Identification method of rolling bearing state under variable load of EEMD-Hilbert envelope spectrum in combination with DBN

Abstract

The invention provides an identification method of rolling bearing state under variable load of EEMD-Hilbert envelope spectrum in combination with DBN, and belongs to the field of rolling bearing fault detection. The aim is to solve the problems that under the circumstance of training data using one load and test data using other loads, the rolling bearing fault state and the fault extent cannot be accurately identified. Firstly EEMD is conducted on the vibration signals of each status of the rolling bearing, then a sensitive eigenmode state function is selected, and Hilbert transformation is conducted to obtain the envelope spectrum. Finally, new high-dimensional data are built according to the order of the IMF envelope spectrum of the vibration signals of each status, then inputted into the DBN of each hidden layer node structure optimized by the genetic algorithm, and the multi-state recognition of rolling bearing under the variable load is achieved. In the process of 10 state recognition of rolling bearing using DBN, under the circumstance of the training data using one load and the test data using other loads, the EEMD-Hilbert envelope spectrum time domain or frequency-domain amplitude spectrum can better reflect the multiple state characteristics of rolling bearing under different loads, and has a higher recognition rate.

Description

technical field [0001] The invention relates to a rolling bearing state recognition method under variable load, which belongs to the field of rolling bearing fault detection. Background technique [0002] Rolling bearings are key components of many rotating machines, and their operating status is affected by many factors [1-2] . The load often changes in the rolling bearing, and the change of the load will directly affect the change of the vibration characteristics of the rolling bearing. Therefore, under variable load conditions, it is of great significance to accurately identify the running state of the rolling bearing to ensure the normal operation of the entire mechanical equipment. [0003] The multi-state recognition of different fault locations and different performance degradation degrees of rolling bearings is essentially a pattern recognition of their operating states [3] . In terms of feature extraction, ensemble empirical mode decomposition (EEMD) has a good ...

AI Technical Summary

Problems solved by technology

[0005] The present invention solves the problem that the existing state recognition method for rolling bearings does not conform to the actual running state of the rolling bearing because the vibration signal of the bearing with constant load is used for model testing, so that the state recognition meth

Images