Design And Implementation Of Operation Monitoring And Diagnosis System For Optoelectronic Equipment

Large-scale optoelectronic equipment is an indispensable detection and tracking instrument for astronomical observation and shooting range testing.It can automatically adjust the focus according to the distance of the measured moving target,realize real-time imaging automatic tracking,and complete the measured target image,measurement time,azimuth angle and pitch angle Synchronous record.Therefore,there are extremely high requirements for the stability and accuracy of the equipment.It is difficult for the traditional manual on-site inspection and repair and afterwards fault repair to effectively ensure the reliability and availability of the equipment.In the Internet era,the development trend of ground support systems for large equipment is gradually becoming networked,intelligent,and integrated.It is necessary to further study fault diagnosis methods with practical engineering value and establish targeted monitoring and diagnosis systems to assist in maintaining optoelectronic equipment.Reliable operation.Based on the above research background,this thesis focuses on the research and application of fault diagnosis methods for large-scale optoelectronic equipment.By analyzing the typical failure modes of the equipment and the business process to develop a supporting operation monitoring and diagnosis system,the main work is as follows:Taking the torque motor bearing of the rack system as the diagnosis object,the fault diagnosis method of mechanical parts based on deep learning is studied.Unlike traditional multi-layer small convolution kernel stacking,the fault diagnosis model based on one-dimensional convolution neural network WDCNN uses a larger convolution kernel in the first layer,which can filter high-frequency noise in the vibration signal;convolution layer and activation Batch normalization functions are added between layers to automatically adjust network parameters and complete fault classification through optimization algorithms;input signals do not need to adopt feature engineering,which truly realizes end-to-end fault diagnosis and meets the needs of optoelectronic equipment monitoring and diagnosis.Design and develop on-line status monitoring and intelligent fault diagnosis system of optoelectronic equipment based on B/S architecture.Research the working principle and structure of the equipment,sort out the corresponding relationship between the failure mode and the failure symptom parameters,sort out the real-time condition monitoring parameter set and the failure knowledge base,analyze the requirements of the operation monitoring and diagnosis system;plan the overall system architecture according to the design principles The multi-source characteristics and transmission form of operating parameters are the starting point,complete the design of torque motor parameter collection,system remote communication,data storage and software functions;apply WDCNN fault diagnosis model to practice,integrate multiple automation technologies and computer technologies to build software Web The platform realizes the real-time monitoring of the operation situation of the optoelectronic equipment and online fault diagnosis and analysis.