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Adaptive event-triggered anomaly detection in compressed vibration data

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)480-501
Number of pages22
JournalMechanical Systems and Signal Processing
Volume122
Early online date29 Dec 2018
DOIs
DateAccepted/In press - 20 Dec 2018
DateE-pub ahead of print - 29 Dec 2018
DatePublished (current) - 1 May 2019

Abstract

Anomaly detection is a crucial task in Prognostics and Condition Monitoring (PCM) of machinery. In modern remote PCM systems, data compression techniques are regularly used to reduce the need for bandwidth and storage. In these systems the challenge arises of how the compressed (distorted) vibration data affects the condition monitoring algorithms. This paper introduces a novel algorithm that can adaptively establish normal bounds of operation from continuous noisy vibration profiles working with compressed vibration data. The proposed technique is based on four modules, including feature extraction, feature fusion, extreme value vibration modeling and adaptive thresholding for anomaly detection. The proposed method has been validated with experiments using three time-series datasets. The experimental results indicate that the proposed algorithm is able to perform detection of malfunctions in rotating machines effectively without faulty reference data. Moreover, the proposed method is able to produce accurate early warning and alarm indications from both the raw and compressed (distorted) datasets with equal veracity.

    Research areas

  • Adaptive learning, Health status modeling, Machine faulty detection, Signal compression

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S0888327018308161?via%3Dihub. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 4 MB, PDF-document

    Embargo ends: 29/12/19

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    Licence: CC BY-NC-ND

DOI

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