Yin Aijun, Zhou Junlin, Liang Tianyou
The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China.
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2022 Mar 25;22(7):2540. doi: 10.3390/s22072540.
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.
残余应力与部件疲劳状态的演变过程密切相关，但它会受到各种因素的影响。传统的疲劳评估要么侧重于物理过程，而这受到物理过程和环境复杂性的限制；要么侧重于监测数据以形成数据驱动模型，该模型与退化过程缺乏关联且对数据质量更为敏感。本文提出了一种基于高斯过程状态空间模型的融合驱动疲劳评估模型，该模型考虑了物理过程和残余量的重要性。通过状态空间理论，将高斯过程和线性物理模型的概率空间评估结果分别用作隐藏状态评估结果和隐藏状态变化观测函数，以构建完整的高斯过程状态空间框架。然后，通过粒子滤波器的求解，推断出残余量的重要性并建立疲劳评估模型。对钛合金部件进行了疲劳试验，以验证疲劳评估模型的有效性。结果表明，所提出的模型能够对远离输入数据的评估结果进行校正，并提高预测的稳定性。
