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Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Luleå University of Technology, SKF-LTU University Technology Centre.ORCID iD: 0000-0001-6099-3882
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825X
Research and Technology Development – Diagnostics and Prognostics, SKF, Luleå, Sweden.
2021 (English)In: Journal of Risk and Reliability, ISSN 1748-006X, E-ISSN 1748-0078, Vol. 235, no 4, p. 660-675Article in journal (Refereed) Published
Abstract [en]

We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

Place, publisher, year, edition, pages
Sage Publications, 2021. Vol. 235, no 4, p. 660-675
Keywords [en]
Unsupervised feature learning, dictionary learning, sparse coding, conditon monitoring, wind turbine
National Category
Embedded Systems Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Cyber-Physical Systems; Electronic systems
Identifiers
URN: urn:nbn:se:ltu:diva-82367DOI: 10.1177/1748006X20984260ISI: 000641110900001Scopus ID: 2-s2.0-85098860500OAI: oai:DiVA.org:ltu-82367DiVA, id: diva2:1517182
Funder
The Kempe Foundations, SMK-1429
Note

Validerad;2021;Nivå 2;2021-07-02 (beamah);

Finansiär: SKF

Available from: 2021-01-13 Created: 2021-01-13 Last updated: 2023-09-05Bibliographically approved

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Martin-del-Campo, SergioSandin, Fredrik

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