Rolling bearing health evaluation method based on local characteristic scale decomposition-approximate entropy and manifold distance

Abstract

The invention proposes a rolling bearing health evaluation method based on local characteristic scale decomposition-approximate entropy (APEn) and manifold distance. First, an original vibration signal is decomposed by LCD into a plurality of intrinsic scale components (ISCs); then, the approximate entropy of each ISC is calculated; and finally, the manifold distance between the approximate entropy of the ISCs and the approximate entropy of normal data is calculated, and the calculated manifold distance is normalized into confidence (CV) to express the health degree of a rolling bearing. The normal operation of rolling bearings is particularly important in the modern industrial complex mechanical system, so that rolling bearing performance evaluation is of great significance in prediction and health assessment of the mechanical system. However, as bearing vibration signals are nonlinear and unsteady, it is particularly difficult to accurately extract the characteristics of bearing vibration signals. Local characteristics of signals can be extracted accurately using the method proposed by the invention. Results show that the method proposed by the invention can be used to evaluate the health degree of rolling bearings effectively.

Description

technical field [0001] The invention relates to the technical field of rolling bearing health assessment, in particular to a rolling bearing health assessment method based on local characteristic scale decomposition-approximate entropy and manifold distance. Background technique [0002] Rolling bearings are one of the most important components in rotating machinery. Bearing failure or damage often leads to mechanical system failures, and even poses a threat to the safety of workers. The bearing health assessment can obtain the health status of the bearing and prevent the occurrence of failure, so that the equipment can be repaired optimally and avoid the loss caused by unplanned downtime. In addition, reasonable maintenance can not only reduce maintenance costs, but also maximize the efficiency of components. Therefore, it is of great significance to evaluate the performance of rolling bearings in mechanical equipment. [0003] Due to the unsteady nonlinear characteristic...

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Problems solved by technology

[0005] In the traditional similarity measurement method, the Euclidean distance has the advantages of simple operation and short calculation time. However, the similarity measurement based on the Euclidean distance measurement cannot fully reflect the spatial distribution characteristics of complex data. The approximate entropy of ISCs may be closer in the same manifold and calculated farther in Euclidean space, so we use a new method to calculate the distance between high-dimensional data with complex structures, which is called is the manifold distance

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