Modular System for High-Speed 24-Bit Data Acquisition of Triaxial MEMS Accelerometers for Structural Health Monitoring Research

2014 ◽  
pp. 313-320 ◽  
Author(s):  
Brianna Klingensmith ◽  
Stephen R. Burgess ◽  
Thomas A. Campbell ◽  
Peter G. Sherman ◽  
Michael Y. Feng ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Data Acquisition ◽  
Health Monitoring ◽  
High Speed ◽  
Modular System ◽  
Structural Health ◽  
Mems Accelerometers

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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 Reliable Data Acquisition System for a Low-Cost Accelerograph Applied to Structural Health Monitoring

2020 ◽  
Vol 3 (2) ◽  
pp. 181-194
Author(s):  
Milton Muñoz ◽  
Remigio Guevara ◽  
Santiago González ◽  
Juan Carlos Jiménez
Keyword(s):  
Structural Health Monitoring ◽  
Data Acquisition ◽  
Health Monitoring ◽  
Seismic Event ◽  
Low Cost ◽  
Recording System ◽  
Continuous Recording ◽  
Remote Communication ◽  
Structural Health ◽  
Hydroelectric Dam

This paper presents and evaluates a continuous recording system designed for a low-cost seismic station. The architecture has three main blocks. An accelerometer sensor based on MEMS technology (Microelectromechanical Systems), an SBC platform (Single Board Computer) with embedded Linux and a microcontroller device. In particular, the microcontroller represents the central component which operates as an intermediate agent to manage the communication between the accelerometer and the SBC block. This strategy allows the system for data acquisition in real time. On the other hand, the SBC platform is used for storing and processing data as well as in order to configure the remote communication with the station. This proposal is intended as a robust solution for structural health monitoring (i.e. in order to characterize the response of an infrastructure before, during and after a seismic event). The paper details the communication scheme between the system components, which has been minutely designed to ensure the samples are collected without information loss. Furthermore, for the experimental evaluation the station was located in the facilities on a relevant infrastructure, specifically a hydroelectric dam. The system operation was compared and verified with respect to a certified accelerograph station. Results prove that the continuous recording system operates successfully and allows for detecting seismic events according to requirements of structural health applications (i.e. detects events with a frequency of vibration less than 100 Hz). Specifically, through the system implemented it was possible to characterize the effect of a seismic event of 4 MD reported by the regional seismology network and with epicenter located about 30 Km of the hydroelectric dam. Particularly, the vibration frequencies detected on the infrastructure are in the range of 13 Hz and 29 Hz. Regarding the station performance, results from experiments reveals an average CPU load of 51%, consequently the processes configured on the SBC platform do not involve an overload. Finally, the average energy consumption of the station is close to 2.4 W, therefore autonomy provided by the backup system is aroud of 10 hours.

Design, Development, and Assembly of Space Flight Structural Health Monitoring Experiment

2012 ◽  
Author(s):  
David Siler ◽  
Ben Cooper ◽  
Chris White ◽  
Stephen Marinsek ◽  
Andrei Zagrai ◽  
...  
Keyword(s):  
Wave Propagation ◽  
Structural Health Monitoring ◽  
Data Acquisition ◽  
Health Monitoring ◽  
Space Flight ◽  
Design Development ◽  
Active Sensors ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
Propagation Experiment

The paper presents the design, development, and assembly of Structural Health Monitoring (SHM) experiments intended to be launch in space on a sub-orbital rocket flight as well as a high altitude balloon flight. The experiments designed investigate the use of both piezoelectric sensing hardware in a wave propagation experiment and piezoelectric wafer active sensors (PWAS) in an electromechanical impedance experiment as active elements of spacecraft SHM systems. The list of PWAS experiments includes a bolted-joint test and an experiment to monitor PWAS condition during spaceflight. Electromechanical impedances of piezoelectric sensors will be recorded in-flight at varying input frequencies using an onboard data acquisition system. The wave propagation experiment will utilize the sensing hardware of the Metis Design MD7 Digital SHM system. The payload will employ a triggering system that will begin experiment data acquisition upon sufficient saturation of g-loading. The experiment designs must be able to withstand the harsh environment of space, intense vibrations from the rocket launch, and large shock loading upon re-entry. The paper discusses issues encountered during design, development, and assembly of the payload and aspects central to successful demonstration of the SHM system during both the sub-orbital space flight and balloon launch.

scholarly journals Wireless Sensor Networks Composed of Standard Microcomputers and Smartphones for Applications in Structural Health Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2070 ◽  
Author(s):  
Guido Morgenthal ◽  
Jan Frederick Eick ◽  
Sebastian Rau ◽  
Jakob Taraben
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Structural Health Monitoring ◽  
Data Acquisition ◽  
Health Monitoring ◽  
Modal Identification ◽  
System Level ◽  
Wireless Sensor ◽  
Raspberry Pi ◽  
Structural Health

Wireless sensor networks have attracted great attention for applications in structural health monitoring due to their ease of use, flexibility of deployment, and cost-effectiveness. This paper presents a software framework for WiFi-based wireless sensor networks composed of low-cost mass market single-board computers. A number of specific system-level software components were developed to enable robust data acquisition, data processing, sensor network communication, and timing with a focus on structural health monitoring (SHM) applications. The framework was validated on Raspberry Pi computers, and its performance was studied in detail. The paper presents several characteristics of the measurement quality such as sampling accuracy and time synchronization and discusses the specific limitations of the system. The implementation includes a complementary smartphone application that is utilized for data acquisition, visualization, and analysis. A prototypical implementation further demonstrates the feasibility of integrating smartphones as data acquisition nodes into the network, utilizing their internal sensors. The measurement system was employed in several monitoring campaigns, three of which are documented in detail. The suitability of the system is evaluated based on comparisons of target quantities with reference measurements. The results indicate that the presented system can robustly achieve a measurement performance commensurate with that required in many typical SHM tasks such as modal identification. As such, it represents a cost-effective alternative to more traditional monitoring solutions.

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