Cloud Diagnosis System For Power End Faults Of Fracturing Vehicles

The fracturing vehicle is the core equipment for fracturing oil layers to increase production.Its usage is increasing with the increasing demand for oil exploration in China.The fracturing vehicle operation is generally field operation,with long continuous working hours and high load for a long time.For large-displacement work,its operation-related status directly affects the health and life of mechanical equipment.The power end is a key component of the fracturing vehicle.Due to its large working load and complex working conditions,it is prone to failure when working in harsh environments such as high pressure,strong corrosiveness,and large displacement.The traditional fault diagnosis method of the power end of the fracturing vehicle requires experienced professionals to judge the fault on site,which relies heavily on manual fault detection,and the staff frequently enter the high-pressure area for inspection and maintenance,and there are problems such as personnel safety hazards.Therefore,there is an urgent need for a system that can remotely and conveniently monitor and diagnose online faults at the power end of fracturing vehicles,so that experts from all over the world can provide technical support through this system,and users can monitor the operating status of the power end of fracturing vehicles through the system.The operation portability,system stability,fault analysis efficiency and recognition accuracy of the designed system all put forward higher requirements.Aiming at the above problems,this paper has carried out the research work on the cloud diagnosis system of the power end fault of the fracturing vehicle.The main work contents are as follows:(1)Aiming at the problems that the faults of the power end of the fracturing truck require frequent maintenance by staff,the cost is high,and there are potential safety hazards for personnel,a cloud diagnosis system for the fault of the power end of the fracturing truck is designed.The system is designed based on the cloud platform and responsive principle,which makes the system conveniently compatible with multiple platforms across terminals,and is designed for human-computer interaction on the mobile terminal,making the fault diagnosis system more convenient and easy to use,with comprehensive system design functions,including fault online The whole process of monitoring and diagnosis.(2)In view of the fact that the fracturing vehicle fault diagnosis system needs to continuously transmit a large amount of data,based on the improved Web Socket protocol combined with the high-performance non-relational database Mongo DB,the system data transmission and storage are realized,so that the system can continuously and stably handle large amounts of data transmission and Access,and implement a set of codes for this system that is compatible with multiple operating systems and screen-sized devices,saving system development costs,using mobile phones to access the system makes it possible to perform fault diagnosis anytime and anywhere.After testing,the developed system It is capable of online monitoring and fault diagnosis of the power end of the fracturing vehicle.(3)The traditional fault diagnosis method packaged in this system can further improve the efficiency and accuracy of power end diagnosis.In order to optimize the accuracy and operation efficiency of system fault diagnosis,a method based on grasshopper algorithm to optimize support vector machine is proposed.Fault diagnosis method.First,the greedy strategy and variational mode decomposition are used to process the vibration signal data,and then the energy and features are combined to form a multi-modal feature matrix,and then the support vector machine optimized by the grasshopper algorithm is used to diagnose and identify faults.The experimental results prove that the proposed algorithm can not only identify different fault types at the power end of the fracturing vehicle,but also has higher accuracy and operating efficiency than traditional methods.