College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel). 2019 Mar 2;19(5):1070. doi: 10.3390/s19051070.
Fatigue cracks are one of the common failure types of key aircraft components, and they are the focus of prognostics and health management (PHM) systems. Monitoring and prediction of fatigue cracks show great application potential and economic benefit in shortening aircraft downtime, prolonging service life, and enhancing maintenance. However, the fatigue crack growth process is a non-linear non-Gaussian dynamic stochastic process, which involves a variety of uncertainties. Actual crack initiation and growth sometimes deviate from the results of fracture mechanics analysis. The Lamb wave-particle filter (LW-PF) fatigue-crack-life prediction based on piezoelectric transducer (PZT) sensors has the advantages of simple modeling and on-line prediction, making it suitable for engineering applications. Although the resampling algorithm of the standard particle filter (PF) can solve the degradation problem, the discretization error still exists. To alleviate the accuracy decrease caused by the discretization error, a Lamb wave-minimum sampling variance particle filter (LW-MSVPF)-based fatigue crack life prediction method is proposed and validated by fatigue test of the attachment lug in this paper. Sampling variance (SV) is used as a quantitative index to analyze the difference of particle distribution before and after resampling. Compared with the LW-PF method, LW-MSVPF can increase the prediction accuracy with the same computational cost. By using the minimum sampling variance (MSV) resampling method, the original particle distribution is retained to a maximum degree, and the discretization error is significantly reduced. Furthermore, LW-MSVPF maintains the characteristic of dimensional freedom, which means a broader application in on-line prognosis for more complex structures.
疲劳裂纹是关键飞机部件的常见失效类型之一，也是预测与健康管理（PHM）系统的关注焦点。疲劳裂纹的监测和预测在缩短飞机停飞时间、延长使用寿命和增强维护方面显示出巨大的应用潜力和经济效益。然而，疲劳裂纹的扩展过程是一个非线性非高斯动态随机过程，涉及多种不确定性。实际的裂纹起始和扩展有时会偏离断裂力学分析的结果。基于压电换能器（PZT）传感器的兰姆波-粒子滤波器（LW-PF）疲劳裂纹寿命预测具有建模简单和在线预测的优点，因此适用于工程应用。尽管标准粒子滤波器（PF）的重采样算法可以解决退化问题，但仍然存在离散化误差。为了缓解离散化误差引起的精度下降问题，提出了一种基于兰姆波最小采样方差粒子滤波器（LW-MSVPF）的疲劳裂纹寿命预测方法，并通过附件凸耳的疲劳试验进行了验证。采样方差（SV）被用作定量指标来分析重采样前后粒子分布的差异。与 LW-PF 方法相比，LW-MSVPF 可以在相同的计算成本下提高预测精度。通过使用最小采样方差（MSV）重采样方法，可以最大程度地保留原始粒子分布，显著降低离散化误差。此外，LW-MSVPF 保持了维数自由度的特性，这意味着在更复杂结构的在线预测中有更广泛的应用。
