Rolling bearing service life state same-scale characterization and recognition method under different rotating speeds

Abstract

The present invention relates to a rolling bearing service life state same-scale characterization and recognition method under different rotating speeds. The method is characterized in that a dimensionless parameter which is low in rotating speed sensitivity and is irrelevant with the vibration energy and a multi-domain information entropy are utilized to reflect the service life state of a rolling bearing, and the dimensionless parameter and the multi-domain information entropy are fused to construct a rolling bearing service life state characteristic set, thereby realizing the same-scale quantitative characterization of the service life state under different rotating speeds; a service life sensitive index algorithm is used to filter the characteristics poor in service life sensibility, the characteristics good in service life sensibility are selected to construct a service life state sensitive characteristic set having stronger characterization capability, and an orthogonal neighborhood preserving embedding algorithm is used for the nonlinear dimensionality reduction and is used to remove the redundant information to thereby obtain a low-dimension service life state sensitive characteristic set of good classification characteristic; and then a weighted nearest neighbor classifier of good robustness is applied to realize the classification recognition of different service life states, thereby finally realizing the rolling bearing service life state same-scale characterization and recognition method under different rotating speeds. The method enables the rolling bearing service life state to be recognized accurately under different rotating speeds, and has a better application effect.

Description

technical field [0001] The invention belongs to the technical field of state monitoring and reliability evaluation of rolling bearings, and relates to a method for characterizing and identifying life states of rolling bearings on the same scale at different rotational speeds. Background technique [0002] Rolling bearings are one of the most important moving parts in mechanical equipment, and their performance degradation or failure directly affects the performance and reliability of the whole machine. Major and key equipment (such as wind turbines, aerospace vehicles, etc.) put forward higher requirements for the reliability and life of rolling bearings. Research on high reliability and long life of rolling bearings has been carried out at home and abroad, and accelerated life experiments have been carried out. In the research process of high reliability and long life of rolling bearings, the primary problem that must be solved is the effective characterization and accurate...

AI Technical Summary

Problems solved by technology

However, the method of order analysis is limited by hardware and algorithm accuracy, and the obtained order spectrum is consistent in the angular domain scale, but its vibration energy is still affected by the rotational speed

The time-frequency analysis method has problems such as cross-term interference and difficult selection of wavelet bases. It works better when the speed fluctuates small, but it is not suitable for the occasion of speed fluctuations.

Therefore, due to their own limitations, the above two methods are difficult to effectively solve the same-scale representation and identification of the rolling bearing life state at variable speeds.

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