Journal article
Hierarchical Fault Diagnosis of Rod Pumping System Based on Fault Distinguishing
Xi nan shi you da xue xue bao. Zi ran ke xue ban, v 36(5), pp 169-175
01 Jan 2014
Abstract
There is higher fault probability under the bad work conditions of rod pumping system. Because of the large amount of sample and the long-time steady status, this paper proposes hierarchical fault diagnosis method. Fault distinguishing and fault identification are two phases in this method. In fault distinguishing, fault phase and normal phase are classified according to statistical rule of normal sample in dynamometer card. Combining statistical theory and search-tree, fault identification is processed in fault sample. Using this method, mechanical model of rod pumping system is not to be established and training set of fault sample is not necessary. Fault distinguishing process includes: fault taring phase and fault distinguishing phase. In fault training phase, stochastic distribution rule and parameters of samples are determined by [chi] super(2) goodness-of-fit test after abnormal samples are rejected, then normal area and fault area is calculated. In fault distinguishing phase, classification of fault is estimated according to whether or not feature value of test sample is in fault area. Examples indicate fault samples can be selected from large number of samples in distinguish fault and correct rate of fault distinguishing is high. Statistical rule of training samples can reflect the actual production of oil well.
Metrics
3 Record Views
2 citations in Scopus
Details
- Title
- Hierarchical Fault Diagnosis of Rod Pumping System Based on Fault Distinguishing
- Creators
- Hua LiangHualou Liang - School of Biomedical Engineering, Science, and Health Systems (1997-)
- Publication Details
- Xi nan shi you da xue xue bao. Zi ran ke xue ban, v 36(5), pp 169-175
- Resource Type
- Journal article
- Language
- Chinese
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Scopus ID
- 2-s2.0-84908290181
- Other Identifier
- 991019320615004721