Fault diagnosis method of submersible screw pump based on random forest.

ABSTRACT: The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used

SUBMITTER: Jiang M 

PROVIDER: S-EPMC7668568 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature