Frugal Data Acquisition and Transmission based on 1-bit Compressive Sensing for Structural Health Monitoring

2019 ◽  
Author(s):  
Liu Changjian ◽  
Chen Kai ◽  
Wang Yifan ◽  
Xu Bo ◽  
Gou Xuan ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Data Acquisition ◽  
Compressive Sensing ◽  
Health Monitoring ◽  
Structural Health

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.

scholarly journals Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach

2019 ◽  
Vol 19 (1) ◽  
pp. 293-304 ◽  
Author(s):  
Yuequan Bao ◽  
Zhiyi Tang ◽  
Hui Li
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Compressive Sensing ◽  
Health Monitoring ◽  
Applied Mathematics ◽  
Modal Identification ◽  
Data Reconstruction ◽  
Wireless Data ◽  
Sensing Data ◽  
Structural Health

Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare damage identification. The key issue in compressive sensing is finding the optimal solution for sparse optimization. In the past several years, many algorithms have been proposed in the field of applied mathematics. In this article, we propose a machine learning–based approach to solve the compressive-sensing data-reconstruction problem. By treating a computation process as a data flow, the solving process of compressive sensing–based data reconstruction is formalized into a standard supervised-learning task. The prior knowledge, i.e. the basis matrix and the compressive sensing–sampled signals, is used as the input and the target of the network; the basis coefficient matrix is embedded as the parameters of a certain layer; and the objective function of conventional compressive sensing is set as the loss function of the network. Regularized by l1-norm, these basis coefficients are optimized to reduce the error between the original compressive sensing–sampled signals and the masked reconstructed signals with a common optimization algorithm. In addition, the proposed network is able to handle complex bases, such as a Fourier basis. Benefiting from the nature of a multi-neuron layer, multiple signal channels can be reconstructed simultaneously. Meanwhile, the disassembled use of a large-scale basis makes the method memory-efficient. A numerical example of multiple sinusoidal waves and an example of field-test wireless data from a suspension bridge are carried out to illustrate the data-reconstruction ability of the proposed approach. The results show that high reconstruction accuracy can be obtained by the machine learning–based approach. In addition, the parameters of the network have clear meanings; the inference of the mapping between input and output is fully transparent, making the compressive-sensing data-reconstruction neural network interpretable.

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