Abstract
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.
Highlights
As a common failure model of mechanical damage, fatigue cracks account for 50% to 90% of the total failures and are one of the most dominant and most dangerous form of structural damage [1].The initiation and propagation of fatigue cracks are affected by many uncertainties, such as the statistical features at the microscale of materials, material parameters, machining errors, internal damage, loading, stress ratio, temperature, etc
The accurate than the Lamb wave-deterministic resampling particle filter (LW-DRPF) method [8,19], it has not lost the characteristic of dimensional freedom, LW-MSVPF method has not overcome the sample impoverishment problem and is slightly less which means a broader application in on-line prognosis for more complex structures
Accurate than the LW-DRPF method [8,19], it has not lost the characteristic of dimensional freedom, which means a broader application in on-line prognosis for more complex structures
Summary
As a common failure model of mechanical damage, fatigue cracks account for 50% to 90% of the total failures and are one of the most dominant and most dangerous form of structural damage [1]. To improve the importance function, Chen et al [20] substituted the transfer probability density function with the mixture of measurement and transition probability density function He drew out a Gaussian weight–mixture proposal particle filter method for on-line prognosis of fatigue crack propagation. In order to mitigate the particle impoverishment caused by the discretization error and improve the prediction accuracy of crack growth life prediction, sampling variance (SV) is used as a metric to study the difference of particle distribution before and after resampling in this paper. Based on the analysis results of SV, an on-line prediction method of Lamb wave-minimum sampling variance particle filter (LW-MSVPF) is proposed and validated by fatigue test of an attachment lug.
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