Structural health monitoring for mechanical structures using multi-sensor data

Xiang, Jiawei; Yang, Zhibo; Aguilar, José

doi:10.1177/1550147718802019

Cited by 14 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a consequence, the fusion of multiple physical entities by multi-sensor approaches receives increasing attention in the scientific community. In 2018, e.g., the International Journal of Distributed Sensor Networks published a special collection dedicated to the topic of multi-sensor data for SHM [22]. Multi-sensor data fusion can be understood as either a fusion of homogenous sensor data (same sensor, respective data type, and physical entities; e.g., guided wave triangulation by three or more transducers [19]) or as a fusion of heterogeneous sensor data (different sensor, respective data types, and physical entities; e.g., neutral axis identification with temperature compensation by a Kalman filter [10]).…”

Section: Introductionmentioning

confidence: 99%

Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

Kralovec

Schagerl

2020

Sensors

141

View full text Add to dashboard Cite

Structural health monitoring (SHM) is the continuous on-board monitoring of a structure’s condition during operation by integrated systems of sensors. SHM is believed to have the potential to increase the safety of the structure while reducing its deadweight and downtime. Numerous SHM methods exist that allow the observation and assessment of different damages of different kinds of structures. Recently data fusion on different levels has been getting attention for joint damage evaluation by different SHM methods to achieve increased assessment accuracy and reliability. However, little attention is given to the question of which SHM methods are promising to combine. The current article addresses this issue by demonstrating the theoretical capabilities of a number of prominent SHM methods by comparing their fundamental physical models to the actual effects of damage on metal and composite structures. Furthermore, an overview of the state-of-the-art damage assessment concepts for different levels of SHM is given. As a result, dynamic SHM methods using ultrasonic waves and vibrations appear to be very powerful but suffer from their sensitivity to environmental influences. Combining such dynamic methods with static strain-based or conductivity-based methods and with additional sensors for environmental entities might yield a robust multi-sensor SHM approach. For demonstration, a potent system of sensors is defined and a possible joint data evaluation scheme for a multi-sensor SHM approach is presented.

show abstract

Section: Introductionmentioning

confidence: 99%

Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

Kralovec

Schagerl

2020

Sensors

141

View full text Add to dashboard Cite

show abstract

“…It reduces the building service life, leading to threats to public safety [1]. Structural health monitoring (SHM) is a critical technology to assure the safety of civil engineering [2,3]. SHM is mainly classified into two categories: data-driven methods based on statistical pattern recognition and physics-based methods based on finite element model updating [4].…”

Section: Introductionmentioning

confidence: 99%

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

Chen

Liu

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

show abstract

Measuring Full-Field Deformation in Ultra-High-Performance Concrete Structural Components Using Tag-Based Robotic Vision

Hassan

Sun

Wang

et al. 2022

Conference Proceedings of the Society for Experimental Mechanics Series

View full text Add to dashboard Cite

Structural health monitoring for mechanical structures using multi-sensor data

Cited by 14 publications

References 32 publications

Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

Measuring Full-Field Deformation in Ultra-High-Performance Concrete Structural Components Using Tag-Based Robotic Vision

Contact Info

Product

Resources

About