TY - JOUR
T1 - Miniature MEMS-based data recorder for prognostics and health management (PHM)
AU - Vohnout, Sonia
AU - Engelman, Matt
AU - Enikov, Eniko
N1 - Funding Information:
The authors have completed the first phase of an R&D program to develop miniature MEMS-based data recorders for prognostics and health management (PHM). This research is funded by a joint Small Business Innovation Research (SBIR) grant from the NAVAIR (PMA-261) and NAVSEA (PMS-408) program offices.
Funding Information:
This research is funded by a joint SBIR grant from the NAVAIR (PMA-261) and NAVSEA (PMS-408) program offices under contract N68335-10-C-0008. The authors would like to thank Ms. Katrina Mansfield from NAVAIR and Mr. John Norton from NAVSEA.
Funding Information:
This research is funded by a joint Small Business Innovation Research (SBIR) grant from the NAVAIR (PMA-261) and NAVSEA(PMS-408) program offices.
PY - 2011/8
Y1 - 2011/8
N2 - Novel prognostic sensors and reasoner algorithms are the core technology for detecting defects caused by accumulation of fatigue damage in electrical and mechanical systems over time. However, serious technical challenges to implementing a general health management strategy for helicopters and military aircraft still exist. For example, severe heat and vibration make it difficult to distinguish fault signatures from environmental noise. Moreover, bearing loads are very dynamic, making it difficult to distinguish subtle wear-out signatures from normal acoustic patterns. Detection can be improved by increasing the number of sensor locations, but this option is unattractive from the standpoint of added cost, weight, and data overhead of such a system.
AB - Novel prognostic sensors and reasoner algorithms are the core technology for detecting defects caused by accumulation of fatigue damage in electrical and mechanical systems over time. However, serious technical challenges to implementing a general health management strategy for helicopters and military aircraft still exist. For example, severe heat and vibration make it difficult to distinguish fault signatures from environmental noise. Moreover, bearing loads are very dynamic, making it difficult to distinguish subtle wear-out signatures from normal acoustic patterns. Detection can be improved by increasing the number of sensor locations, but this option is unattractive from the standpoint of added cost, weight, and data overhead of such a system.
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U2 - 10.1109/MIM.2011.5961365
DO - 10.1109/MIM.2011.5961365
M3 - Article
AN - SCOPUS:79961056809
SN - 1094-6969
VL - 14
SP - 18
EP - 26
JO - IEEE Instrumentation and Measurement Magazine
JF - IEEE Instrumentation and Measurement Magazine
IS - 4
M1 - 5961365
ER -