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Abstract and Applied Analysis
Volume 2014, Article ID 902304, 9 pages
Research Article

Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique

1College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
2College of Information and Telecommunication, Harbin Engineering University, Harbin, Heilongjiang 150001, China
3School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China

Received 17 April 2014; Revised 4 June 2014; Accepted 4 June 2014; Published 23 June 2014

Academic Editor: Hamid Reza Karimi

Copyright © 2014 Zhilin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Metal magnetic memory (MMM) technique is an effective method to achieve the detection of stress concentration (SC) zone for oil well casing. It can provide an early diagnosis of microdamages for preventive protection. MMM is a natural space domain signal which is weak and vulnerable to noise interference. So, it is difficult to achieve effective feature extraction of MMM signal especially under the hostile subsurface environment of high temperature, high pressure, high humidity, and multiple interfering sources. In this paper, a method of median filter preprocessing based on data preprocessing technique is proposed to eliminate the outliers point of MMM. And, based on wavelet transform (WT), the adaptive wavelet denoising method and data smoothing arithmetic are applied in testing the system of MMM. By using data preprocessing technique, the data are reserved and the noises of the signal are reduced. Therefore, the correct localization of SC zone can be achieved. In the meantime, characteristic parameters in new diagnostic approach are put forward to ensure the reliable determination of casing danger level through least squares support vector machine (LS-SVM) and nonlinear quantitative mapping relationship. The effectiveness and feasibility of this method are verified through experiments.