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FMECA and FTA analysis for industrial and collaborative robots

Marco Scriboni

FMECA and FTA analysis for industrial and collaborative robots.

Rel. Massimo Sorli, Stefano Mauro, Andrea De Martin, Andrea Raviola. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2020

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In industrial manufacturing the automation of production processes has become a core factor and robots play a key role in improving productivity, availability and safety. In this prospective, unexpected robot downtime or failure may not only affect daily production tasks, but also cause unpredictable additional production and economic losses. Reliability and availability of the robots is therefore crucial in such systems and a careful maintenance based on condition monitoring of the actual operational health status of the machine has become to play a vital role in order to avoid unscheduled breakdowns, wasteful replacement or repairs before the end of the Remaining Useful Life (RUL) of components. In order to achieve better performances and reduce the overall maintenance costs, there has been an evolution in maintenance techniques: from the earliest Unplanned Breakdown Maintenance (UBM), which takes place only at breakdowns, passing through time-based Planned Preventive Maintenance (PPM), which sets a periodic interval to perform preventive maintenance prior to the Mean Time Between Failure (MTBF) regardless of the health status of a physical asset, finally settling on a more efficient maintenance approach which is Condition Based Maintenance (CBM). In order to perform CBM, an integration of health monitoring, diagnostics, prognostics and maintenance techniques (collectively known as Prognostics and Health Management (PHM)) is needed. In the current essay a Failure Mode, Effects and Criticality Analysis (FMECA) on the joint of a robotic manipulator (specifically UR5) is carried on, in order to find the most likely causes of fault/failure of the various components and a Fault Tree Analysis (FTA) is undertaken to find correlation between components faults.

Relators: Massimo Sorli, Stefano Mauro, Andrea De Martin, Andrea Raviola
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 186
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/16764
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