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- W3009080359 endingPage "1680" @default.
- W3009080359 startingPage "1680" @default.
- W3009080359 abstract "With the rapid progress in the deep learning technology, it is being used for vibration-based structural health monitoring. When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the structure. The measured vibration responses show large deviation in spectral and transient characteristics for systems to be monitored. Consequently, the diagnosis using vibration requires complete understanding of the extracted features to discard the influence of surrounding environments or unnecessary variations. The deep-learning-based algorithms are expected to find increasing application in these complex problems due to their flexibility and robustness. This review provides a summary of studies applying machine learning algorithms for fault monitoring. The vibration factors were used to categorize the studies. A brief interpretation of deep neural networks is provided to guide further applications in the structural vibration analysis." @default.
- W3009080359 created "2020-03-13" @default.
- W3009080359 creator A5000397758 @default.
- W3009080359 creator A5052026571 @default.
- W3009080359 date "2020-03-02" @default.
- W3009080359 modified "2023-10-13" @default.
- W3009080359 title "Review of Vibration-Based Structural Health Monitoring Using Deep Learning" @default.
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