The Development Of Remote Fault Diagnosis Platform Of Large Machinery Based On SaaS And Multi-information Fusion Technology

With the application of large-scale machinery and equipment in industrial production, the development of modern industry is becoming more and more high-speed, automatic and intelligent. At the same time, requirements which about low failure, reliability and stability of the mechanical equipment are increasingly high, a lot of faults only to be found and dealed with as soon as possible, can reduce the occurrence of major accidents, and avoid unnecessary economic losses and casualties utmostly.To solve the above problems, the purpose of this paper is to use Saa S framework and multi-information fusion technology to fault diagnosis field, forming a fusion fault diagnosis method of weighted evidence theory, design and implementation a remote fault diagnosis platform of large machinery based on Saa S and multi-information fusion technology. To improve the normative and ordering of remote fault diagnosis service of mechanical equipment, and expand the scope of remote online monitoring and fault diagnosis, so that operators can monitor equipment any time and any place, to understand the status of the running equipment, so the abnormal state of equipment can be dealed with as early as possible, and nip in the bud.In this paper, firstly, the research and development of mechanical equipment fault diagnosis are analyzed, combining with the advantages and disadvantages of Saa S multi-information fusion technology, it is concluded that this system platform for the industry's outlook. Secondly, in view of the uncertainty of the mechanical equipment in the fault diagnosis, the D-S evidence theory is used to fuse judgment results of the multi-sensor. After comprehensive analysis of the existing shortcomings of D-S evidence theory, the improvement is made. To combine D-S evidence theory with weighted ideas, it forms the weighted evidence theory. Through numerical example analysis, it is proved that the weighted evidence theory can improve the accuracy and reliability of fault diagnosis, and it can lay a solid foundation for the application of evidence theory in fault diagnosis. Thirdly, after the detailed analysis of the four framework models of Saa S software services and the needs of the system, we design the framework model of the platform system, and design the various modules of the system in detail. Finally, the development of remote fault diagnosis platform of large machinery based on Saa S and multi-information fusion technology is accomplished.