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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Expert systems Ročník 41; číslo 11
Hlavní autori: Kim, GwanPil, Jung, Jason J., Camacho, David
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.11.2024
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.13689