机械科学与技术2023，Vol.42Issue(12)：1959-1966,8.DOI:10.13433/j.cnki.1003-8728.20220155
改进连续隐马尔可夫模型的有杆抽油故障诊断
Fault Diagnosis of Sucker Rod Pumping System Using Improved Continuous Hidden Markov Model
摘要
Abstract
When using artificial intelligence to diagnose the fault of sucker rod pumping system,dynamometer cards describing the working condition become the object of machine learning,and the main steps are to extract the feature of dynamometer card and build the diagnosis model.The existing geometric features based on valve working position cannot directly reflect the area of dynamometer card,so a group of improved training features are proposed,and the continuous hidden Markov model(CHMM)is used to establish the diagnosis model.In order to make the initialization of parameters more reliable,the K-means clustering algorithm associated with the Gaussian mixture model is used.The diagnosis method proposed in this paper is used to test the dynamometer card set of real oil wells,the results show that this method is effective,and the improved training features and modeling methods can enhance the accuracy of fault diagnosis关键词
示功图/特征提取/连续隐马尔可夫模型/K-means聚类Key words
dynamometer card/feature extraction/continuous hidden Markov model/K-means clustering分类
能源科技引用本文复制引用
王东宇,刘宏昭,任慧..改进连续隐马尔可夫模型的有杆抽油故障诊断[J].机械科学与技术,2023,42(12):1959-1966,8.基金项目
国家自然科学基金项目(51275404)与陕西省"13115"重大科技专项(2009ZDKGG-33) （51275404）
