A new diagnostic method for identifying working conditions of submersible reciprocating pumping systems
| Autor: | Xinmin Wang, Hongmei Bian, Weigui Qi, Yongming Zhang, Yu Deliang |
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| Rok vydání: | 2013 |
| Předmět: |
Engineering
Learning vector quantization Diagnostic methods Failure diagnosis business.industry Energy Engineering and Power Technology Geology Control engineering Linear motor Geotechnical Engineering and Engineering Geology Working condition Support vector machine Reciprocating motion Geophysics Fuel Technology Geochemistry and Petrology Economic Geology business Classifier (UML) Marine engineering |
| Zdroj: | Petroleum Science. 10:81-90 |
| ISSN: | 1995-8226 1672-5107 |
| Popis: | The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine (SVM) method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization (LVQ) classifier. |
| Databáze: | OpenAIRE |
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