Abstract
The fault phenomenon of high pressure roller mill gearbox lubrication system is not easy to be find in many cases, the fault of system is easy to be ignored, and it is more difficult to judge with the traditional method. For this reason, the fault diagnosis model of the particle swarm neural network has been established by using actual sample data, determining the cause of fault through the actual monitoring data. The practice has proved that it has better prediction effect.
Highlights
The high pressure roller mill is widely used in mining, and cement enterprises etc
As the important transmission mechanism, the high pressure roller mill gearbox directly affects the normal operation of the equipment, but its lubrication system is the assurance of safe and reliable production
The paper presented the method of particle swarm neural network fault diagnosis, which is established based on the actual data
Summary
As an important equipment of crushing, it directly affects the production and benefit of enterprises. It is well received by enterprise because of its characteristics of energy saving and high efficiency, and has been widely promoted. As the important transmission mechanism, the high pressure roller mill gearbox directly affects the normal operation of the equipment, but its lubrication system is the assurance of safe and reliable production. Whether the cause of the problem is quickly identified for troubleshooting. These questions have become a barrier in the daily inspection and maintenance. The paper has diagnosed and predicted the fault by using the actual data, and the practice showed that prediction result is very good
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