The operating environment of underground oil mining equipment is complicated, which is greatly affected by high temperature, high pressure, corrosion and other factors. The real-time and accuracy of equipment condition monitoring are directly related to the mining efficiency and safety. The traditional condition monitoring method based on rule diagnosis and simple signal processing is difficult to deal with the multi-modal, high-dimensional and nonlinear characteristic data during the operation of equipment. The development of deep learning technology combined with intelligent device condition monitoring of sensor network has become a research hotspot. Based on the operation characteristics of underground oil mining equipment, this paper proposes a condition monitoring framework based on deep learning, which realizes the acquisition and transmission of vibration, acoustic, temperature, pressure and other multi-modal data through multi-type sensor networks. Deep learning algorithms such as convolutional neural network (CNN), long short-term memory network (LSTM) and Autoencoder are used for feature extraction, anomaly detection and fault prediction. This paper analyzes the key technologies of data acquisition, transmission, pre-processing and deep learning model training in the monitoring framework, and verifies the efficiency and accuracy of fault diagnosis and state prediction through experiments. The experimental results show that compared with traditional methods, the deep learning method has higher accuracy and robustness under complex conditions. The research in this paper not only provides theoretical and technical support for the intelligent monitoring of underground oil extraction equipment, but also lays a foundation for the construction of intelligent oil fields.

Yuxin Wang. Application and Practice of Sensor Network Based on Deep Learning in Condition Monitoring of Underground Oil Production Equipment. International Journal of Frontiers in Engineering Technology (2024), Vol. 6, Issue 6: 53-58. https://doi.org/10.25236/IJFET.2024.060608.

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