English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Conventional fault diagnosis methods of sucker rod pump (SRP) mainly focus the operating status of oil well by identifying the dynamometer cards (DCs), which are limited by the sensor maintenance and calibration, battery replacement and safety hazards for staff. Motor power, as the most basic parameter providing the energy source for the oil well, is directly related to the real-time operation state of oil well. Therefore, a novel deep and broad learning system (DBLS) based on motor power data for fault diagnosis of sucker rod pump is proposed in this paper. Considering the key parameters such as mechanical wear and balance weight, the motor power data are labeled by the DCs with typical working conditions. Furthermore, CNN-based feature extractor is designed to make up for the lack of expert experience in motor power, which is obtained by merging the output of the CNNs with the manual features extracted based on mechanical analysis. And then the broad learning system is employed as the classifier to solve the problem of real-time update of system structure. Finally, a dataset containing six different working states collected from the oilfield by a self-developed device is employed to verify the proposed method experimentally and compared with other methods.

Fault Diagnosis of Sucker Rod Pump Based on Deep-Broad Learning Using Motor Data