Design of an Ultra-low Power Device for Aircraft Structural Health Monitoring

2013 ◽  
Author(s):  
Alessandro Perelli ◽  
Carlo Caione ◽  
Luca De Marchi ◽  
Davide Brunelli ◽  
Alessandro Marzani ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Low Power ◽  
Health Monitoring ◽  
Power Device ◽  
Ultra Low Power ◽  
Structural Health

Implementation of Extreme Low Power Micro-Controller for a Wireless Structural Health Monitoring (SHM) System

2012 ◽  
Vol 225 ◽  
pp. 344-349 ◽  
Author(s):  
Mohamad Zikri Zainol ◽  
Faizal Mustapha ◽  
Mohamed Thariq Hameed Sultan ◽  
N. Yidris
Keyword(s):  
Structural Health Monitoring ◽  
Low Power ◽  
Health Monitoring ◽  
Green Energy ◽  
Wireless Sensor ◽  
Structural Defects ◽  
Sensor Technology ◽  
Energy Sources ◽  
Automatic Data ◽  
Structural Health

This paper presents the newly improved design of wireless sensor technology for Structural Health Monitoring (SHM) system or continuous monitoring for Non Destructive Testing (NDT). Numerous researches have indulged in designing wireless sensor networks where the reliability and the capability to do rapid assessment on the aeronautical, mechanical and civil structure are concerned. A lot of challenges associated with the design have been discussed including on power consumption by the device with regards the operation nature over period of times vs. energy sources. This research project explores the implementation of Nano Watt XLPTM technology microcontroller from Microchip and applicable smart PZT sensors or the newly refined technique in (NDT) that utilise ultrasonic guided waveform response to detect structural defects. The developed SHM system provide low power wireless nodes to perform automatic data collection and analysis with possibilities to integrate with green energy sources more effectively thus reducing the cost of maintenance and increase the reliability of the system.

scholarly journals SSVM: An Ultra-Low-Power Strain Sensing and Visualization Module for Long-Term Structural Health Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2211
Author(s):  
Suleman Khan ◽  
Jongbin Won ◽  
Junsik Shin ◽  
Junyoung Park ◽  
Jong-Woong Park ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Low Power ◽  
Data Management ◽  
Power Management ◽  
Health Monitoring ◽  
Strain Measurement ◽  
Strain Response ◽  
Strain Sensing ◽  
Ultra Low Power

Structural health monitoring (SHM) is crucial for quantitative behavioral analysis of structural members such as fatigue, buckling, and crack propagation identification. However, formerly developed approaches cannot be implemented effectively for long-term infrastructure monitoring, owing to power inefficiency and data management challenges. This study presents the development of a high-fidelity and ultra-low-power strain sensing and visualization module (SSVM), along with an effective data management technique. Deployment of 24-bit resolution analog to a digital converter and precise half-bridge circuit for strain sensing are two significant factors for efficient strain measurement and power management circuit incorporating a low-power microcontroller unit (MCU), and electronic-paper display (EPD) enabled long-term operation. A prototype for SSVM was developed that performs strain sensing and encodes the strain response in a QR code for visualization on the EPD. For efficient power management, SSVM only activated when the trigger-signal was generated and stayed in power-saving mode consuming 18 mA and 337.9 μA, respectively. The trigger-signal was designed to be generated either periodically by a timer or intentionally by a push-button. A smartphone application and cloud database were developed for efficient data acquisition and management. A lab-scale experiment was carried out to validate the proposed system with a reference strain sensing system. A cantilever beam was deflected by increasing load at its free end, and the resultant strain response of SSVM was compared with the reference. The proposed system was successfully validated to use for long-term static strain measurement.

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