Structural damage monitoring method based on adaptive density peak value-hybrid probability modeling

Abstract

The invention puts forward a structural damage monitoring method based on adaptive density peak value-hybrid probability modeling, and belongs to the technical field of aviation structure health monitoring. The method comprises the following steps that: firstly, through an adaptive density peak value quick searching strategy, determining the probability density peak value of a structure signal feature sample; on the basis, through an expectation maximization algorithm, establishing a hybrid probability model which can represent structure signal feature sample probability distribution; and finally, adopting a probability density inner product damage index to quantitatively represent the migration change, which is relative to the hybrid probability model under a health state, of the hybrid probability model so as to realize normalized damage evaluation. The method is simple and high in calculation efficiency, and the adaptivity, the stability and the reliability of structural damage monitoring under time varying environment can be effectively improved.

Description

technical field [0001] The invention relates to a structural damage monitoring method based on adaptive density peak-mixed probability modeling, and belongs to the technical field of aviation structure health monitoring. Background technique [0002] Aeronautical structure health monitoring technology can monitor the health status of aviation structures online, and then predict and estimate structural damage and remaining life, so as to ensure the safety of aircraft structures and reduce structural maintenance costs. In recent years, aeronautical structural health monitoring technology has gradually shifted from early theoretical research to engineering application research. However, in actual aeronautical engineering applications, structural health monitoring technology often faces more complex time-varying service environments than laboratory conditions, such as changing temperature and humidity, boundary conditions, random vibration, fatigue loads, etc. Under the influen...

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Problems solved by technology

The number of probability components of the mixed probability model established in this type of method can adapt to the change of the probability distribution of the signal characteristic parameters caused by time-varying environmental factors, but the adaptive mechanism of this type of method is often very complicated and cannot guarantee the stability of the modeling results Therefore, the computational efficiency and reliability of such damage monitoring methods need to be improved urgently.

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