Second generation small-wave support vector machine assessment method for damage and remaining life of metal structure

Abstract

The invention discloses a second generation small-wave support vector machine assessment method for damage and remaining life of a metal structure. According to the method, an intrinsic mode function is obtained through decomposition of an experience mode, a time-frequency domain statistic characteristic of the intrinsic mode function is extracted, a most sensitive characteristic is chosen according to a distance accessment principle to construct a best characteristic set, a minimum quantization error indicator which has an obvious performance degradation trend along with time changes is constructed by means of self-organization neural network characteristic fusion techniques, a biorthogonal small-wave support vector machine kernel function of second generation small-wave transform is provided, a service life prediction model of the second generation small-wave support vector machine is constructed, the minimum quantization error indicator serves as a prediction characteristic, and quantitative assessment for the damage and the remainig service life of a metal structural component of mechanical equipment under a small subsample is achieved.

Description

technical field [0001] The invention belongs to the field of failure prediction of mechanical equipment, and in particular relates to a method for quantitative evaluation of damage failure detection and remaining life of key metal structural components. Background technique [0002] With the increasing requirements of modern warfare on the attendance rate and combat readiness rate of weapons and equipment, and the rapid development of material science, testing technology, signal analysis and artificial intelligence technology, a large number of weapons and equipment currently use failure prediction and state management technology (Prognostic and Health Management, PHM) to achieve "condition-based maintenance", that is, preventive maintenance based on conditions. This technology detects the state of the system to predict the failure of the system and its components, and determines its remaining life; it uses multi-sensor information fusion technology to diagnose system failur...

AI Technical Summary

Problems solved by technology

However, in practical applications, the selection of kernel function parameters makes the support vector machine prone to over-learning or under-learning, which directly affects its generalization performance.

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