Cargando…
Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis
Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in th...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185423/ https://www.ncbi.nlm.nih.gov/pubmed/35684709 http://dx.doi.org/10.3390/s22114091 |
_version_ | 1784724720325754880 |
---|---|
author | Nguyen, Cong Dai Kim, Cheol Hong Kim, Jong-Myon |
author_facet | Nguyen, Cong Dai Kim, Cheol Hong Kim, Jong-Myon |
author_sort | Nguyen, Cong Dai |
collection | PubMed |
description | Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the proposed approach employs ANCT in the first stage to optimize vibration signals. The ANCT applies an adaptive denoising technique to each basic frequency segment in the whole frequency response of vibrations. Following that, a novel DRPCA is used to extract the discriminating low-dimensional features. The DRPCA initially determines each feature’s relative proximity to fault categories by computing the average Euclidian distance ratio between similar and dissimilar classes. The most discriminatory features with the lowest dimensions are selected, as determined by principal component analysis (PCA). The new DRPCA is created by combining distance ratio–based feature inspection with PCA. The optimal feature set containing the most discriminative features is then fed to the support vector machine classifier to identify multiple failure categories. The experimental results indicate that the proposed model outperforms the state-of-art approaches and offers the highest identification accuracy. |
format | Online Article Text |
id | pubmed-9185423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91854232022-06-11 Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis Nguyen, Cong Dai Kim, Cheol Hong Kim, Jong-Myon Sensors (Basel) Article Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the proposed approach employs ANCT in the first stage to optimize vibration signals. The ANCT applies an adaptive denoising technique to each basic frequency segment in the whole frequency response of vibrations. Following that, a novel DRPCA is used to extract the discriminating low-dimensional features. The DRPCA initially determines each feature’s relative proximity to fault categories by computing the average Euclidian distance ratio between similar and dissimilar classes. The most discriminatory features with the lowest dimensions are selected, as determined by principal component analysis (PCA). The new DRPCA is created by combining distance ratio–based feature inspection with PCA. The optimal feature set containing the most discriminative features is then fed to the support vector machine classifier to identify multiple failure categories. The experimental results indicate that the proposed model outperforms the state-of-art approaches and offers the highest identification accuracy. MDPI 2022-05-27 /pmc/articles/PMC9185423/ /pubmed/35684709 http://dx.doi.org/10.3390/s22114091 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nguyen, Cong Dai Kim, Cheol Hong Kim, Jong-Myon Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
title | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
title_full | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
title_fullStr | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
title_full_unstemmed | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
title_short | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
title_sort | gearbox fault identification model using an adaptive noise canceling technique, heterogeneous feature extraction, and distance ratio principal component analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185423/ https://www.ncbi.nlm.nih.gov/pubmed/35684709 http://dx.doi.org/10.3390/s22114091 |
work_keys_str_mv | AT nguyencongdai gearboxfaultidentificationmodelusinganadaptivenoisecancelingtechniqueheterogeneousfeatureextractionanddistanceratioprincipalcomponentanalysis AT kimcheolhong gearboxfaultidentificationmodelusinganadaptivenoisecancelingtechniqueheterogeneousfeatureextractionanddistanceratioprincipalcomponentanalysis AT kimjongmyon gearboxfaultidentificationmodelusinganadaptivenoisecancelingtechniqueheterogeneousfeatureextractionanddistanceratioprincipalcomponentanalysis |