Research nowadays takes advantage of the cyclostationary properties in the vibration waveforms of rotating machines for fault detection. For example, cyclic spectral coherence maps (CSCM) break down vibration signals into cyclic and carrier frequencies. However, the large size of the CSCMs and the enormous amount of data makes it challenging to identify the faults. Therefore this paper presents an approach that uses compressed CSCMs as input for a deep learning autoencoder, as a means to detect mechanical failures from vibration signals. The autoencoder learns to reconstruct a healthy version of the CSCMs, taking advantage of the generalization bias due to the unbalanced existence of healthy data. Furthermore, if the autoencoder receives a faulty CSCM as input, it reconstructs a healthy version. Alarms are triggered if the residuals between reconstruction and input CSCM exceed a calculated threshold. Next, the reporting step lists the cyclic order, carrier frequency band, and their respective alarm numbers. Five years of data from five different turbines in the field is used to train the autoencoder. The evaluation is validated on data from two faulty and eight healthy turbines. This work provides a fast, reliable and automated method to detect mechanical failures.
Originele taal-2English
TitelASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition
Aantal pagina's9
ISBN van elektronische versie9780791887127
ISBN van geprinte versie978-0-7918-8712-7
StatusPublished - 28 sep 2023

Publicatie series

NaamProceedings of the ASME Turbo Expo

Bibliografische nota

Funding Information:
This research was supported by funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme and under the VLAIO Supersized 4.0 ICON project. Calculations were facilitated by the “Vlaams Supercomputer Centrum” and VSC Cloud.

Publisher Copyright:
© 2022 Institute of Physics Publishing. All rights reserved.

Copyright 2022 Elsevier B.V., All rights reserved.


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