Health indicator construction based on normal states through FFT‐graph embedding

Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT‐based raw...

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Vydáno v:Expert systems Ročník 41; číslo 11
Hlavní autoři: Kim, GwanPil, Jung, Jason J., Camacho, David
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.11.2024
Témata:
fast Fourier transform
Fast Fourier transformations
Fault detection
Frequencies
graph convolutional autoencoder
graph embedding
Graphical representations
health indicator
Indicators
Performance degradation
Rotating machinery
Vibration
ISSN:0266-4720, 1468-0394
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Shrnutí:Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT‐based raw vibration data preprocessing and graph representation technique, which analyses changes in frequency bands to detect early degradation trends in vibration data that may appear normal. The approach proposes a methodology that utilizes a graph convolutional autoencoder trained using only normal data to extract health indicators using the differences in the vectors as degradation progresses. This approach has the advantage of using only normal data to detect subtle performance degradation early and effectively represent health indicators accordingly.
Bibliografie:ObjectType-Article-1
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ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.13689