Adaptive event-triggered anomaly detection in compressed vibration data

Yang Zhang*, Paul Hutchinson, Nicholas A.J. Lieven, Jose Nunez-Yanez

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

2 Citations (Scopus)
89 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)480-501
Number of pages22
JournalMechanical Systems and Signal Processing
Volume122
Early online date29 Dec 2018
DOIs
Publication statusPublished - 1 May 2019

Keywords

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

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