A method for predicting the remaining life of rotating machinery based on fda and svdd

Abstract

The invention discloses a method for predicting the remaining life of rotating machinery based on FDA and SVDD. The method uses a functional data analysis (FDA) method to convert the collected discrete data into a smooth function to solve the loss of information or the distortion of model estimation. and other issues, using SVDD to identify mechanical operating conditions to reduce the need for sample collection and provide prediction accuracy, calculate the SVDD model radius of the test sample and compare it with the time radius trajectory of the entire life cycle to obtain prediction results. The invention effectively reduces the sample collection time required for the prediction of the remaining life of the rotating machinery, and improves the prediction accuracy.

Description

technical field

[0001] The invention relates to a method for predicting the remaining life of a rotating machine, in particular to a method for predicting the remaining life of a rotating machine based on FDA and SVDD. Background technique

[0002] Functional Data Analysis (FDA) method is a new branch of statistics. The concept of functional data was first proposed by Canadian scholar J.O. Ramsay, pointing out that modern sophisticated data collection systems can obtain a series of functional data, and observation data should be regarded as a dynamic concept, which can be solved by traditional data analysis methods. Research will bring problems such as loss of information or distortion of model estimation, which are mainly used in the fields of biomedicine and economics, and rarely involved in the field of mechanics.

[0003] Support vector data description (Support vector data description, SVDD) was proposed by Tax and Duin, based on statistical learning theory, to establi...

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