Novel Anomaly Detection Based on the nearest Neighbours and Sequential Methods

L. Gelman, I. Petrunin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A novel anomaly detection technique based on the k-nearest neighbours method with the sequential majority rule is proposed, developed and validated for damage diagnosis.

Original languageEnglish
Title of host publication8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011
Subtitle of host publicationCM 2011/MFPT 2011
PublisherBritish Institute of Non-Destructive Testing
Pages1005-1012
Number of pages8
Volume1
ISBN (Print)9781618390141
Publication statusPublished - 2011
Externally publishedYes
Event8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies - Cardiff, United Kingdom
Duration: 20 Jun 201122 Jun 2011
Conference number: 8

Conference

Conference8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies
Abbreviated titleCM/MFPT 2011
CountryUnited Kingdom
CityCardiff
Period20/06/1122/06/11

Cite this

Gelman, L., & Petrunin, I. (2011). Novel Anomaly Detection Based on the nearest Neighbours and Sequential Methods. In 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011: CM 2011/MFPT 2011 (Vol. 1, pp. 1005-1012). British Institute of Non-Destructive Testing.
Gelman, L. ; Petrunin, I. / Novel Anomaly Detection Based on the nearest Neighbours and Sequential Methods. 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011: CM 2011/MFPT 2011. Vol. 1 British Institute of Non-Destructive Testing, 2011. pp. 1005-1012
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title = "Novel Anomaly Detection Based on the nearest Neighbours and Sequential Methods",
abstract = "A novel anomaly detection technique based on the k-nearest neighbours method with the sequential majority rule is proposed, developed and validated for damage diagnosis.",
keywords = "Rotating machines, condition mointoring, Failure Analysis",
author = "L. Gelman and I. Petrunin",
year = "2011",
language = "English",
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volume = "1",
pages = "1005--1012",
booktitle = "8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011",
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Gelman, L & Petrunin, I 2011, Novel Anomaly Detection Based on the nearest Neighbours and Sequential Methods. in 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011: CM 2011/MFPT 2011. vol. 1, British Institute of Non-Destructive Testing, pp. 1005-1012, 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Cardiff, United Kingdom, 20/06/11.

Novel Anomaly Detection Based on the nearest Neighbours and Sequential Methods. / Gelman, L.; Petrunin, I.

8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011: CM 2011/MFPT 2011. Vol. 1 British Institute of Non-Destructive Testing, 2011. p. 1005-1012.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Novel Anomaly Detection Based on the nearest Neighbours and Sequential Methods

AU - Gelman, L.

AU - Petrunin, I.

PY - 2011

Y1 - 2011

N2 - A novel anomaly detection technique based on the k-nearest neighbours method with the sequential majority rule is proposed, developed and validated for damage diagnosis.

AB - A novel anomaly detection technique based on the k-nearest neighbours method with the sequential majority rule is proposed, developed and validated for damage diagnosis.

KW - Rotating machines

KW - condition mointoring

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BT - 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011

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Gelman L, Petrunin I. Novel Anomaly Detection Based on the nearest Neighbours and Sequential Methods. In 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011: CM 2011/MFPT 2011. Vol. 1. British Institute of Non-Destructive Testing. 2011. p. 1005-1012