Bearing fault intelligent diagnosis method for digital twin system

Abstract

The invention discloses a bearing fault intelligent diagnosis method for a digital twin system, and belongs to the technical field of bearing fault unbalance detection. According to the method, the bearing fault diagnosis effect is improved under the condition that normal data and abnormal data in a digital twin system are unbalanced under the actual condition and original data are not expanded. The method comprises the steps of monitoring a bearing vibration signal of a target bearing in real time by a digital twin system; inputting the current bearing vibration signal of the target bearing into the bearing fault diagnosis network, and obtaining the current fault detection result of the target bearing based on the output of the bearing fault diagnosis network. The bearing fault diagnosis method is used for diagnosing the data imbalance phenomenon in the actual scene of the bearing, the diagnosis effect on the fault data can be improved in the actual scene with normal and fault data imbalance, and the health condition of the bearing equipment can be monitored in real time by utilizing the bearing fault diagnosis network set by the bearing fault diagnosis method in the digital twin system.

Description

technical field [0001] The invention relates to the technical field of bearing fault unbalance detection, in particular to an intelligent diagnosis method for bearing faults oriented to a digital twin system. Background technique [0002] Preventive maintenance of equipment can effectively prolong the service life of equipment and improve the efficiency of equipment use. It is very important to carry out preventive maintenance of equipment. With the development of big data technology and intelligent manufacturing, it is particularly important to use modern information technology to manage equipment scientifically and informationally. The digital twin system can well establish the connection between virtual simulation and physical entity. Digital twin (Digital Twin) refers to the use of data such as physical entities, sensors, and operation history, combined with multi-physical quantities, multi-disciplines, multi-scales, and multi-probabilities to describe and build the cha...

AI Technical Summary

Problems solved by technology

However, in practical situations, it is very difficult to determine the misclassification cost of different classes

It is not sufficient to directly determine the misclassification cost of a failure sample based on the imbalance ratio of the samples

Images