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 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 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.

Design and Practice of Structural Health Monitoring System for Large Span Urban Rail Transit Bridge Based on Internet of Things

2019 ◽  
Author(s):  
Xueshan Liu ◽  
Xiaohu Chen ◽  
Jianting Zhou
Keyword(s):  
Structural Health Monitoring ◽  
Internet Of Things ◽  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System

<p>Rail transit bridges in Chongqing are of considerably complexed structure and enormous scale, thus even a minor impairment of the bridge structural performance could cause significant consequences in the daily operation of rail transit systems. To answer the realistic needs and requests brought up by the rail transit bridges, this paper introduces a framework of structural health monitoring system that is based on Internet of Things (IoT) and intelligent cloud platform. The system is designed to improve the safety of bridges by performing real-time intelligent acquisition and information transmission on subjects such as natural environment, stress status of the bridge structures, status of trains operation, and etc. The system realizes a series of functions: reception and storage of real-time information of the structure’s behavior, remote monitoring, early warning, and etc. Those functions empower the decision-maker to act more efficiently and more effectively in different scenarios, both at management, maintenance and trains operational levels.</p>

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