Sequential Monte-Carlo sampling based on a committee of artificial neural networks for posterior state estimation and residual lifetime prediction

Sbarufatti, Claudio; Corbetta, Matteo; Manes, Andrea; Giglio, Marco

doi:10.1016/j.ijfatigue.2015.05.017

Cited by 37 publications

(21 citation statements)

References 21 publications

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“…The ANNs of the committee are trained off‐line on the basis of numerical examples generated by simulating the presence of cracks of different lengths and at different locations, and computing the corresponding modifications of the strain field at the sensors locations by means of finite element model (FEM) calculations. Note that, as demonstrated in other studies, this training procedure is such that the ANN‐based reconstruction of the crack length does not need any information on the position of the crack relative to the sensor grid.…”

Section: Introductionmentioning

confidence: 94%

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

Cadini

Sbarufatti

Corbetta

et al. 2017

Struct Control Health Monit

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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.

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Section: Introductionmentioning

confidence: 94%

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

Cadini

Sbarufatti

Corbetta

et al. 2017

Struct Control Health Monit

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“…Resampling aims to prevent the propagated particles' degeneracy by altering the random measure X t to X t and enhancing the state space examination at t + 1. While addressing degeneracy during resampling, it is also important for the random measure to approximate the original distribution as precisely as possible so that bias in the estimates can be prevented [40][41][42][43]. Although the X t approximation closely resembles that of X t , the set of X t particles have important variations from that of X t .…”

Section: Resamplingmentioning

confidence: 99%

Adaptive memory-based single distribution resampling for particle filter

et al. 2017

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IntroductionOver the past two decades, particle filtering has emerged as a procedure for sequential signal processing (Refs. [1][2][3][4] presents the review). Particle filter has also become popular because it is capable of processing observations signified by nonlinear state-space models. In such models, the noises can be non-Gaussian. Several fields have adopted this methodology including: finance [5][6][7][8], wireless communications [9][10][11][12], geophysical systems [13][14][15][16][17], navigation and tracking [18][19][20], control [21][22][23][24][25], and robotics [26][27][28][29][30][31]. Generally, this methodology can approximate state density p(x k ) using a range of random particles that have related nonnegative weights: AbstractThe restrictions that are related to using single distribution resampling for some specific computing devices' memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices' memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices' physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle's particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.

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“…Especially, particle filter algorithms are able to update not only the system states but also the probability distributions of parameters in the equations representing the system and measurement process, without tailored re-formulations [ 17 ]. Therefore, including online parameter identification [ 15 , 18 , 19 ] as a part of particle filter applications has been widely practiced. In this paper, we propose utilizing this parameter-updating feature of the particle filter for the purpose of monitoring and prediction of structural deterioration.…”

Section: Introductionmentioning

confidence: 99%

Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses

Song

2018

Sensors

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Prediction of structural deterioration is a challenging task due to various uncertainties and temporal changes in the environmental conditions, measurement noises as well as errors of mathematical models used for predicting the deterioration progress. Monitoring of deterioration progress is also challenging even with successive measurements, especially when only indirect measurements such as structural responses are available. Recent developments of Bayesian filters and Bayesian inversion methods make it possible to address these challenges through probabilistic assimilation of successive measurement data and deterioration progress models. To this end, this paper proposes a new framework to monitor and predict the spatiotemporal progress of structural deterioration using successive, indirect and noisy measurements. The framework adopts particle filter for the purpose of real-time monitoring and prediction of corrosion states and probabilistic inference of uncertain and/or time-varying parameters in the corrosion progress model. In order to infer deterioration states from sparse indirect inspection data, for example structural responses at sensor locations, a Bayesian inversion method is integrated with the particle filter. The dimension of a continuous domain is reduced by the use of basis functions of truncated Karhunen-Loève expansion. The proposed framework is demonstrated and successfully tested by numerical experiments of reinforcement bar and steel plates subject to corrosion.

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Sequential Monte-Carlo sampling based on a committee of artificial neural networks for posterior state estimation and residual lifetime prediction

Cited by 37 publications

References 21 publications

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

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

Adaptive memory-based single distribution resampling for particle filter

Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses

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