Adapting approximate entropy as a health indicator of rotating machinery for estimation of remaining useful life

Cody Walker

doi:10.36001/phmconf.2019.v11i1.910

Adapting approximate entropy as a health indicator of rotating machinery for estimation of remaining useful life

Cody Walker

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Despite significant attention to online health monitoring and prognostics of bearings, many common health indicators are not sensitive to early stages of degradation. This research investigates the use of approximate entropy (ApEn), previously developed for fault diagnostics, as a health indicator for prognostics. ApEn quantifies the regularity of a signal; as bearings degrade, the frequency content of vibration signals changes and affects the ApEn as the vibration becomes more chaotic. Early results suggest ApEn supports earlier degradation detection and more predictable progression from fault to failure. This research focuses on optimizing parameters of the ApEn calculation to provide guidance across a variety of bearing types, sizes, and geometries in both steady-state and transient operation.

Original language	English
Title of host publication	Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Editors	N. Scott Clements, Bin Zhang, Abhinav Saxena
Publisher	Prognostics and Health Management Society
Edition	1
ISBN (Electronic)	9781936263059
DOIs	https://doi.org/10.36001/phmconf.2019.v11i1.910
State	Published - Sep 23 2019
Externally published	Yes
Event	11th Annual Conference of the Prognostics and Health Management Society, PHM 2019 - Scottsdale, United States Duration: Sep 23 2019 → Sep 26 2019

Publication series

Name	Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Number	1
Volume	11
ISSN (Print)	2325-0178

Conference

Conference	11th Annual Conference of the Prognostics and Health Management Society, PHM 2019
Country/Territory	United States
City	Scottsdale
Period	09/23/19 → 09/26/19

Access to Document

10.36001/phmconf.2019.v11i1.910

Cite this

Walker, C. (2019). Adapting approximate entropy as a health indicator of rotating machinery for estimation of remaining useful life. In N. S. Clements, B. Zhang, & A. Saxena (Eds.), Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM (1 ed.). (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Vol. 11, No. 1). Prognostics and Health Management Society. https://doi.org/10.36001/phmconf.2019.v11i1.910

Walker, Cody. / Adapting approximate entropy as a health indicator of rotating machinery for estimation of remaining useful life. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. editor / N. Scott Clements ; Bin Zhang ; Abhinav Saxena. 1. ed. Prognostics and Health Management Society, 2019. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; 1).

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title = "Adapting approximate entropy as a health indicator of rotating machinery for estimation of remaining useful life",

abstract = "Despite significant attention to online health monitoring and prognostics of bearings, many common health indicators are not sensitive to early stages of degradation. This research investigates the use of approximate entropy (ApEn), previously developed for fault diagnostics, as a health indicator for prognostics. ApEn quantifies the regularity of a signal; as bearings degrade, the frequency content of vibration signals changes and affects the ApEn as the vibration becomes more chaotic. Early results suggest ApEn supports earlier degradation detection and more predictable progression from fault to failure. This research focuses on optimizing parameters of the ApEn calculation to provide guidance across a variety of bearing types, sizes, and geometries in both steady-state and transient operation.",

author = "Cody Walker",

note = "Publisher Copyright: {\textcopyright} 2019 Prognostics and Health Management Society. All rights reserved.; 11th Annual Conference of the Prognostics and Health Management Society, PHM 2019 ; Conference date: 23-09-2019 Through 26-09-2019",

year = "2019",

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language = "English",

series = "Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM",

publisher = "Prognostics and Health Management Society",

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Walker, C 2019, Adapting approximate entropy as a health indicator of rotating machinery for estimation of remaining useful life. in NS Clements, B Zhang & A Saxena (eds), Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. 1 edn, Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, no. 1, vol. 11, Prognostics and Health Management Society, 11th Annual Conference of the Prognostics and Health Management Society, PHM 2019, Scottsdale, United States, 09/23/19. https://doi.org/10.36001/phmconf.2019.v11i1.910

Adapting approximate entropy as a health indicator of rotating machinery for estimation of remaining useful life. / Walker, Cody.
Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. ed. / N. Scott Clements; Bin Zhang; Abhinav Saxena. 1. ed. Prognostics and Health Management Society, 2019. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Vol. 11, No. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Walker, Cody

PY - 2019/9/23

Y1 - 2019/9/23

N2 - Despite significant attention to online health monitoring and prognostics of bearings, many common health indicators are not sensitive to early stages of degradation. This research investigates the use of approximate entropy (ApEn), previously developed for fault diagnostics, as a health indicator for prognostics. ApEn quantifies the regularity of a signal; as bearings degrade, the frequency content of vibration signals changes and affects the ApEn as the vibration becomes more chaotic. Early results suggest ApEn supports earlier degradation detection and more predictable progression from fault to failure. This research focuses on optimizing parameters of the ApEn calculation to provide guidance across a variety of bearing types, sizes, and geometries in both steady-state and transient operation.

AB - Despite significant attention to online health monitoring and prognostics of bearings, many common health indicators are not sensitive to early stages of degradation. This research investigates the use of approximate entropy (ApEn), previously developed for fault diagnostics, as a health indicator for prognostics. ApEn quantifies the regularity of a signal; as bearings degrade, the frequency content of vibration signals changes and affects the ApEn as the vibration becomes more chaotic. Early results suggest ApEn supports earlier degradation detection and more predictable progression from fault to failure. This research focuses on optimizing parameters of the ApEn calculation to provide guidance across a variety of bearing types, sizes, and geometries in both steady-state and transient operation.

UR - https://www.scopus.com/pages/publications/85084008401

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DO - 10.36001/phmconf.2019.v11i1.910

M3 - Conference contribution

AN - SCOPUS:85084008401

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BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM

A2 - Clements, N. Scott

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Walker C. Adapting approximate entropy as a health indicator of rotating machinery for estimation of remaining useful life. In Clements NS, Zhang B, Saxena A, editors, Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. 1 ed. Prognostics and Health Management Society. 2019. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; 1). doi: 10.36001/phmconf.2019.v11i1.910

Adapting approximate entropy as a health indicator of rotating machinery for estimation of remaining useful life

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