Rolling bearing prediction method and system based on local binary pattern and deep belief network

Abstract

The invention provides a rolling bearing prediction method and system based on a local binary pattern and a deep belief network. The rolling bearing prediction method comprises the steps of firstly extracting a vibration signal of a one-dimensional rolling bearing, and then converting the vibration signal into a two-dimensional time domain grayscale image; blocking the two-dimensional time domaingrayscale image; respectively extracting local textural features of block images by using LBP<P, R><u2>, and then combining statistical histograms of the block images based on the LBP<P, R><u2> to serve as the input of a DBN (Dynamic Bayesian Network), wherein the DBN performs automatic extraction on deep features of the statistical histograms, and model parameters of the whole DBN are adjusted through DBN forward self-learning and gradient descent based backward propagation so as to obtain the trained DBN; and finally enabling the statistical histogram features, which are obtained through theLBP<P, R><u2>, of the two-dimensional grayscale image of the vibration signal of the rolling bearing with the state being unknown to serve as the input, extracting high-level features capable of reflecting intrinsic information layer by layer by using the trained DBN network, and then inputting a layer-by-layer feature self-extraction into top classification so as to realize fault recognition forthe rolling bearing under the condition of multiple loads and strong noise.

Description

technical field [0001] The invention belongs to the fields of image processing and mechanical equipment health detection, and relates to a local binary pattern algorithm, in particular to a rolling bearing prediction method and system based on a local binary pattern and a deep belief network. Background technique [0002] Rolling bearings are the most common and critical mechanical components in rotating machinery, widely used in household and industrial equipment. Because bearings usually work in harsh working environments, they are prone to failure during work. If failures are not detected in time, they can lead to unplanned machine downtime and even catastrophic damage. Therefore, it is necessary to adopt a detection method to detect the health status of rolling bearings, identify whether there is a fault, the type of fault and the severity of the fault, and then take necessary measures to prevent further damage to the bearing and ensure timely rest of the equipment. ru...

AI Technical Summary

Problems solved by technology

[0006] The technical problem solved by the technical solution provided according to the embodiment of the present invention is that the combination of the traditional bearing diagnosis method and the image processing method is not effective, and it is difficult to perform better feature extraction and dimension reduction on the vibration time-domain two-dimensional image, and to mine the essential information of the data sample , the problem of limited classification accuracy and high computational consumption

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