160 A Proposal of a Real-Time Data Processing Method for Structural Health Monitoring

2011 ◽  
Vol 2011 (0) ◽  
pp. _160-1_-_160-10_
Author(s):  
Kohji SAKATA ◽  
Takuya YOSHIMURA
Keyword(s):  
Structural Health Monitoring ◽  
Data Processing ◽  
Real Time ◽  
Health Monitoring ◽  
Processing Method ◽  
Time Data ◽  
Structural Health ◽  
Data Processing Method ◽  
Real Time Data ◽  
Real Time Data Processing

Aerocraft Real-Time Measured Data Processing Method Based on Combined Filtering

2014 ◽  
Vol 716-717 ◽  
pp. 983-986
Author(s):  
Yan Li ◽  
Hua Jun Liu ◽  
Guang Lang Bian ◽  
Miao Hui Liu
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Measured Data ◽  
Control Procedure ◽  
Time Data ◽  
The Real ◽  
Data Processing Method ◽  
Real Time Data ◽  
Processing Accuracy ◽  
Real Time Data Processing

To solve the problems that resulted from using a certain filtering method alone to process the real-time data measured on aerocraft, a new method combined filter and Savitzky-Golay smoothing filter is proposed to process the real-time measuring data, which could classify and segment the measured data of aerocraft trajectory according to its priority and time domain. It could provide useful principle and control procedure of combined filters on different conditions to improve the filter efficiency, and the combined filtering results meet the needs of aerocraft real-time data processing accuracy in different measured sections.

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.

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