Faulty data detection and classification for bridge structural health monitoring via statistical and deep‐learning approach

Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection

(2020) Limin Sun et al.   JOURNAL OF STRUCTURAL ENGINEERING

Sensor fault management techniques for wireless smart sensor networks in structural health monitoring

(2019) Yuguang Fu et al.   Structural Control & Health Monitoring

Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-Sensor and Single Camera for Short and Medium Span Bridges

(2019) Ye Xia et al.   Remote Sensing

Deep learning for data anomaly detection and data compression of a long‐span suspension bridge

(2019) FuTao Ni et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Deep convolutional neural network model based chemical process fault diagnosis

(2018) Hao Wu et al.   COMPUTERS & CHEMICAL ENGINEERING

Computer vision and deep learning–based data anomaly detection method for structural health monitoring

(2018) Yuequan Bao et al.   STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL

Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

(2018) Zhiyi Tang et al.   Structural Control & Health Monitoring

Sensor fault detection with generalized likelihood ratio and correlation coefficient for bridge SHM

(2018) Lili Li et al.   JOURNAL OF SOUND AND VIBRATION

Convolutional neural networks for time series classification

(2017) Bendong Zhao et al.   Journal of Systems Engineering and Electronics

Development of sensor validation methodologies for structural health monitoring: A comprehensive review

(2017) Ting-Hua Yi et al.   MEASUREMENT

Harnessing data structure for recovery of randomly missing structural vibration responses time history: Sparse representation versus low-rank structure

(2016) Yongchao Yang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Distributed adaptive diagnosis of sensor faults using structural response data

(2016) Kosmas Dragos et al.   Smart Materials and Structures

Modeling and harnessing sparse and low-rank data structure: a new paradigm for structural dynamics, identification, damage detection, and health monitoring

(2016) Satish Nagarajaiah et al.   Structural Control & Health Monitoring

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy

(2014) Kay Smarsly et al.   ADVANCES IN ENGINEERING SOFTWARE

Detection, identification, and quantification of sensor fault in a sensor network

(2013) Jyrki Kullaa   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Blind denoising of structural vibration responses with outliers via principal component pursuit

(2013) Yongchao Yang et al.   Structural Control & Health Monitoring

A novel probabilistic method for robust parametric identification and outlier detection

(2012) Ka-Veng Yuen et al.   PROBABILISTIC ENGINEERING MECHANICS

Isolability of faults in sensor fault diagnosis

(2011) Reza Sharifi et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring

(2011) Jyrki Kullaa   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Structural Health Monitoring in mainland China: Review and Future Trends

(2010) Jinping Ou et al.   STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL

Sensor validation using minimum mean square error estimation

(2009) Jyrki Kullaa   MECHANICAL SYSTEMS AND SIGNAL PROCESSING