scholarly journals A custom acoustic emission monitoring system for harsh environments: application to freezing-induced damage in alpine rock-walls

Author(s):  
L. Girard ◽  
J. Beutel ◽  
S. Gruber ◽  
J. Hunziker ◽  
R. Lim ◽  
...  

Abstract. We present a custom acoustic emission (AE) monitoring system designed to perform long-term measurements on high-alpine rock-walls. AE monitoring is a common technique for characterizing damage evolution in solid materials. The system is based on a two-channel AE sensor node (AE-node) integrated into a Wireless Sensor Network (WSN) customized for operation in harsh environments. This wireless architecture offers flexibility in the deployment of AE-nodes at any position of the rock-wall that needs to be monitored, within a range of a few hundred meters from a core station connected to the internet. The system achieves near real-time data delivery and allows the user to remotely control the AE detection threshold. In order to protect AE sensors and capture acoustic signals from specific depths of the rock-wall, a special casing was developed. The monitoring system is completed by two probes that measure rock temperature and liquid water content, both probes being also integrated into the WSN. We report a first deployment of the monitoring system on a rock-wall at Jungfraujoch, 3500 m a.s.l., Switzerland. While this first deployment of the monitoring system aims to support fundamental research on processes that damage rock under cold climate, the system could serve a number of other applications, including rock-fall hazard surveillance or structural monitoring of concrete structures.

2012 ◽  
Vol 1 (2) ◽  
pp. 155-167 ◽  
Author(s):  
L. Girard ◽  
J. Beutel ◽  
S. Gruber ◽  
J. Hunziker ◽  
R. Lim ◽  
...  

Abstract. We present a custom acoustic emission (AE) monitoring system designed to perform long-term measurements on high-alpine rock walls. AE monitoring is a common technique for characterizing damage evolution in solid materials. The system is based on a two-channel AE sensor node (AE-node) integrated into a wireless sensor network (WSN) customized for operation in harsh environments. This wireless architecture offers flexibility in the deployment of AE-nodes at any position of the rock wall that needs to be monitored, within a range of a few hundred meters from a core station connected to the internet. The system achieves near real-time data delivery and allows the user to remotely control the AE detection threshold. In order to protect AE sensors and capture acoustic signals from specific depths of the rock wall, a special casing was developed. The monitoring system is completed by two probes that measure rock temperature and liquid water content, both probes being also integrated into the WSN. We report a first deployment of the monitoring system on a rock wall at Jungfraujoch, 3500 m a.s.l., Switzerland. While this first deployment of the monitoring system aims to support fundamental research on processes that damage rock under cold climate, the system could serve a number of other applications, including rock fall hazard surveillance or structural monitoring of concrete structures.


2021 ◽  
Vol 11 (15) ◽  
pp. 7045
Author(s):  
Ming-Chyuan Lu ◽  
Shean-Juinn Chiou ◽  
Bo-Si Kuo ◽  
Ming-Zong Chen

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.


2012 ◽  
Vol 490-495 ◽  
pp. 2624-2627
Author(s):  
Hua Wen Zheng ◽  
Liu Chang ◽  
Chuan Hui Wu ◽  
Yu Liang

Design of a monitoring system based on virtual instrument technology for a simple bridge. This paper discusses the bridge monitoring system structure, software and hardware. The system can realize functions such as data acquisition, real-time data display, online analysis, offline analysis, data management and so on. Therefore, the monitoring system can be applied to a variety of bridges. The presented monitoring system has an important guidance meaning and practical value for bridge monitoring.


2011 ◽  
Vol 65 ◽  
pp. 295-298 ◽  
Author(s):  
Fan Yang ◽  
Cai Li Zhang

Considering the insufficient ability of data processing existed in configuration software, a scheme integrated both advantages of advanced programming language and configuration software is provided. In this scheme real-time data acquisition and complex processing are achieved by advanced programming language, the human-computer interface and other functions of the monitoring system are achieved by configuration software. Configuration software achieves the purpose of expanding data processing ability by data communications between advanced programming language and configuration software based on OLE technology. The practical application result indicates that the data processing ability of configuration software can be effectively expanded based on OLE technology, which has well stability and real-time, and can play significant performance in complex parameters and data processing related monitoring system.


2011 ◽  
Vol 291-294 ◽  
pp. 3036-3043 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Channarong Rungruang

The aim of this research is to propose and develop the in-process monitoring system of the tool wear for the carbon steel (S45C) in CNC turning process by utilizing the multi-sensor which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. The progress of the tool wear results in the larger cutting force, the higher amplitude of the acceleration signal, and the higher power spectrum densities of sound and acoustic emission signals. Hence, their signals have been integrated via the neural network with the back propagation technique to monitor the tool wear. The experimentally obtained results showed that the in-process monitoring system proposed and developed in this research can be effectively used to estimate the tool wear level with the higher accuracy and reliability.


2021 ◽  
Vol 9 (2) ◽  
pp. 27-36
Author(s):  
Sheikh Hasib Cheragee ◽  
Nazmul Hassan ◽  
Sakil Ahammed ◽  
Abu Zafor Md. Touhidul Islam

We have Developed an IoT-based real-time solar power monitoring system in this paper. It seeks an opensource IoT solution that can collect real-time data and continuously monitor the power output and environmental conditions of a photovoltaic panel.The Objective of this work is to continuously monitor the status of various parameters associated with solar systems through sensors without visiting manually, saving time and ensures efficient power output from PV panels while monitoring for faulty solar panels, weather conditionsand other such issues that affect solar effectiveness.Manually, the user must use a multimeter to determine what value of measurement of the system is appropriate for appliance consumers, which is difficult for the larger System. But the Solar Energy Monitoring system is designed to make it easier for users to use the solar system.This system is comprised of a microcontroller (Node MCU), a PV panel, sensors (INA219 Current Module, Digital Temperature Sensor, LDR), a Battery Charger Module, and a battery. The data from the PV panels and other appliances are sent to the cloud (Thingspeak) via the internet using IoT technology and a Wi-Fi module (NodeMCU). It also allows users in remote areas to monitor the parameters of the solar power plant using connected devices. The user can view the current, previous, and average parameters of the solar PV system, such as voltage, current, temperature, and light intensity using a Graphical User Interface. This will facilitate fault detection and maintenance of the solar power plant easier and saves time.


2013 ◽  
Vol 845 ◽  
pp. 283-286 ◽  
Author(s):  
Malik Abdul Razzaq Al Saedi ◽  
Mohd Muhridza Yaacob

There is a high risk of insulation system dielectric instability when partial discharge (PD) occurs. Therefore, measurement and monitoring of PD is an important preventive tool to safeguard high-voltage equipment from wanton damage. PD can be detected using optical method to increase the detection threshold and to improve the performance of on-line measurement of PD in noise environment. The PD emitted energy as acoustic emission. We can use this emitted energy to detect PD signal. The best method to detect PD in power transformer is by using acoustic emission. Optical sensor has some advantages such as; high sensitivity, more accuracy small size. Furthermore, in on-site measurements and laboratory experiments, it isoptical methodthat gives very moderate signal attenuations. This paper reviews the available PD detection methods (involving high voltage equipment) such as; acoustic detection and optical detection. The advantages and disadvantages of each method have been explored and compared. The review suggests that optical detection techniques provide many advantages from the consideration of accuracy and suitability for the applications when compared to other techniques.


Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6422
Author(s):  
Grega Morano ◽  
Andrej Hrovat ◽  
Matevž Vučnik ◽  
Janez Puhan ◽  
Gordana Gardašević ◽  
...  

The LOG-a-TEC testbed is a combined outdoor and indoor heterogeneous wireless testbed for experimentation with sensor networks and machine-type communications, which is included within the Fed4FIRE+ federation. It supports continuous deployment principles; however, it is missing an option to monitor and control the experiment in real-time, which is required for experiment execution under comparable conditions. The paper describes the implementation of the experiment control and monitoring system (EC and MS) as the upgrade of the LOG-a-TEC testbed. EC and MS is implemented within existing infrastructure management and built systems as a new service. The EC and MS is accessible as a new tab in sensor management system portal. It supports several commands, including start, stop and restart application, exit the experiment, flash or reset the target device, and displays the real-time status of the experiment application. When nodes apply Contiki-NG as their operating system, the Contiki-NG shell tool is accessible with the help of the newly developed tool, giving further experiment execution control capabilities to the user. By using the ZeroMQ concurrency framework as a message exchange system, information can be asynchronously sent to one or many devices at the same time, providing a real-time data exchange mechanism. The proposed upgrade does not disrupt any continuous deployment functionality and enables remote control and monitoring of the experiment. To evaluate the EC and MS functionality, two experiments were conducted: the first demonstrated the Bluetooth Low Energy (BLE) localization, while the second analysed interference avoidance in the 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) wireless technology for the industrial Internet of Things (IIoT).


1999 ◽  
Vol 89 (4) ◽  
pp. 989-1003 ◽  
Author(s):  
István Bondár ◽  
Robert G. North ◽  
Gregory Beall

Abstract The prototype International Data Center (PIDC) in Arlington, Virginia, has been developing and testing software and procedures for use in the verification of the Comprehensive Test Ban Treaty. After three years of operation with a global network of array and three-component stations, it has been possible to characterize various systematic biases of those stations that are designated in the Treaty as part of the International Monitoring System (IMS). These biases include deviations of azimuth and slowness measurements from predicted values, caused largely by lateral heterogeneity. For events recorded by few stations, azimuth and slowness are used in addition to arrival-time data for location by the PIDC. Corrections to teleseismic azimuth and slowness observations have been empirically determined for most IMS stations providing data to the PIDC. Application of these corrections is shown to improve signal association and event location. At some stations an overall systematic bias can be ascribed to local crustal structure or to unreported instrumental problems. The corrections have been applied in routine operation of the PIDC since February 1998.


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