---
_id: '9880'
abstract:
- lang: eng
text: With the paradigm shift towards prognostic and health management (PHM) of
machinery, there is need for reliable PHM methodologies with narrow error bounds
to allow maintenance engineers take decisive maintenance actions based on the
prognostic results. Prognostics is mainly concerned with the estimation of the
remaining useful life (RUL) or time to failure (TTF). The accuracy of PHM methods
is usually a function of the features extracted from the raw data obtained from
sensors. In cases where the extracted features do not display clear degradation
trends, for instance highly loaded bearings, the accuracy of the state of the
art PHM methods is significantly affected. The data which lacks clear degradation
trend is referred to as non-trending data. This study presents a method for extracting
degradation trends from non-trending condition monitoring data for RUL estimation.
The raw signals are first filtered using a discrete wavelet transform (DWT) denoising
filter to remove noise from the acquired signals. Time domain, frequency domain
and time-frequency domain features are then extracted from the filtered signals.
An autoregressive model is then applied to the extracted features to identify
the degradation trends. Features representing the maximum health information are
then selected based on a performance evaluation criteria using extreme learning
machine (ELM) algorithm. The selected features can then be used as inputs in a
prognostic algorithm. The feasibility of the method is demonstrated using experimental
bearing vibration data. The performance of the method is evaluated on the accuracy
of RUL estimation and the results show that the method can be used to accurately
estimate RUL with a maximum error of 10\%.
author:
- first_name: James Kuria
full_name: Kimotho, James Kuria
last_name: Kimotho
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Kimotho JK, Sextro W. An approach for feature extraction and selection from
non-trending data for machinery prognosis. In: Proceedings of the Second European
Conference of the Prognostics and Health Management Society 2014. Vol 5. ;
2014.'
apa: Kimotho, J. K., & Sextro, W. (2014). An approach for feature extraction
and selection from non-trending data for machinery prognosis. In Proceedings
of the Second European Conference of the Prognostics and Health Management Society
2014 (Vol. 5).
bibtex: '@inproceedings{Kimotho_Sextro_2014, title={An approach for feature extraction
and selection from non-trending data for machinery prognosis}, volume={5}, booktitle={Proceedings
of the Second European Conference of the Prognostics and Health Management Society
2014}, author={Kimotho, James Kuria and Sextro, Walter}, year={2014} }'
chicago: Kimotho, James Kuria, and Walter Sextro. “An Approach for Feature Extraction
and Selection from Non-Trending Data for Machinery Prognosis.” In Proceedings
of the Second European Conference of the Prognostics and Health Management Society
2014, Vol. 5, 2014.
ieee: J. K. Kimotho and W. Sextro, “An approach for feature extraction and selection
from non-trending data for machinery prognosis,” in Proceedings of the Second
European Conference of the Prognostics and Health Management Society 2014,
2014, vol. 5.
mla: Kimotho, James Kuria, and Walter Sextro. “An Approach for Feature Extraction
and Selection from Non-Trending Data for Machinery Prognosis.” Proceedings
of the Second European Conference of the Prognostics and Health Management Society
2014, vol. 5, 2014.
short: 'J.K. Kimotho, W. Sextro, in: Proceedings of the Second European Conference
of the Prognostics and Health Management Society 2014, 2014.'
date_created: 2019-05-20T13:13:00Z
date_updated: 2019-09-16T10:37:35Z
department:
- _id: '151'
intvolume: ' 5'
keyword:
- autoregressive model ELM feature extraction feature selection non-trending Remaining
useful Life
language:
- iso: eng
publication: Proceedings of the Second European Conference of the Prognostics and
Health Management Society 2014
quality_controlled: '1'
status: public
title: An approach for feature extraction and selection from non-trending data for
machinery prognosis
type: conference
user_id: '55222'
volume: 5
year: '2014'
...