scholarly journals Integration with 3D Visualization and IoT-Based Sensors for Real-Time Structural Health Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 6988
Author(s):  
Hung-Fu Chang ◽  
Mohammad Shokrolah Shokrolah Shirazi
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
3D Model ◽  
3D Visualization ◽  
Sensor Data ◽  
Unique Identifier ◽  
Element Analysis ◽  
Structural Dynamic ◽  
Structural Health

Real-time monitoring on displacement and acceleration of a structure provides vital information for people in different applications such as active control and damage warning systems. Recent developments of the Internet of Things (IoT) and client-side web technologies enable a wireless microcontroller board with sensors to process structural-related data in real-time and to interact with servers so that end-users can view the final processed results of the servers through a browser in a computer or a mobile phone. Unlike traditional structural health monitoring (SHM) systems that deliver warnings based on peak acceleration of earthquake, we built a real-time SHM system that converts raw sensor results into movements and rotations on the monitored structure’s three-dimensional (3D) model. This unique approach displays the overall structural dynamic movements directly from measured displacement data, rather than using force analysis, such as finite element analysis, to predict the displacement statically. As an application to our research outcomes, patterns of movements related to its structure type can be collected for further cross-validating the results derived from the traditional stress-strain analysis. In this work, we overcome several challenges that exist in displaying the 3D effects in real-time. From our proposed algorithm that converts the global displacements into element’s local movements, our system can calculate each element’s (e.g., column’s, beam’s, and floor’s) rotation and displacement at its local coordinate while the sensor’s monitoring result only provides displacements at the global coordinate. While we consider minimizing the overall sensor usage costs and displaying the essential 3D movements at the same time, a sensor deployment method is suggested. To achieve the need of processing the enormous amount of sensor data in real-time, we designed a novel structure for saving sensor data, where relationships among multiple sensor devices and sensor’s spatial and unique identifier can be presented. Moreover, we built a sensor device that can send the monitoring data via wireless network to the local server or cloud so that the SHM web can integrate what we develop altogether to show the real-time 3D movements. In this paper, a 3D model is created according to a two-story structure to demonstrate the SHM system functionality and validate our proposed algorithm.

scholarly journals The Industry Internet of Things (IIoT) as a Methodology for Autonomous Diagnostics in Aerospace Structural Health Monitoring

Aerospace ◽  
2020 ◽  
Vol 7 (5) ◽  
pp. 64
Author(s):  
Sarah Malik ◽  
Rakeen Rouf ◽  
Krzysztof Mazur ◽  
Antonios Kontsos
Keyword(s):  
Structural Health Monitoring ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Health Monitoring ◽  
Sensor Data ◽  
Management Approach ◽  
Time Data ◽  
Data Quality Management ◽  
Structural Health

Structural Health Monitoring (SHM), defined as the process that involves sensing, computing, and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a way to decisively address SHM’s big data problem and provide a framework for autonomous processing. The key focus of IIoT is operational efficiency and cost optimization. The purpose, therefore, of the IIoT approach in this investigation is to develop a framework that connects nondestructive evaluation sensor data with real-time processing algorithms on an IoT hardware/software system to provide diagnostic capabilities for efficient data processing related to SHM. Specifically, the proposed IIoT approach is comprised of three components: the Cloud, the Fog, and the Edge. The Cloud is used to store historical data as well as to perform demanding computations such as off-line machine learning. The Fog is the hardware that performs real-time diagnostics using information received both from sensing and the Cloud. The Edge is the bottom level hardware that records data at the sensor level. In this investigation, an application of this approach to evaluate the state of health of an aerospace grade composite material at laboratory conditions is presented. The key link that limits human intervention in data processing is the implemented database management approach which is the particular focus of this manuscript. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. Through this database, the algorithms used are capable to execute filtering by classification at the Fog level, as live data is recorded. The processed data is automatically sent to the Cloud for further operations such as visualization. The system integration with three layers provides an opportunity to create a paradigm for intelligent real-time data quality management.

scholarly journals Structural Health Monitoring with Sensor Data and Cosine Similarity for Multi-Damages

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3047 ◽  
Author(s):  
Byungmo Kim ◽  
Cheonhong Min ◽  
Hyungwoo Kim ◽  
Sugil Cho ◽  
Jaewon Oh ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Damage Identification ◽  
Offshore Structures ◽  
Cosine Similarity ◽  
Sensor Data ◽  
Current Status ◽  
Damage Level ◽  
Structural Health

There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique.

scholarly journals Practical Implementation of Structural Health Monitoring in Multi-Story Buildings

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 263
Author(s):  
Arvindan Sivasuriyan ◽  
D.S. Vijayan ◽  
Wojciech Górski ◽  
Łukasz Wodzyński ◽  
Magdalena Daria Vaverková ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Sensor Technology ◽  
Future Research ◽  
Practical Implementation ◽  
Building Structures ◽  
Element Analysis ◽  
Self Monitoring ◽  
Structural Health

This study investigated operational and structural health monitoring (SHM) as well as damage evaluations for building structures. The study involved damage detection and the assessment of buildings by placing sensors and by assuming weak areas, and considered situations of assessment and self-monitoring. From this perspective, advanced sensor technology and data acquisition techniques can systematically monitor a building in real time. Furthermore, the structure’s response and behavior were observed and recorded to predict the damage to the building. In this paper, we discuss the real-time monitoring and response of buildings, which includes both static and dynamic analyses along with numerical simulation studies such as finite element analysis (FEA), and recommendations for the future research and development of SHM are made.

scholarly journals Arrays of Conformable Ultrasonic Lamb Wave Transducers for Structural Health Monitoring with Real-time Electronics

2014 ◽  
Vol 87 ◽  
pp. 1266-1269 ◽  
Author(s):  
L. Capineri ◽  
A. Bulletti ◽  
M. Calzolai ◽  
P. Giannelli ◽  
D. Francesconi
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Structural Health

scholarly journals A Wireless Strain Sensor Network for Structural Health Monitoring

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chengyin Liu ◽  
Jun Teng ◽  
Ning Wu
Keyword(s):  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Evaluation System ◽  
Strain Sensor ◽  
Modal Testing ◽  
Dynamic Strain ◽  
Structural Dynamic ◽  
Structural Health ◽  
Strain Monitoring

Structural strain under external environmental loads is one of the main monitoring parameters in structural health monitoring or dynamic tests. This paper presents a wireless strain sensor network (WSSN) design for monitoring structural dynamic strain field. A precision strain sensor board is developed and integrated with the IRIS mote hardware/software platform for multichannel strain gauge signal conditioning and wireless monitoring. Measurement results confirm the sensor’s functionality regarding its static and dynamic characterization. Furthermore, in order to verify the functionality of the designed wireless strain sensor for dynamic strain monitoring, a cluster-star network evaluation system is developed for strain modal testing on an experimental steel truss structure. Test results show very good agreement with the finite element (FE) simulations. This paper demonstrates the feasibility of the proposed WSSN for large structural dynamic strain monitoring.

Safety 4.0 for the Most Dangerous Industrial Objects

2021 ◽  
Author(s):  
Igor Razuvaev
Keyword(s):  
Decision Making ◽  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Low Temperatures ◽  
Storage Tanks ◽  
Data Verification ◽  
Structural Health

Abstract Isothermal Storage Tanks (IST) contains tens thousands tons of the liquefied gases (propane, ethane, ethylene, etc.) at very low temperatures. These are the most dangerous industrial objects. In the report the Integrated Structural Health Monitoring (ISHM) Systems for the management of the integrity of these tanks in real time is considered. The structure of the ISHM Systems, NDT methods, technical characteristics, data verification procedures, a decision-making algorithm and practical results are described.

Dielectric Response of Adaptive Piezoelectric Sensoriactuators

2000 ◽  
Author(s):  
Jeffrey S. Vipperman ◽  
Deyu Li
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Field Dependence ◽  
Health Monitoring ◽  
Dielectric Response ◽  
Capacitance Measurement ◽  
Structural Health ◽  
Nonideal Behavior

Abstract This paper closely examines the nature of the dielectric response of piezoceramics that are used as Adaptive Piezoelectric Sensoriactuators (APSAs). Firstly, it is demonstrated that he APSA possesses real time structural health monitoring abilities, based on the capacitance measurement of the piezoceramic. Secondly, nonideal behavior including lossy, hysteretic, and field dependence is measured in the piezoceramics and a method mitigating some of this response in the Adaptive Piezoelectric Sensoriactuator is proposed.

Development of IOT Based Smart Instrumentation for the Real Time Structural Health Monitoring

2020 ◽  
Vol 113 (3) ◽  
pp. 1641-1649
Author(s):  
Bhawani Shankar Chowdhry ◽  
Ali Akbar Shah ◽  
Muhammad Aslam Uqaili ◽  
Tayab Memon
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
The Real ◽  
Structural Health
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