publications by type

Works published during the Ph.D. View all publications in Porto@Iris

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