Research On Fault Diagnosis Method Of Pumping Unit Based On Video Recognition

With the rapidly development of artificial intelligence technology,video recognition technology based on deep learning has been consistently applied to video analysis tasks in fields,including video surveillance,video recommendation and automatic driving,but it has not been studied in the oilfield field deeply.The beam pumping machine is the main equipment for oil extraction in China’s oil fields,and due to its year-round condition in remote areas with harsh environment,it is prone to problems such as donkey head stopping pumping and suspension rope device failure.If no timely measures are taken,not only will the oil production be affected,but also safety accidents may occur in serious cases,so it is important to monitor the equipment failure on the pumping machine wells in real time.The traditional fault diagnosis method has low efficiency and poor real-time effect,which cannot meet the production and development of modern digital oilfield under the big data environment,so it is important to study the efficient and advanced fault diagnosis method to realize the real-time monitoring of pumping equipment operation status,which has important application value and research significance in the oilfield field.To solve the above problems,video recognition technology based on deep learning is studied in depth,and a video recognition-based fault diagnosis method for oil pumping machines is proposed.The 3D convolutional neural network model is selected as the research method,and the performance of the model is analyzed and optimized.Firstly,the video-based pumping machine fault diagnosis process is studied in depth and its difficulties are analyzed,and the focus of the study is clearly on the training of the deep learning network model under small sample data sets and the improvement and optimization of the 3D convolutional neural network;secondly,based on the video collection data under an oilfield well field,the data expansion technique is used to increase the sample size for the small sample problem,and the fine-tuning strategy is used on the basis of migration learning Then,we use different fine-tuning strategies to select the base network model for 3D-Res Net50,and then,we design an improved model for deep learning network model with complex structure,large number of parameters and long training time.Then,to address the problems of complex structure,large number of parameters and long training time of the deep learning network model,to design an improved lightweight network model for fault diagnosis.The dense cavity convolution module DACM is used to replace the shallow convolution in the base model firstly,and then the deep separable convolution is used to improve the structure of the base residual unit,and the channel-bychannel convolution and point-by-point convolution are combined to achieve the optimization of the network model parameters,and the fine-tuning strategy is combined with the experimental analysis;finally,according to the oilfield.Finally,the trained network model is combined with the video hidden violation intelligent identification system to achieve real-time monitoring of the operation status of the equipment on the pumping machine wells according to the oilfield requirements.The research results show that the pumping unit fault diagnosis algorithm model based on video recognition can accurately identify the fault types of equipment on the pumping unit wells and make alarm alerts,and can operate stably in the front-end edge equipment,which has high application value in practical scenarios.