Method of Statistical Data Processing for Two-Stage Fatigue Tests

Kozhokhina, Olena; Yutskevych, Sviatoslav; Радченко, Олександр; Gribov, Viktor; Chuzha, O. O.

doi:10.1109/acit49673.2020.9208864

Cited by 2 publications

(1 citation statement)

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“…With the ever-increasing demand for high efficiency, lightweight, and highly reliable equipment, challenges are put forward for study on fatigue evaluation and the characteristics of its key components [ 1 , 2 ]. During the service life of metal components, cyclic load often leads to fatigue damage; however, it is difficult to measure the fatigue state of components directly by sensors.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian Process State Space Model Fusion Physical Model and Residual Analysis for Fatigue Evaluation

Yin

Zhou

Liang

2022

Sensors

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Residual stress is closely related to the evolution process of the component fatigue state, but it can be affected by various sources. Conventional fatigue evaluation either focuses on the physical process, which is limited by the complexity of the physical process and the environment, or on monitored data to form a data-driven model, which lacks a relation to the degenerate process and is more sensitive to the quality of the data. This paper proposes a fusion-driven fatigue evaluation model based on the Gaussian process state–space model, which considers the importance of physical processes and the residuals. Through state–space theory, the probabilistic space evaluation results of the Gaussian process and linear physical model are used as the hidden state evaluation results and hidden state change observation function, respectively, to construct a complete Gaussian process state–space framework. Then, through the solution of a particle filter, the importance of the residual is inferred and the fatigue evaluation model is established. Fatigue tests on titanium alloy components were conducted to verify the effectiveness of the fatigue evaluation model. The results indicated that the proposed models could correct evaluation results that were far away from the input data and improve the stability of the prediction.

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Section: Introductionmentioning

confidence: 99%