A particle filter method for damage location in plate-like structures by using Lamb waves

Summary The early diagnosis of cracks in aeronautical structures is a fundamental task for the safe system operation and the optimization of maintenance policies, in view of the increasing interest in life extension programs of several high‐investment industries. In principle, these tasks could be fulfilled within a condition‐based framework, where direct or indirect observations of the degradation evolution are processed, possibly in real time, by proper diagnostic computational tools. In the past, several attempts have been made to build real‐time monitoring systems collecting strain signals acquired from sensor networks. However, in real applications, some issues remain unresolved, for example, the large number of observations available to be handled within a unique diagnostic framework, their relationship with the underlying crack size, and their typical large uncertainties. In this paper, we provide a practical solution by innovatively combining a particle filtering‐based model identification algorithm with a measurement system exploiting real‐time observations of the crack length reconstructed by a committee of artificial neural networks. The artificial neural networks are trained by simulated strain fields generated by a finite element model. The resulting tool allows to perform an automatic, simultaneous, and real‐time (a) selection of the model more properly describing the system state evolution, so as to detect the crack propagation onset time, (b) estimation of the model parameters, and (c) estimation of the crack length, within a unique probabilistic framework based on particle filtering. The methodology is demonstrated with reference to a real helicopter panel subject to fatigue and equipped with a fiber Bragg grating sensor network.

Section: Introductionmentioning

confidence: 99%

A particle filter-based model selection algorithm for fatigue damage identification on aeronautical structures

Cadini

Sbarufatti

Corbetta

et al. 2017

“…The application of Bayesian inference to the AE source localization problem has gained significant attention in recent years, mainly due to its capability to quantify the uncertainty in the estimates, accuracy, and robustness to modeling errors. () In this paper, particle‐based stochastic filters are applied to estimate AE sources in plate‐type structures in a Bayesian framework; stochastic filters are typically used in online state estimation and damage estimation, and in particular, particle‐based methods are useful when the models involved are highly nonlinear. () The use of particle‐based filters results in an increased accuracy in the estimates of the parameters and their uncertainty due to their ability to accurately characterize the posterior distribution.…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation of acoustic emissions source in plate structures using particle-based stochastic filtering

Sen

Erazo

Nagarajaiah

2017

Summary The application of particle‐based stochastic filters to acoustic emission source localization in plate structures is presented. The approach employs time‐of‐flight measurements of guided waves using triangulation to estimate the acoustic emission source coordinates in a probabilistic framework using Bayesian inference, incorporating uncertainties related to material properties, measurement noise, and geometry of the system of interest. The estimate of the source location is given by a probability density function conditional on the guided wave measurements, found using particle‐based stochastic simulation algorithms; in this setting, a set of particles is used to explore the space of possible source locations and efficiently estimate the posterior. The use of 2 filters is explored: the ensemble Kalman filter and the particle filter. The former filter assumes that the posterior distribution can be approximated by a Gaussian distribution, although the latter provides a nonparametric estimate of the posterior in the form of a weighted set of samples, overcoming the challenges related to the evaluation of high‐dimensional integrals in an efficient way. Results of an experimental validation study conducted in a laboratory environment demonstrate the accuracy and efficiency of the particle filter‐based approach. In particular, it is shown that the proposed particle filter‐based approach has the capability to locate the emission source under minimal instrumentation, providing confidence intervals as a quantitative measure of the uncertainty in the estimates.

“…In situ structural health monitoring (SHM) provides a new method for online evaluation of the integrity and safety of civil structures and providing alert at the early stage of critical damage . Typically, an integrated SHM system consists of transducers mounted on the surface or embedded in structures, associated damage identification algorithm, and supporting hardware devices .…”

Section: Introductionmentioning

confidence: 99%

“…Because of these excellent characteristics, damage detection methods based on Lamb wave are widely researched, and nondestructive testing method using Lamb wave has a long history . Piezoceramic transducers are often used to induce and receive Lamb wave . Yuan et al proposed an active damage detection method for composite structures by using a damage index, which could compensate the influence of the distance between different transducers.…”

Section: Introductionmentioning

confidence: 99%

A novel time reversal sub-group imaging method with noise suppression for damage detection of plate-like structures

Gao

Zhang

et al. 2017

Summary In this paper, a new time reversal imaging (TRI) algorithm with noise suppression is developed for the application of imaged based structural damage detection of plate‐like structures. The conventional TRI method suffers from performance degradation in high noise condition. The proposed method addresses the aforementioned issues. First, an array of detection transducers is used and is divided into several subgroups. Then, the echo signals of the subgroups are time reversed and reemitted via numerical computation. Finally, the cross‐correlation functions of the summation of refocused time reversed signals in each subgroup are obtained to locate damages. The time reversed signals at the reference time are irrelevant to the noise, meanwhile, the multiple refocused signals in each subgroup are first added and then cross‐correlated. Therefore, the proposed method can effectively suppress noise. To validate the effectiveness of proposed method, 2 experiments were performed. The 2 experiments involved 2 aluminum plate specimens. Each specimen was equipped with 4 surface‐bonded lead zirconate titanate transducers. One specimen involved a simulated damage (an addition of a mass), and the other one involved an actual through‐hole damage. The experimental performances of the proposed method are compared to those of the conventional TRI method. The imaging results demonstrated that the damage on both specimens was clearly displayed with high spatial resolution by the proposed method even under the low signal‐to‐noise ratio condition. On the contrary, the location of the damage computed by the conventional TRI method was submerged in noise and cannot be distinguished.