Application of deterministic resampling particle filter to fatigue prognosis

Yuan, Shenfang; Chen, Jian

doi:10.21595/jve.2017.18537

Cited by 8 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application range of both methods is limited. To reduce calculation consumption and extend the application range of SIF, this paper uses a simplified version of the SIF range [19,27], as shown in Equation (3). ΔK=Δσsans-serifπa where Δ σ is the stress amplitude.…”

Section: State–space Model For Fatigue Crack Growthmentioning

confidence: 99%

“…To take the uncertainty during fatigue crack growth into consideration, Zio et al [13] adopted the following revised model, as shown in Equation (5). normaldanormaldNf=10ωCfalse(tfalse)C(ΔK)m where ωCfalse(tfalse) is a zero-mean additive white Gaussian noise that follows Nfalse(0,σωC2false), denoting the uncertainty of material parameters during crack propagation, and σωC is the standard deviation [18,19,20]. The results of a large number of fatigue tests show that material parameters C and m have differences even with the same type of structure under the same working conditions, which characterizes the uncertainty of crack growth.…”

Section: State–space Model For Fatigue Crack Growthmentioning

confidence: 99%

“…Calculation times were 0.4262 s and 0.4302 s, respectively (MATLAB R2016b, 64-bit 8G memory, Core i5-3230M 2.60GHz, Win7 system), which means that the LW-MSVPF method can obtain a higher precision with the same computational cost than the LW-PF. Although the LW-MSVPF method has not overcome the sample impoverishment problem and is slightly less 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.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…By using the standard PF method, the on-line prediction of crack growth on edge and central cracked aluminum plate were realized, influences brought by uncertainties of crack propagation parameters and monitoring methods were effectively eliminated, and a higher prediction accuracy was achieved. To overcome the sample impoverishment problem of the standard PF, Yuan and Yang et al [ 8 , 19 ] proposed a Lamb wave-deterministic resampling particle filter (LW-DRPF) method for fatigue crack prognosis, and it was verified on attachment lugs and edge cracked aluminum plate during fatigue tests. The prediction results of LW-PF and LW-DRPF indicate that the prediction accuracy has been further improved by LW-DRPF.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Lamb Wave-Minimum Sampling Variance Particle Filter-Based Fatigue Crack Prognosis

Gao

2019

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: State–space Model For Fatigue Crack Growthmentioning

confidence: 99%

Section: State–space Model For Fatigue Crack Growthmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lamb Wave-Minimum Sampling Variance Particle Filter-Based Fatigue Crack Prognosis

Gao

2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…For further improvement, most studies are focused on improving particle resampling, usually based on traditional resampling mechanisms, such as hierarchical resampling [30], adaptive resampling [31][32][33], deterministic resampling [34][35][36], etc. Another way is to increase the diversity of samples by introducing intelligence optimization ideas, such as a genetic algorithm [37,38], firefly algorithm [39], bat algorithm [40] and so on.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking

Deng

Chen

2019

Applied Sciences

View full text Add to dashboard Cite

High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the effectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios.

show abstract