Rolling bearing fault on-line detection and state assessment method

Abstract

A rolling bearing fault on-line detection and state assessment method is disclosed. The method comprises the following steps: twelve dimensional dimensionless parameters are extracted; the twelve dimensional dimensionless parameters comprise six dimensional time domain statistical parameters, three dimensional frequency domain statistical parameters and three dimensional dimensionless parameters in a small wave envelope spectrum; standardized reconstruction characteristic vectors can be obtained; whether a rolling bearing malfunctions is determined, and a state of the rolling bearing is assessed. Via the rolling bearing fault on-line detection and state assessment method, the twelve dimensional dimensionless parameters which can be used for effectively representing the state of the rolling bearing can be automatically extracted, the twelve dimensional dimensionless parameters are subjected to decorrelation and standardization operation, standardized reconstruction characteristic vectors that are distributed to form a hypersphere with an original point being a sphere center, and fault detection and state assessment of the rolling bearing can be realized via 2-norms of the standardized reconstruction characteristic vectors; difficult problems of long on line training time, low efficiency, and hard-to-obtain fault samples and the like of a rolling bearing state assessing model can be solved.

Description

technical field [0001] The invention belongs to the field of rolling bearing fault intelligent detection and state evaluation methods, and in particular relates to a rolling bearing fault online detection and state evaluation method. Background technique [0002] Since rolling bearing fault samples are often difficult to obtain, and the types of bearing faults are complex and diverse, several types of faults may exist at the same time, so the state evaluation of rolling bearings often faces the problem of data domain description, that is, the feature evaluation method adopted should be applicable to only normal The condition of the sample. [0003] Since the on-line monitoring of rolling bearings can essentially be regarded as the problem of describing the boundary of the normal data domain of bearings, it is necessary to study the distribution of multidimensional feature vectors in space in order to better use prior knowledge to build a suitable model. Usually, the dimensi...

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Problems solved by technology

[0006] The purpose of the present invention is to provide an online training rolling bearing fault detection and state evaluation method,...

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