.png){kind=icon}
返回
Data anomaly detection with automatic feature selection and deep learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.istruc.2023.105082.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As increasing sensor-based structural health monitoring (SHM) systems are implemented on civil infrastructures, sensor data reliability plays a crucial role in the assessment of operational performance of bridges. Sensors are heavily exposed to harsh environmental conditions during their operations and inevitably lead to possible unstable performance or failure. Thus, to accurately identify faulty sensors is a prerequisite to processing and analyzing the collected data for assessment purpose. Recently, researchers adopted the convolutional neural network (CNN) approach to identify faulty sensors, focusing on image features. Such approach may overlook some important detailed signal features and the time series approach may still be needed. However, algorithms based on time series tend to be time consuming because of the lengthy and high dimensional dataset. This may be effectively resolved using an automatic feature selection technique, namely Tsfresh, as proposed in this paper to select highly relevant signal features based on statistical tests of significance. A deep learning technique based on fully convolutional network (FCN) can then be efficiently employed for anomaly classification. The algorithm is validated using a dataset collected from a real cable-stayed bridge and results show that the proposed method significantly reduces the training time for the neural network, albeit with high classification accuracy.
Keyword:
Structural health monitoring
Anomaly detection
Convolutional neural network
Feature selection
Tsfresh
Deep learning
期刊
IF:
4.3
论文数:
1.3W
被引数:
2.7W
机构
引用论文
分享收藏Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders面向自动结构健康监测的数据异常检测: 利用生成对抗网络和自动编码器
PREAI
分享收藏Chiller sensor fault detection using a self-Adaptive Principal Component Analysis method.png)
ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Changes of Endogenous Hormones and Polyamines in Cytoplasmic Male Sterile Non-heading Chinese Cabbage During Anther DevelopmentHortScience
OAAI
HortScienceIF0
OAAI分享收藏
查看更多内容