Cargando…
Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds
Early identification of failures in rolling element bearings is an important research issue in mechanical systems. In this study, a reliable methodology for bearing fault detection is proposed, which is based on an optimal sub-band selection scheme using the discrete wavelet packet transform (DWPT)...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981466/ https://www.ncbi.nlm.nih.gov/pubmed/29723996 http://dx.doi.org/10.3390/s18051389 |
_version_ | 1783328052589101056 |
---|---|
author | Nguyen, Hung Ngoc Kim, Jaeyoung Kim, Jong-Myon |
author_facet | Nguyen, Hung Ngoc Kim, Jaeyoung Kim, Jong-Myon |
author_sort | Nguyen, Hung Ngoc |
collection | PubMed |
description | Early identification of failures in rolling element bearings is an important research issue in mechanical systems. In this study, a reliable methodology for bearing fault detection is proposed, which is based on an optimal sub-band selection scheme using the discrete wavelet packet transform (DWPT) and envelope power analysis techniques. A DWPT-based decomposition is first performed to extract the characteristic defect features from the acquired acoustic emission (AE) signals. The envelope power spectrum (EPS) of each sub-band signal is then calculated to detect the characteristic defect frequencies to reveal abnormal symptoms in bearings. The selection of an appropriate sub-band is essential for effective fault diagnosis, as it can reveal intrinsically explicit information about different types of bearing faults. To address this issue, we propose a Gaussian distribution model-based health-related index (HI) that is a powerful quantitative parameter to accurately estimate the severity of bearing defects. The most optimal sub-band for fault detection is determined using two dimensional (2D) visualization analysis. The efficiency of the proposed approach is validated using several experiments in which different defect conditions are identified under variable, and low operational speeds. |
format | Online Article Text |
id | pubmed-5981466 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59814662018-06-05 Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds Nguyen, Hung Ngoc Kim, Jaeyoung Kim, Jong-Myon Sensors (Basel) Article Early identification of failures in rolling element bearings is an important research issue in mechanical systems. In this study, a reliable methodology for bearing fault detection is proposed, which is based on an optimal sub-band selection scheme using the discrete wavelet packet transform (DWPT) and envelope power analysis techniques. A DWPT-based decomposition is first performed to extract the characteristic defect features from the acquired acoustic emission (AE) signals. The envelope power spectrum (EPS) of each sub-band signal is then calculated to detect the characteristic defect frequencies to reveal abnormal symptoms in bearings. The selection of an appropriate sub-band is essential for effective fault diagnosis, as it can reveal intrinsically explicit information about different types of bearing faults. To address this issue, we propose a Gaussian distribution model-based health-related index (HI) that is a powerful quantitative parameter to accurately estimate the severity of bearing defects. The most optimal sub-band for fault detection is determined using two dimensional (2D) visualization analysis. The efficiency of the proposed approach is validated using several experiments in which different defect conditions are identified under variable, and low operational speeds. MDPI 2018-05-01 /pmc/articles/PMC5981466/ /pubmed/29723996 http://dx.doi.org/10.3390/s18051389 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nguyen, Hung Ngoc Kim, Jaeyoung Kim, Jong-Myon Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds |
title | Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds |
title_full | Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds |
title_fullStr | Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds |
title_full_unstemmed | Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds |
title_short | Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds |
title_sort | optimal sub-band analysis based on the envelope power spectrum for effective fault detection in bearing under variable, low speeds |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981466/ https://www.ncbi.nlm.nih.gov/pubmed/29723996 http://dx.doi.org/10.3390/s18051389 |
work_keys_str_mv | AT nguyenhungngoc optimalsubbandanalysisbasedontheenvelopepowerspectrumforeffectivefaultdetectioninbearingundervariablelowspeeds AT kimjaeyoung optimalsubbandanalysisbasedontheenvelopepowerspectrumforeffectivefaultdetectioninbearingundervariablelowspeeds AT kimjongmyon optimalsubbandanalysisbasedontheenvelopepowerspectrumforeffectivefaultdetectioninbearingundervariablelowspeeds |