期刊
SENSORS
卷 21, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/s21227761
关键词
acoustic emission; deep neural network; hit removal; long short-term memory; one-class support vector machine; remaining useful life; stacked autoencoder
资金
- National Demand Customized Life Safety R&D Project - Korean Ministry of the Interior and Safety (MOIS) [2019-MOIS41-002]
- National Disaster Management Research Institute (NDMI), Republic of Korea [2019-MOIS41] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This study presents a scheme for predicting the remaining useful lifetime of concrete structures from raw acoustic emission data by using a deep neural network to generate health indicators, with a hit removal process using OC-SVM to improve accuracy.
In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure's failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network.
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