Data mining in the diagnostics of oil extraction equipment

We present the key steps in the dynamogram classification algorithm development. These are data processing, procedures of generation and selection of features, constructing of a neural network classifier and estimation of its work quality. To estimate the possibility to single out complex defects (subclasses), we analyzed the structure of the input pattern sample with the aid of clusterization algorithms. Possibilities of the diagnostic algorithm for submersible equipment on the basis of the pretreated dynamometer cards recognition that characterize the current object state were explored. The key steps of the algorithm implementation for the dynamometer card classification are: feature generation, feature selection and classifiers building. The classifiers based on single neural networks and hierarchic neural network committees with different architectures have demonstrated the accuracy of recognition of the equipment condition classes at the level of 80-94%. The reliability of recognition for the equipment condition classes is at the level of 78-98% for the data that were not included in the training set.