Miniature MEMS-based data recorder for prognostics and health management (PHM)

Sonia Vohnout, Matt Engelman, Eniko T Enikov

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number5961365
Pages (from-to)18-26
Number of pages9
JournalIEEE Instrumentation and Measurement Magazine
Volume14
Issue number4
DOIs
StatePublished - Aug 2011

Fingerprint

data recorders
microelectromechanical systems
health
MEMS
Bearings (structural)
signatures
Health
military aircraft
Military aircraft
helicopters
sensors
Sensors
Fatigue damage
Helicopters
aircraft
Acoustics
Wear of materials
damage
costs
heat

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation

Cite this

Miniature MEMS-based data recorder for prognostics and health management (PHM). / Vohnout, Sonia; Engelman, Matt; Enikov, Eniko T.

In: IEEE Instrumentation and Measurement Magazine, Vol. 14, No. 4, 5961365, 08.2011, p. 18-26.

Research output: Contribution to journalArticle

@article{996ca4c0cbd34041bffbdfe18a1f2bff,
title = "Miniature MEMS-based data recorder for prognostics and health management (PHM)",
abstract = "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.",
author = "Sonia Vohnout and Matt Engelman and Enikov, {Eniko T}",
year = "2011",
month = "8",
doi = "10.1109/MIM.2011.5961365",
language = "English (US)",
volume = "14",
pages = "18--26",
journal = "IEEE Instrumentation and Measurement Magazine",
issn = "1094-6969",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - Miniature MEMS-based data recorder for prognostics and health management (PHM)

AU - Vohnout, Sonia

AU - Engelman, Matt

AU - Enikov, Eniko T

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.

UR - http://www.scopus.com/inward/record.url?scp=79961056809&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79961056809&partnerID=8YFLogxK

U2 - 10.1109/MIM.2011.5961365

DO - 10.1109/MIM.2011.5961365

M3 - Article

AN - SCOPUS:79961056809

VL - 14

SP - 18

EP - 26

JO - IEEE Instrumentation and Measurement Magazine

JF - IEEE Instrumentation and Measurement Magazine

SN - 1094-6969

IS - 4

M1 - 5961365

ER -