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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.
|Number of pages||22|
|Journal||Mechanical Systems and Signal Processing|
|Early online date||29 Dec 2018|
|Publication status||Published - 1 May 2019|
- Adaptive learning
- Health status modeling
- Machine faulty detection
- Signal compression
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- 1 Finished
ENergy Efficient Adaptive Computing with multi-grain heterogeneous architectures (ENEAC)
Nunez-Yanez, J. L.
5/01/16 → 4/01/20
Professor Nicholas A J Lieven
- Department of Aerospace Engineering - Professor
- Dynamics and Control
- Cabot Institute for the Environment
Person: Academic , Member