Bearing fault monitoring method and device

Abstract

The invention discloses a bearing fault monitoring method and device. The method comprises the steps of obtaining and training full life cycle historical data of a to-be-monitored bearing; processing the first alarm time data and the first early warning time data to obtain first alarm time data and first early warning time data; different preset large cycles and different preset small cycles are respectively selected, and are gradually converted into a plurality of specific value different cycles, and second alarm time data and second early warning time data are determined; substituting the first alarm time data, the first early warning time data, the second alarm time data and the second early warning time data into a preset different-period evaluation function to obtain optimal data; and if the false alarm exists, updating and monitoring the bearing to be monitored by using the optimal alarm data and the optimal early warning data. According to the bearing fault monitoring method and device provided by the embodiment of the invention, the alarm data and the early warning data are dynamically updated by designing the specific bearing fault monitoring strategy, so that the timeliness and the accuracy of bearing fault monitoring of mechanical equipment are improved.

Description

technical field [0001] The invention relates to the technical field of bearings, in particular to a method and device for monitoring bearing faults. Background technique [0002] With the continuous development of science and technology, rotating machinery has become an indispensable component in production technology, and has a very wide range of applications in different fields such as electric power, petrochemical, metallurgy, aviation and aerospace. Among them, as a component that can support the mechanical rotating body, reduce the friction coefficient during its movement, and ensure its rotation accuracy, the bearing plays an important role in contemporary mechanical equipment. [0003] In the fault detection of mechanical equipment, about 30%-40% of equipment faults are caused by bearing faults, so the research on fault state monitoring of rolling bearings has always been a very important technical field. In the prior art, the monitoring and diagnosis of mechanical e...

AI Technical Summary

Problems solved by technology

[0005] from Figure 1 can be seen that the monitored data has changed significantly since 150 minutes, and the data jumps from normal to the size that needs to be alarmed or shut down in a very short period of time. Downtime for maintenance can have serious consequences

Images