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11 April 2013 Turbine engine rotor health monitoring and durability evaluation using spin tests data
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Safety and maintenance cost are among the major features that engine manufacturers strive for in their design approach to produce efficient and successful products. However, this design success is subject to manufacturing highly reliable rotating components that typically undergo high rotational loading conditions that subject them to various types of failure initiation mechanisms. To counter such design concerns; health monitoring of these components is becoming a necessity, yet, this attribute remains somewhat challenging to implement. This is mostly due to the fact that presence of scattered loading conditions, crack sizes, component geometry and material property hinders the simplicity of imposing such applications. Therefore, exploitation of suitable techniques to monitor the health of these rotating components is ongoing and investigating other means of inspections such as non-destructive approaches to pre-detect hidden flaws and mini cracks is also being considered. These approaches or techniques extend more to assess materials’ discontinuities and other defects that have matured to the level where a failure is likely. This paper is pertained to presenting data collected from a spin experiment of a turbine like rotor disk tested at a range of rotational speeds up to 12000 rpm. It further includes an analytical modeling of the rotor vibration response that is characterized by a combination of numerical and experimental data. The data include blade tip clearance, tip timing measurements and shaft displacements. The tests are conducted at the NASA Glenn Research Center’s Rotordynamics Laboratory, a high precision spin rig. The results are evaluated and scrutinized to explore their relevance towards the development of a crack detection system and a supplemental physics based fault prediction analytical model.
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Ali Abdul-Aziz, Mark R. Woike, Michelle Clem, and George Y. Baaklini "Turbine engine rotor health monitoring and durability evaluation using spin tests data", Proc. SPIE 8693, Smart Sensor Phenomena, Technology, Networks, and Systems Integration 2013, 86930N (11 April 2013);

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