Online Detection and Evaluation of Tank Bottom Corrosion Based on Acoustic Emission

2018 ◽  
pp. 1284-1291 ◽  
Author(s):  
Ke Gong ◽  
Jiashun Hu
Keyword(s):  
Acoustic Emission ◽  
Online Detection ◽  
Tank Bottom

Acoustic Emission Based Tank Bottom Floor Corrosion Detection

2013 ◽  
Vol 7 (2) ◽  
pp. 205-210 ◽  
Author(s):  
Tao Zhang ◽  
◽  
Hao Feng ◽  
Zhou-Mo Zeng
Keyword(s):  
Acoustic Emission ◽  
Optical Fiber ◽  
Storage Tanks ◽  
Fiber Sensors ◽  
Testing Method ◽  
Actual Application ◽  
Oil Storage ◽  
Crucial Component ◽  
Tank Bottom ◽  
Nondestructive Testing Method

The tank bottom floor is a crucial component of large oil storage tanks, and its status has a pivotal impact on the integrity of the entire tank. Acoustic Emission (AE) monitoring is an advanced, in-service, nondestructive testing method internationally recognized to be capable of assessing the tank bottom floor without the necessity of prior tank cleaning. In this paper, the principle of Time Difference of Arrival (TDOA) is illustrated, and the efficiency of piezoelectric AE sensors is verified through a field experiment. The authors then investigate Mach-Zehnder interferometerbased AE sensors with a view to using optical fiber sensors as a substitute for acoustic emission detecting. The results of the experiment indicate that optical fiber AE sensors based on the Mach-Zehnder interferometer can be used as transducers sensitive to acoustic events, so they can serve as indicators of the imminent failure of a structure. In addition, some suggestions are put forward regarding forthcoming actual application.

Identify on Acoustic Emission Signals of Tank Bottom Corrosion Using Hidden Markov Model

2012 ◽  
Vol 239-240 ◽  
pp. 42-46
Author(s):  
Yang Yu ◽  
Ming Yu Zhang
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Emission Signal ◽  
Model Library ◽  
Oil Storage ◽  
Oil Storage Tank ◽  
Tank Bottom ◽  
Output Probability

Aiming at the acoustic emission signals of oil storage tank bottom injured, hidden Markov algorithm is proposed to identify the tank bottom corrosion signal. Typical corrosion acoustic emission signal is divided into transient acoustic signal, continuous acoustic emission signal and mixed acoustic emission.Baum-Welch algorithm is used to train these typical corrosion acoustic emission signals model, then establish HMM model library. The forward-backward algorithm is used to compute each acoustic emission model’s output probability. The simulation experiments shows that the hidden Markov algorithm can correctly identified the acoustic emission signals.

A study on acoustic emission technology for tank bottom corrosion inspection

2010 ◽  
Vol 57 (6) ◽  
pp. 275-279 ◽  
Author(s):  
Du Gang ◽  
Jin Shijiu ◽  
Zhang Congying ◽  
Wang Weikui
Keyword(s):  
Acoustic Emission ◽  
Tank Bottom ◽  
Acoustic Emission Technology

The Tank Bottom Safety Detection Based on Acoustic Emission Technology

2013 ◽  
Vol 561 ◽  
pp. 667-671
Author(s):  
Yi Hu Huang ◽  
Ji Xiang Ma ◽  
Zhong Hong Li ◽  
Yin Ping Zhang ◽  
Xiao Dong Han
Keyword(s):  
Acoustic Emission ◽  
Detection Method ◽  
Measured Data ◽  
Actual Situation ◽  
Emission Testing ◽  
Acoustic Emission Testing ◽  
Tank Bottom ◽  
Emission Detection ◽  
Ultrasonic Thickness ◽  
Acoustic Emission Technology

We conduct acoustic emission online detection on the bottom of atmospheric tank based on the principle of acoustic emission testing. Based the actual situation, the result of ultrasonic thickness opening inspection of tank bottom and the results of acoustic emission measured data are compared and analyzed to verify the feasibility and reliability of acoustic emission detection method.

Enhancing In-Service Tank Maintenance Through Industrial Internet of Things

2021 ◽  
pp. 244-270
Author(s):  
Moses Gwaindepi ◽  
Tawanda Mushiri
Keyword(s):  
Acoustic Emission ◽  
Storage Tanks ◽  
Inspection Method ◽  
Technological Gap ◽  
Industrial Internet ◽  
Data Compilation ◽  
Fuel Storage ◽  
Tank Bottom ◽  
And Control ◽  
Ultrasonic Thickness

In the area of tank inspections across the industry, robots were introduced to replace human inspectors in selected operations. The technological gap in adoption of similar technologies by Zimbabwe's bulk fuel storage tanks operators motivated this research. The industry's current NDT practices were investigated, costs and inconveniences were identified, and improvements were explored. Operators of bulk fuel facilities and companies providing tank inspection services were engaged to establish the reasons for the gaps in technological assimilation. Emerging global technologies that enable in-service inspections were identified and their applicability to Zimbabwe's bulk fuel facilities was investigated. A combination of crawler based ultrasonic thickness tests for tank shells, and acoustic emission in-service tank bottom testing was observed to be the most convenient and relevant in-service tank inspection method for Zimbabwe's bulk fuel storage tanks industry. Internet-based remote connectivity and control was considered for data compilation, analysis, storage, and reporting.

Study on Corrosion Acoustic Emission Separation Based on Blind Source Separation

2012 ◽  
Vol 503-504 ◽  
pp. 1597-1600
Author(s):  
Yang Yu ◽  
Xiang Zhou
Keyword(s):  
Acoustic Emission ◽  
Blind Source Separation ◽  
Source Separation ◽  
New Method ◽  
Entropy Maximization ◽  
Tank Bottom

When corrosion signals of tank bottom is detected by online method, it is essential to identify corrosion signals of different corrosion pots. It is a new method based on blind source separation. Blind source separation has produced many arithetics, among which entropy maximization is more mature. The aim of this paper is to separate corrosion signals by using entropy maximization arithmetic. Furthermore, the separation of linear mixed acoustic emission signals is achieved through simulation. The results indicate that blind source separation is an effective method for the separation of corrosion signals.

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