A method and system for on-line predict residual life of rolling bear

Abstract

The invention discloses an on-line prediction method for residual life of rolling bearing, As that roll bearing move from a healthy state to a damaged state, The original signal samples and corresponding degeneration energy indexes are extracted from the running process of the bearing, and the original signal samples are used as the input of the five-layer convolution neural network model, and thedegeneration energy indexes are used as the output of the convolution neural network model, and the degeneration energy state model is obtained by training. Real-time acquisition of the original running signals of the rolling bearings to be tested; The original running signal of the rolling bearing to be tested is input into the degradation energy state model, and the degradation energy index isestimated. Then the estimated energy degradation index is used to predict the residual life of the rolling bearings to be tested. The prediction process of the invention only needs to collect the original operation signal of the bearing, and does not need to extract and screen the features, thus overcoming the technical problems that the prior art adopts the methods of feature extraction, featurescreening and regression prediction, which have the characteristics extraction difficulty and the precision is limited.

Description

technical field

[0001] The invention belongs to the technical field of rolling bearing degradation state monitoring, and in particular relates to an online prediction method and system for the remaining life of rolling bearings. Background technique

[0002] With the development of computer and automation technology, the field of machinery manufacturing is developing in the direction of intelligence. Real-time status monitoring of manufacturing equipment is the basic guarantee for the continuous and stable operation of the processing process. Rolling bearings are the basic elements of rotating machinery structures, and their health is directly related to the safe operation of manufacturing equipment. According to the literature, nearly half of the motor failures are caused by the failure of rolling bearings. Especially in extreme working environments such as high speed and heavy load, rolling bearings are prone to failure, which will undoubtedly pose a serious threat to th...

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