Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique
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 interfe...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/902304 |
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| author | Zhilin Liu Lutao Liu Jun Zhang |
| author_facet | Zhilin Liu Lutao Liu Jun Zhang |
| author_sort | Zhilin Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5a44b74d6a3443ff8bc0d775342be9c9 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-5a44b74d6a3443ff8bc0d775342be9c92025-02-03T01:30:11ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/902304902304Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing TechniqueZhilin Liu0Lutao Liu1Jun Zhang2College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, ChinaCollege of Information and Telecommunication, Harbin Engineering University, Harbin, Heilongjiang 150001, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, ChinaMetal 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.http://dx.doi.org/10.1155/2014/902304 |
| spellingShingle | Zhilin Liu Lutao Liu Jun Zhang Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique Abstract and Applied Analysis |
| title | Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique |
| title_full | Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique |
| title_fullStr | Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique |
| title_full_unstemmed | Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique |
| title_short | Signal Feature Extraction and Quantitative Evaluation of Metal Magnetic Memory Testing for Oil Well Casing Based on Data Preprocessing Technique |
| title_sort | signal feature extraction and quantitative evaluation of metal magnetic memory testing for oil well casing based on data preprocessing technique |
| url | http://dx.doi.org/10.1155/2014/902304 |
| work_keys_str_mv | AT zhilinliu signalfeatureextractionandquantitativeevaluationofmetalmagneticmemorytestingforoilwellcasingbasedondatapreprocessingtechnique AT lutaoliu signalfeatureextractionandquantitativeevaluationofmetalmagneticmemorytestingforoilwellcasingbasedondatapreprocessingtechnique AT junzhang signalfeatureextractionandquantitativeevaluationofmetalmagneticmemorytestingforoilwellcasingbasedondatapreprocessingtechnique |