Health state assessment method for rotary machine based on probability density function

Abstract

The invention relates to a health state assessment method for a rotary machine based on statistical methods such as kernel density estimation and K-L divergence calculation. The method comprises the following steps that 1) original vibration data of a monitored object is collected; 2) time-domain and frequency-domain features are extracted from the original vibration data; 3) the dimensions of the time-domain and frequency-domain features are reduced to obtain sensitive features; 4) a movable sliding window of the width k is used to dynamically select sample sets, and the statistical characteristics of the sample sets are calculated; 5) the probability density function of each sample set under each sensitive feature is calculated; 6) the K-L divergence between the probability density functions of two adjacent sample sets under the same sensitive feature is calculated; and 7) an integrated K-L divergence is calculated and serves as a health assessment index of the monitored object. The health state assessment method has the advantages that the statistical uncertainty of the samples is fully considered, and the accuracy and generalization performance of a health state assessment model are improved.

Description

technical field [0001] The invention belongs to the field of performance degradation evaluation of mechanical equipment, and in particular relates to a statistical method based on kernel density estimation and K-L divergence to realize health state evaluation of rotating machinery. Background technique [0002] Rotating machinery is widely used in large-scale manufacturing systems and important technical equipment, such as CNC machine tools, wind power generators, aerospace engines, etc. In these manufacturing systems and equipment, key components in rotating machinery such as rolling bearings and gears play an important role, but rotating machinery parts need to withstand alternating mechanical stress and occasional impact during operation, coupled with inherent manufacturing errors, often There will be some early defects (such as: mild wear, pitting, etc.). If these defects are not diagnosed in time, they will continue to deteriorate over time, eventually leading to syste...

AI Technical Summary

Problems solved by technology

At present, domestic and foreign research on intelligent fault diagnosis methods from the perspective of sample statistics is still very limited.

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