Abstract:Accurate prognosis of fatigue crack growth is of great importance to ensure structural integrity, which is a challenging task due to various uncertainties affecting crack growth. To deal with this problem, the particle filter (PF) based prognostics that incorporates on-line structural health monitoring (SHM) becomes a new trend. However, most existing studies adopt the basic PF algorithm, which needs improvements to meet the requirement for on-line prognosis. It refers to the choice of the importance density a… Show more
“…Methods of doing so has been the focus of previous literature on the use of particle filtering methods for FFS assessment and remnant life predictions. 21,40,54 A hypothetical example problem was used in this article to demonstrate how the proposed framework can be applied to different problems. Despite simplifications in defect growth models and the choice of POD curves, the modularity of the proposed framework means that different defect growth models and POD models can be easily substituted as necessary for the specific problem.…”
Section: Discussionmentioning
confidence: 99%
“…Methods of doing so has been the focus of previous literature on the use of particle filtering methods for FFS assessment and remnant life predictions. 21,40,54 Figure 17.Schematic illustration of when a positive monitoring result is obtained, where sizing using the monitoring data can be done to perform more accurate remnant life predictions.…”
The value of using permanently installed monitoring systems for managing the life of an engineering asset is determined by the confidence in its damage detection capabilities. A framework is proposed that integrates detection data from permanently installed monitoring systems with probabilistic structural integrity assessments. Probability of detection (POD) curves are used in combination with particle filtering methods to recursively update a distribution of postulated defect size given a series of negative results (i.e. no defects detected). The negative monitoring results continuously filter out possible cases of severe damage, which in turn updates the estimated probability of failure. An implementation of the particle filtering method that takes into account the effect of systematic uncertainty in the detection capabilities of a monitoring system is also proposed, addressing the problem of whether negative measurements are simply a consequence of defects occurring outside the sensors field of view. A simulated example of fatigue crack growth is used to demonstrate the proposed framework. The results demonstrate that permanently installed sensors with low susceptibility to systematic effects may be used to maintain confidence in fitness-for-service while relying on fewer inspections. The framework provides a method for using permanently installed sensors to achieve continuous assessments of fitness-for-service for improved integrity management.
“…Methods of doing so has been the focus of previous literature on the use of particle filtering methods for FFS assessment and remnant life predictions. 21,40,54 A hypothetical example problem was used in this article to demonstrate how the proposed framework can be applied to different problems. Despite simplifications in defect growth models and the choice of POD curves, the modularity of the proposed framework means that different defect growth models and POD models can be easily substituted as necessary for the specific problem.…”
Section: Discussionmentioning
confidence: 99%
“…Methods of doing so has been the focus of previous literature on the use of particle filtering methods for FFS assessment and remnant life predictions. 21,40,54 Figure 17.Schematic illustration of when a positive monitoring result is obtained, where sizing using the monitoring data can be done to perform more accurate remnant life predictions.…”
The value of using permanently installed monitoring systems for managing the life of an engineering asset is determined by the confidence in its damage detection capabilities. A framework is proposed that integrates detection data from permanently installed monitoring systems with probabilistic structural integrity assessments. Probability of detection (POD) curves are used in combination with particle filtering methods to recursively update a distribution of postulated defect size given a series of negative results (i.e. no defects detected). The negative monitoring results continuously filter out possible cases of severe damage, which in turn updates the estimated probability of failure. An implementation of the particle filtering method that takes into account the effect of systematic uncertainty in the detection capabilities of a monitoring system is also proposed, addressing the problem of whether negative measurements are simply a consequence of defects occurring outside the sensors field of view. A simulated example of fatigue crack growth is used to demonstrate the proposed framework. The results demonstrate that permanently installed sensors with low susceptibility to systematic effects may be used to maintain confidence in fitness-for-service while relying on fewer inspections. The framework provides a method for using permanently installed sensors to achieve continuous assessments of fitness-for-service for improved integrity management.
“…Yang et al [22] proposed a method of crack prognosis and compared it to Extended Kalman Filtering (EKF), which illustrated the superiority of the PF-based approach. Additionally, Chen et al [23] used hole-edge crack specimens as a fatigue experiment object. According to the piezoelectric transducers (PZTs) active lamb, a method of online fatigue crack prognosis was presented, which adopted PF to deal with crack growth and monitor uncertainties.…”
Section: Introductionmentioning
confidence: 99%
“…Hol et al [27] compared four kinds of resampling methods and analyzed the pros and cons of the four resampling algorithms according to the perspectives of resampling quality, computational complexity, and uniform distribution. Crack growth is a complicated process; predictive models are often established using an approximate method [23], and prediction models inevitably produce errors. The choice of resampling method is an important step in improving prediction accuracy.…”
Fatigue crack prediction is a critical aspect of prognostics and health management research. The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth, and it is widely used to predict the state of fatigue crack. This paper proposes a method of lamb wavebased early fatigue microcrack prediction with the aid of particle filters. With this method, which the changes in signal characteristics under different fatigue crack lengths are analyzed, and the state-and observation-equations of crack extension are established. Furthermore, an experiment is conducted to verify the feasibility of the proposed method. The Root Mean Square Error (RMSE) of the three different resampling methods are compared. The results show the system resampling method has the highest prediction accuracy. Furthermore, the factors affected by the accuracy of the prediction are discussed.
“…Chen et al [16] established an ultrasonic guided wave and PF-based on-line crack propagation forecasting framework, in which DIs extracted from ultrasonic guided wave signals were considered in the standard PF approach. In [17][18][19], the PF was further improved to solve the particle impoverishment problem. In [20], an on-line renewal mechanism for amending SHM measuring equation dominated by Gaussian process was presented to reduce the posterior estimation error.…”
Due to the stochastic behaviour of fatigue crack propagation resulting from the multifarious uncertainties such as aleatory uncertainty and epistemic uncertainty, a synthetic scheme integrating heterogeneous uncertainty resources is needed to make the prognosis of fatigue crack propagation be accurate. In this paper, an on-line updating strategy combining an adaptively tunable hybrid radial basis function (RBF) network and active Lamb waves based structural health monitoring is proposed for prognosis of fatigue cracks in an aircraft wing. A particle filter and Brownian motion with drift and proportionality coefficients are put forward to dynamically access a posteriori estimation of crack state and model parameters. Because of the advantages of flexibility, electrically stabilized and suitable for complex structure, the sensor layer with an embedded network of distributed piezoelectric transducers is utilized to collect Lamb wave signals describing crack states on-line. A hybrid spatial phase difference-based damage index (DI) is employed to capture the characteristics of Lamb wave signals to quantitatively determine the crack length. Furthermore, fatigue tests on the outboard wing of a real airplane are conducted to substantiate the proposed method. The experimental results demonstrate that the hybrid RBF model can timely trap the dynamic characteristics of sensor signals, and effectively predict the varying tendency of cracks growth. The uncertainties during the prognosis of fatigue cracking propagation can be effectively dealt with by combining the dynamic adjustability and the DIs-based on-line monitoring technique.
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