| In the wave of intelligent construction,relying on the Internet of things technology,information and communication technology and computer technology,the coal preparation plant has always regarded online monitoring and intelligent management of equipment as the top priority in the construction of intelligent coal preparation plant.At present,many coal preparation plants use a variety of intelligent sensors to collect signals of electromechanical equipment to achieve real-time monitoring of equipment.However,with the increase of the number of sensors,coal preparation plants have not put forward more efficient solutions for the storage of large amounts of data,high concurrent reading and writing,remote monitoring and intelligent diagnosis and other problems.By means of new type of relational database schema free,high scalability,high availability,and ease of management features to manage store data,blend in the advantage of the new Web technology equipment remote monitoring service system,at the same time use data analysis tools such as MATLAB,cloud platform means big data analysis methods,the accuracy of equipment fault diagnosis and constantly improve the speed,so as to speed up the intellectualization and informatization construction of coal preparation plant.Based on this,this paper will make use of the advantages of MongoDB and Node.js to design a coal preparation plant equipment operation monitoring and intelligent diagnosis system to realize mass sensor data storage and application.The main work of this paper is as follows:(1)Coal preparation plant equipment operation data storage research based on mongoDB.According to the design principles of MongoDB database and the application characteristics of monitoring data of equipment operation in coal preparation plant,the database design was completed and the data storage platform was constructed.Aiming at the storage and management of a large amount of data,the scheme of database cluster configuration was studied to enhance the reliability of the system.In addition,the method of migrating the relational database to MongoDB database is studied.(2)Web application research based on MongoDB and Node.js.Based on the strengths of MongoDB and Node.js,this paper studied the basic data operation interface of the equipment operation data of coal preparation plant.It used the mature Express framework of Node.js to build the RESTful Web Services interface,completed the design of the server-side function module based on the data interface.Meanwhile,according to the main function modules of the server,using mature open source framework of JQuery and Bootstrap front-end,JavaScript,HTML and CSS front-end development technology and other advanced concepts,Mockplus prototype design software was used to complete the design of the front-end page,laying a good foundation for the completion of the complete state detection and fault diagnosis client system in the later stage.In addition,the efficient compression methods of temperature data and vibration data are studied.(3)Motor fault intelligent diagnosis system based on multi-source data fusion.In view of the traditional motor fault diagnosis system using a single data analysis method,it is hard to comprehensive analysis was carried out on the motor running status.Therefore,this paper adopts the method of electrical data and mechanical data fusion to realize data processing and fault diagnosis through the wavelet packet and the BP neural network to improve the accuracy of fault diagnosis. |
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