Fault Tolerant Complex Event Detection in WSNs: A Case Study in Structural Health Monitoring

2015 ◽  
Vol 14 (12) ◽  
pp. 2502-2515 ◽  
Author(s):  
Xuefeng Liu ◽  
Jiannong Cao ◽  
Shaojie Tang ◽  
Peng Guo
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Event Detection ◽  
Fault Tolerant ◽  
Structural Health

scholarly journals Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

2018 ◽  
Vol 7 (3) ◽  
pp. 30 ◽  
Author(s):  
Chiara Bedon ◽  
Enrico Bergamo ◽  
Matteo Izzi ◽  
Salvatore Noè
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Full Scale ◽  
Shaking Table ◽  
Efficient Design ◽  
Dynamic Identification ◽  
Structural Health ◽  
Cable Stayed Bridge ◽  
Mems Accelerometers

In recent years, thanks to the simple and yet efficient design, Micro Electro-Mechanical Systems (MEMS) accelerometers have proven to offer a suitable solution for Structural Health Monitoring (SHM) in civil engineering applications. Such devices are typically characterised by high portability and durability, as well as limited cost, hence resulting in ideal tools for applications in buildings and infrastructure. In this paper, original self-made MEMS sensor prototypes are presented and validated on the basis of preliminary laboratory tests (shaking table experiments and noise level measurements). Based on the well promising preliminary outcomes, their possible application for the dynamic identification of existing, full-scale structural assemblies is then discussed, giving evidence of their potential via comparative calculations towards past literature results, inclusive of both on-site, Experimental Modal Analysis (EMA) and Finite Element Analytical estimations (FEA). The full-scale experimental validation of MEMS accelerometers, in particular, is performed using, as a case study, the cable-stayed bridge in Pietratagliata (Italy). Dynamic results summarised in the paper demonstrate the high capability of MEMS accelerometers, with evidence of rather stable and reliable predictions, and suggest their feasibility and potential for SHM purposes.

scholarly journals Structural reliability assessment based on optical monitoring system: case study

2016 ◽  
Vol 9 (2) ◽  
pp. 297-305 ◽  
Author(s):  
E. Mesquita ◽  
P. Antunes ◽  
A. A. Henriques ◽  
A. Arêde ◽  
P. S. André ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Reliability ◽  
Extreme Values ◽  
Low Cost ◽  
Reliability Assessment ◽  
Water Reservoir ◽  
Optical Systems ◽  
Structural Health

ABSTRACT Optical systems are recognized to be an important tool for structural health monitoring, especially for real time safety assessment, due to simplified system configuration and low cost when compared to regular systems, namely electrical systems. This work aims to present a case study on structural health monitoring focused on reliability assessment and applying data collected by a simplified optical sensing system. This way, an elevated reinforced concrete water reservoir was instrumented with a bi-axial optical accelerometer and monitored since January 2014. Taking into account acceleration data, the natural frequencies and relative displacements were estimated. The reliability analysis was performed based on generalized extreme values distribution (GEV) and the results were employed to build a forecast of the reliability of the water elevated reservoir for the next 100 years. The results showed that the optical system combined with GEV analysis, implemented in this experimental work, can provide adequate data for structural reliability assessment.

scholarly journals A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2955 ◽  
Author(s):  
Mario de Oliveira ◽  
Andre Monteiro ◽  
Jozue Vieira Filho
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lead Zirconate ◽  
Industrial Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

An Innovative Structural Health Monitoring System for Large Transmission Towers Based on GPS

2019 ◽  
Vol 19 (04) ◽  
pp. 1971002 ◽  
Author(s):  
X. X. Cheng ◽  
Y. J. Ge
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Time History ◽  
Time Behavior ◽  
Structural Health ◽  
Transmission Towers ◽  
Strong Winds ◽  
Force Equilibrium

In this paper, we propose an innovative structural health monitoring (SHM) system for large transmission towers that are frequently subjected to strong winds. The system is based on the strategy of using a static force equilibrium equation to calculate the whole structure’s real-time stress distribution according to its real-time behavior, as captured by the global positioning system (GPS). The reason for adopting this approach is that large transmission towers are fundamentally quasi-static structures and they are not prone to resonance under wind excitations. A case study is used to present the SHM system, then its effectiveness is validated by comparing the simulated SHM results with the exact solution obtained by a realistic time-history dynamic analysis. Additionally, we discuss the use of a new reliability analysis method based on the Ditlevsen’s bounds to assess the real-time structural conditions.

Non-contact structural health monitoring of a cable-stayed bridge: case study

2019 ◽  
Vol 15 (8) ◽  
pp. 1119-1136 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Linlin Ge ◽  
Kamyar Kildashti ◽  
Yincai Zhou ◽  
Bruce Harvey ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Cable Stayed Bridge

scholarly journals Vibration-Based Structural Health Monitoring Using Piezoelectric Transducers and Parametric t-SNE

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1716
Author(s):  
David Agis ◽  
Francesc Pozo
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Computational Cost ◽  
Principal Component ◽  
Piezoelectric Transducers ◽  
Majority Voting ◽  
Structural Health

In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.

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