Study On Signal Processing Of Acoustic Emission Testing For Tank Bottom

To protect the security of oil supply, a number of national strategic oil reserve base is constructing, and thus the number of large-scale vertical metal tanks is growthing rapidly. As special equipment, the safety of storage tanks is very important. Once the leakage accident of a tank happens, the consequences would be very serious. Therefore, enhancing monitoring the tanks’healthy is an important work after the national strategic oil reserve base being constructed. It’s is meaningful to study on advanced tanks online testing technology, for maintaining tanks’stable operation, reducing or avoiding environmental pollution, and reducing the economic loss due to blindness repairing.In this paper, the acoustic emission (AE) testing for storage tank is studied, which is mainly focusing on the AE signals processing. The study includes the following aspects:1. The fusion of the AE signals is studied, and a new AE signals fusion method, which is based on cluster analysis, is proposed. The new fusion method could raise the accuracy of extracting AE events and reduce signal noise effectively.2. According to the requirements of tank bottom AE source positioning, the triangulate positioning algorithm and the overdetermined positioning algorithm are studied. Different solution methods are developed for the two different algorithms. In order to study the two algorithms’positioning performance under ideal and non-ideal conditions, Monte Carlo method is used. The result would be very meaningful for analyzing the AE sources’location of tank bottom.3. In order to identificate and assess the corrosion region of tank bottom from AE testing data, wavelet cluster algorithm is developed. And the AE source distribution information entropy is proposed to assess the identification effect. Through analysis of the real test results, the range of parameter settings in the wavelet cluster algorithm is optimized to raise the identification efficiency.4. 25 oil tanks located in 11 cities are tested by AE testing method. The impacts of tank type on test results are analyzed. The methods of avoiding noise and interference signals during the testing process are summarized, which would be meaningful for grading the further AE testing of tank bottom.5. Based on the study of AE signal processing algorithms, a tank bottom AE data analysis software is developed. The software is modular, scalable and multifunctional, such as AE signal waveform analysis, AE signal characteristic extraction and analysis, AE source location and tank bottom corrosion assessment, etc.