Review of Vibration-Based Structural Health Monitoring Using Deep Learning

• Published in: Applied sciences Vol. 10; no. 5; p. 1680
• Main Authors: Toh, Gyungmin, Park, Junhong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2020
• Subjects: Accuracy; Algorithms; Aluminum; Composite materials; Deep learning; deep neural network; Fault diagnosis; health monitoring; Laboratories; Localization; Machine learning; Neural networks; Principal components analysis; Signal processing; Vibration; Wavelet transforms
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10051680

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.