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Corrosion in reinforced concrete panels: wireless monitoring and wavelet-based analysis.

Qiao G, Sun G, Hong Y, Liu T, Guan X - Sensors (Basel) (2014)

Bottom Line: We design a novel pitting corrosion-detecting mote and a communication protocol such that the monitoring platform can sample the electrochemical emission signals of corrosion process with a configured period, and send these signals to a central computer for the analysis.We also conducted test-bed experiments based on RC panels.The results verify the feasibility and efficiency of the proposed WSN system and algorithms.

View Article: PubMed Central - PubMed

Affiliation: Key Lab of Structures Dynamic Behavior and Control, Harbin Institute of Technology, Ministry of Education, Harbin 150090, China. qgf_forever@hit.edu.cn.

ABSTRACT
To realize the efficient data capture and accurate analysis of pitting corrosion of the reinforced concrete (RC) structures, we first design and implement a wireless sensor and network (WSN) to monitor the pitting corrosion of RC panels, and then, we propose a wavelet-based algorithm to analyze the corrosion state with the corrosion data collected by the wireless platform. We design a novel pitting corrosion-detecting mote and a communication protocol such that the monitoring platform can sample the electrochemical emission signals of corrosion process with a configured period, and send these signals to a central computer for the analysis. The proposed algorithm, based on the wavelet domain analysis, returns the energy distribution of the electrochemical emission data, from which close observation and understanding can be further achieved. We also conducted test-bed experiments based on RC panels. The results verify the feasibility and efficiency of the proposed WSN system and algorithms.

No MeSH data available.


Energy distribution plots of the electrochemical emission signals in wavelet domain. (a) and (b) are the energy distribution plots of the electrochemical potential emission and electrochemical current emission, respectively.
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f8-sensors-14-03395: Energy distribution plots of the electrochemical emission signals in wavelet domain. (a) and (b) are the energy distribution plots of the electrochemical potential emission and electrochemical current emission, respectively.

Mentions: To idey the pitting corrosion qualitatively, the energy distribution algorithm of the electrochemical emission signal in wavelet domain described in Section 3 of this paper is applied to analyze the potential and current emission data in Figures 6 and 7. The orthogonal Sym4 wavelet is utilized to decompose the electrochemical emission data in depth of 8-level, and the approximation signal in 8-level is removed as the harmful trend. Then, the decomposed signal in each level is reconstructed and the energy distribution plots are obtained based on the Equation (3). Figure 8a,b illustrates the energy ratio on each level of the potential and current emission data, respectively. According to Figure 8a, the energy of potential emission data collected by SN0 at passive sate accumulates on the level 1–3. The frequency of these parts between 1 and 2–3 Hz is much higher than that of other parts. However, the energy transfers to level 5–8 as the pitting corrosion appears. The energy condensed on these levels is no less than 80% of the total energy. The frequency of the levels from 5 to 8 is no more than 2–5 Hz. The condition of electrochemical current emission in Figure 8b is similar with that of the electrochemical potential emission in Figure 8a. According to the change of the energy distribution on the different crystal, the pitting corrosion is verified. The energy which is distributed on the level 5–8 is the intrinsic characteristics of pitting corrosion of the reinforcing steel in concrete. The energy distribution plot of the electrochemical emission signal can be applied as the benchmark to qualitatively idey the presence of the pitting corrosion. Actually, the in-situ, real-time and on-line corrosion monitoring information of the electrochemical emission spectrum could be applied to drive the 3-D cellular automata to quantitatively predict the development of the corrosion pit. Considering the aim of this paper, we do not discuss this point in detail here.


Corrosion in reinforced concrete panels: wireless monitoring and wavelet-based analysis.

Qiao G, Sun G, Hong Y, Liu T, Guan X - Sensors (Basel) (2014)

Energy distribution plots of the electrochemical emission signals in wavelet domain. (a) and (b) are the energy distribution plots of the electrochemical potential emission and electrochemical current emission, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958240&req=5

f8-sensors-14-03395: Energy distribution plots of the electrochemical emission signals in wavelet domain. (a) and (b) are the energy distribution plots of the electrochemical potential emission and electrochemical current emission, respectively.
Mentions: To idey the pitting corrosion qualitatively, the energy distribution algorithm of the electrochemical emission signal in wavelet domain described in Section 3 of this paper is applied to analyze the potential and current emission data in Figures 6 and 7. The orthogonal Sym4 wavelet is utilized to decompose the electrochemical emission data in depth of 8-level, and the approximation signal in 8-level is removed as the harmful trend. Then, the decomposed signal in each level is reconstructed and the energy distribution plots are obtained based on the Equation (3). Figure 8a,b illustrates the energy ratio on each level of the potential and current emission data, respectively. According to Figure 8a, the energy of potential emission data collected by SN0 at passive sate accumulates on the level 1–3. The frequency of these parts between 1 and 2–3 Hz is much higher than that of other parts. However, the energy transfers to level 5–8 as the pitting corrosion appears. The energy condensed on these levels is no less than 80% of the total energy. The frequency of the levels from 5 to 8 is no more than 2–5 Hz. The condition of electrochemical current emission in Figure 8b is similar with that of the electrochemical potential emission in Figure 8a. According to the change of the energy distribution on the different crystal, the pitting corrosion is verified. The energy which is distributed on the level 5–8 is the intrinsic characteristics of pitting corrosion of the reinforcing steel in concrete. The energy distribution plot of the electrochemical emission signal can be applied as the benchmark to qualitatively idey the presence of the pitting corrosion. Actually, the in-situ, real-time and on-line corrosion monitoring information of the electrochemical emission spectrum could be applied to drive the 3-D cellular automata to quantitatively predict the development of the corrosion pit. Considering the aim of this paper, we do not discuss this point in detail here.

Bottom Line: We design a novel pitting corrosion-detecting mote and a communication protocol such that the monitoring platform can sample the electrochemical emission signals of corrosion process with a configured period, and send these signals to a central computer for the analysis.We also conducted test-bed experiments based on RC panels.The results verify the feasibility and efficiency of the proposed WSN system and algorithms.

View Article: PubMed Central - PubMed

Affiliation: Key Lab of Structures Dynamic Behavior and Control, Harbin Institute of Technology, Ministry of Education, Harbin 150090, China. qgf_forever@hit.edu.cn.

ABSTRACT
To realize the efficient data capture and accurate analysis of pitting corrosion of the reinforced concrete (RC) structures, we first design and implement a wireless sensor and network (WSN) to monitor the pitting corrosion of RC panels, and then, we propose a wavelet-based algorithm to analyze the corrosion state with the corrosion data collected by the wireless platform. We design a novel pitting corrosion-detecting mote and a communication protocol such that the monitoring platform can sample the electrochemical emission signals of corrosion process with a configured period, and send these signals to a central computer for the analysis. The proposed algorithm, based on the wavelet domain analysis, returns the energy distribution of the electrochemical emission data, from which close observation and understanding can be further achieved. We also conducted test-bed experiments based on RC panels. The results verify the feasibility and efficiency of the proposed WSN system and algorithms.

No MeSH data available.