Accurate identification and early-warning of faults of fracturing equipments based on infrared thermal imaging and convolutional neural network

Key words:  infrared thermal imaging  convolutional neural network  fracturing pump  condition monitoring  fault identification

Abstract:

During the large-scale shale gas fracturing operation, the safety and reliability of the fracturing equipment represented by the fracturing pump is directly related to the smooth progress of the overall fracturing operation. Considering the impact of complex working conditions and operating environment on vibration analysis and the inconvenience of installing vibration sensors inside the equipment, infrared thermal imaging technology is introduced to monitor the operating status. Due to the thick outer shell of the shale gas cracking equipment and the cooling effect of the internal liquid, the temperature characteristics of common fault areas such as the pump head are not obvious. In view of this problem, convolutional neural network (CNN) was introduced to realize the intelligent and unmanned precision identification and early warning of fracturing equipment faults. By simulating on-site fracturing conditions and conducting laboratory tests, the analysis results show that the fracturing equipment fault identification method proposed in this paper can achieve an accuracy rate of 94.8%, and advance the warning time by 10 s, which is of great significance to reduce the severity of the accident consequences.