A general anomaly detection approach applied to rolling element bearings via reduced-dimensionality transition matrix analysis
Rolling element bearings are vital components in most rotating machines. Bearings often operate in harsh environments where manufacturing imperfections, misalignments, and fatigue can result in reduced component lifespan. These failures are often preceded by changes in the normal vibration of the sy...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Harris, B. W [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Proceedings of the Institution of Mechanical Engineers / C - Los Angeles, Calif. [u.a.] : Sage, 1983, 230(2016), 13, Seite 2169-2180 |
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| Übergeordnetes Werk: |
volume:230 ; year:2016 ; number:13 ; pages:2169-2180 |
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| DOI / URN: |
10.1177/0954406215592439 |
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| 520 | |a Rolling element bearings are vital components in most rotating machines. Bearings often operate in harsh environments where manufacturing imperfections, misalignments, and fatigue can result in reduced component lifespan. These failures are often preceded by changes in the normal vibration of the system. Modeling and detecting these vibrational anomalies is common practice in predicting machine failure. This paper develops and implements a novel approach to detecting bearing vibration anomalies in the time-frequency domain. The performance of the new approach is quantified using both simulated and experimental bearing vibration data. In these ground-truth experiments, the proposed time-frequency method successfully detects anomalies (>98% true positive) using short time spans (<0.1 s) with low false alarm rates (<1% false positive). Using experimental data, this time-frequency approach is shown to outperform one-dimensional time series analysis techniques. | ||
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10.1177/0954406215592439 doi PQ20160815 (DE-627)OLC1980340056 (DE-599)GBVOLC1980340056 (PRQ)c934-d65ed08f7e71760449e9645c689485cea8ee9e70a300ce71a3dc2c736f4865990 (KEY)0121116120160000230001302169generalanomalydetectionapproachappliedtorollingele DE-627 ger DE-627 rakwb eng 620 DNB 52.10 bkl Harris, B. W verfasserin aut A general anomaly detection approach applied to rolling element bearings via reduced-dimensionality transition matrix analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Rolling element bearings are vital components in most rotating machines. Bearings often operate in harsh environments where manufacturing imperfections, misalignments, and fatigue can result in reduced component lifespan. These failures are often preceded by changes in the normal vibration of the system. Modeling and detecting these vibrational anomalies is common practice in predicting machine failure. This paper develops and implements a novel approach to detecting bearing vibration anomalies in the time-frequency domain. The performance of the new approach is quantified using both simulated and experimental bearing vibration data. In these ground-truth experiments, the proposed time-frequency method successfully detects anomalies (>98% true positive) using short time spans (<0.1 s) with low false alarm rates (<1% false positive). Using experimental data, this time-frequency approach is shown to outperform one-dimensional time series analysis techniques. Rolling Experiments Matrix Bearings Vibration Time series Milo, M. W oth Roan, M. J oth Enthalten in Proceedings of the Institution of Mechanical Engineers / C Los Angeles, Calif. [u.a.] : Sage, 1983 230(2016), 13, Seite 2169-2180 (DE-627)130863114 (DE-600)1030844-1 (DE-576)023106441 0954-4062 nnns volume:230 year:2016 number:13 pages:2169-2180 http://dx.doi.org/10.1177/0954406215592439 Volltext http://search.proquest.com/docview/1807871426 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4046 GBV_ILN_4700 52.10 AVZ AR 230 2016 13 2169-2180 |
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10.1177/0954406215592439 doi PQ20160815 (DE-627)OLC1980340056 (DE-599)GBVOLC1980340056 (PRQ)c934-d65ed08f7e71760449e9645c689485cea8ee9e70a300ce71a3dc2c736f4865990 (KEY)0121116120160000230001302169generalanomalydetectionapproachappliedtorollingele DE-627 ger DE-627 rakwb eng 620 DNB 52.10 bkl Harris, B. W verfasserin aut A general anomaly detection approach applied to rolling element bearings via reduced-dimensionality transition matrix analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Rolling element bearings are vital components in most rotating machines. Bearings often operate in harsh environments where manufacturing imperfections, misalignments, and fatigue can result in reduced component lifespan. These failures are often preceded by changes in the normal vibration of the system. Modeling and detecting these vibrational anomalies is common practice in predicting machine failure. This paper develops and implements a novel approach to detecting bearing vibration anomalies in the time-frequency domain. The performance of the new approach is quantified using both simulated and experimental bearing vibration data. In these ground-truth experiments, the proposed time-frequency method successfully detects anomalies (>98% true positive) using short time spans (<0.1 s) with low false alarm rates (<1% false positive). Using experimental data, this time-frequency approach is shown to outperform one-dimensional time series analysis techniques. Rolling Experiments Matrix Bearings Vibration Time series Milo, M. W oth Roan, M. J oth Enthalten in Proceedings of the Institution of Mechanical Engineers / C Los Angeles, Calif. [u.a.] : Sage, 1983 230(2016), 13, Seite 2169-2180 (DE-627)130863114 (DE-600)1030844-1 (DE-576)023106441 0954-4062 nnns volume:230 year:2016 number:13 pages:2169-2180 http://dx.doi.org/10.1177/0954406215592439 Volltext http://search.proquest.com/docview/1807871426 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4046 GBV_ILN_4700 52.10 AVZ AR 230 2016 13 2169-2180 |
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A general anomaly detection approach applied to rolling element bearings via reduced-dimensionality transition matrix analysis |
| abstract |
Rolling element bearings are vital components in most rotating machines. Bearings often operate in harsh environments where manufacturing imperfections, misalignments, and fatigue can result in reduced component lifespan. These failures are often preceded by changes in the normal vibration of the system. Modeling and detecting these vibrational anomalies is common practice in predicting machine failure. This paper develops and implements a novel approach to detecting bearing vibration anomalies in the time-frequency domain. The performance of the new approach is quantified using both simulated and experimental bearing vibration data. In these ground-truth experiments, the proposed time-frequency method successfully detects anomalies (>98% true positive) using short time spans (<0.1 s) with low false alarm rates (<1% false positive). Using experimental data, this time-frequency approach is shown to outperform one-dimensional time series analysis techniques. |
| abstractGer |
Rolling element bearings are vital components in most rotating machines. Bearings often operate in harsh environments where manufacturing imperfections, misalignments, and fatigue can result in reduced component lifespan. These failures are often preceded by changes in the normal vibration of the system. Modeling and detecting these vibrational anomalies is common practice in predicting machine failure. This paper develops and implements a novel approach to detecting bearing vibration anomalies in the time-frequency domain. The performance of the new approach is quantified using both simulated and experimental bearing vibration data. In these ground-truth experiments, the proposed time-frequency method successfully detects anomalies (>98% true positive) using short time spans (<0.1 s) with low false alarm rates (<1% false positive). Using experimental data, this time-frequency approach is shown to outperform one-dimensional time series analysis techniques. |
| abstract_unstemmed |
Rolling element bearings are vital components in most rotating machines. Bearings often operate in harsh environments where manufacturing imperfections, misalignments, and fatigue can result in reduced component lifespan. These failures are often preceded by changes in the normal vibration of the system. Modeling and detecting these vibrational anomalies is common practice in predicting machine failure. This paper develops and implements a novel approach to detecting bearing vibration anomalies in the time-frequency domain. The performance of the new approach is quantified using both simulated and experimental bearing vibration data. In these ground-truth experiments, the proposed time-frequency method successfully detects anomalies (>98% true positive) using short time spans (<0.1 s) with low false alarm rates (<1% false positive). Using experimental data, this time-frequency approach is shown to outperform one-dimensional time series analysis techniques. |
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| container_issue |
13 |
| title_short |
A general anomaly detection approach applied to rolling element bearings via reduced-dimensionality transition matrix analysis |
| url |
http://dx.doi.org/10.1177/0954406215592439 http://search.proquest.com/docview/1807871426 |
| remote_bool |
false |
| author2 |
Milo, M. W Roan, M. J |
| author2Str |
Milo, M. W Roan, M. J |
| ppnlink |
130863114 |
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| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1177/0954406215592439 |
| up_date |
2024-07-04T02:54:21.699Z |
| _version_ |
1803615376990273536 |
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