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Particle filter-based fatigue damage prognosis by fusing multiple degradation models

Li, Tianzhi (author), Chen, Jian (author), Yuan, Shenfang (author), Zarouchas, D. (author), Sbarufatti, Claudio (author), Cadini, Francesco (author)

Fatigue damage prognosis always requires a degradation model describing the damage evolution with time; thus, the prognostic performance highly depends on the selection of such a model. The best model should probably be case specific, calling for the fusion of multiple degradation models for a robust prognosis. In this context, this paper...

journal article 2024

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Probabilistic Labeling in Radar Track-before-Detect Processing: Algorithms for tracking closely-spaced and/or interacting targets

Moreno León, C. (author)

Radar-tracking of low-observable targets such as drones suffers from low detection performance. In these type of applications, it is desirable to avoid data thresholding in order to preserve the weak target signal in the raw sensor data. This thesis considers the Multiple Object Tracking (MOT) problem in the context of radar Track-before-Detect ...

doctoral thesis 2023

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Mechanics Informed Approach to Online Prognosis of Composite Airframe Element: Stiffness Monitoring with SHM Data and Data-Driven RUL Prediction

Yue, N. (author), Galanopoulos, Georgios (author), Loutas, Theodoros (author), Zarouchas, D. (author)

During the service of composite airframes, damage initiates and accumulates due to the manufacturing imperfections, impact damage and cyclic loadings, leading to the degradation in its load-bearing capacity. The nature of the degradation process is complicated due to the multi-mode damage propagation and complexity in the structural details...

conference paper 2023

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Particle filter–based data assimilation in dynamic data-driven simulation: sensitivity analysis of three critical experimental conditions

Huang, Yilin (author), Xie, Xu (author), Cho, Yubin (author), Verbraeck, A. (author)

Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the...

journal article 2022

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Indoor SLAM using a foot-mounted IMU and the local magnetic field

Osman, M.E.A. (author), Viset, F.M. (author), Kok, M. (author)

In this paper, a simultaneous localization and mapping algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit is proposed. The algorithm uses two maps, namely, a motion map and a magnetic field map. The motion map captures typical motion patterns of pedestrians in buildings that are constrained by e.g....

conference paper 2022

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Data Assimilation in High Dimensional Systems Using Local Particle Filters: Overcoming the curse of dimensionality in hydrology

Wang, Z. (author)

This dissertation's ultimate goal is to provide solutions to two problems that the promising data assimilation method, called the Particle Filter, has when applied to high dimensional non-linear models, such as those often used in hydrological research and forecasting. Two local particle filters have been proposed to overcome three major issues....

doctoral thesis 2021

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Online model-based remaining-useful-life prognostics for aircraft cooling units using time-warping degradation clustering

Mitici, M.A. (author), de Pater, I.I. (author)

Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are...

journal article 2021

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Sample Regenerating Particle Filter Combined With Unequal Weight Ensemble Kalman Filter for Nonlinear Systems

Li, X. (author), Cheng, Ai Jie (author), Lin, H.X. (author)

We present an approach which combines the sample regenerating particle filter (SRGPF) and unequal weight ensemble Kalman filter (UwEnKF) to obtain a more accurate forecast for nonlinear dynamic systems. Ensemble Kalman filter assumes that the model errors and observation errors are Gaussian distributed. Particle filter has demonstrated its...

journal article 2021

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Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Cho, Yubin (author), Huang, Yilin (author), Verbraeck, A. (author)

Dynamic data-driven simulation (DDDS) incorporates real-time measurement data to improve simulation models during model run-time. Data assimilation (DA) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical...

conference paper 2020

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A Local Particle Filter Using Gamma Test Theory for High-Dimensional State Spaces

Wang, Z. (author), Hut, R.W. (author), van de Giesen, N.C. (author)

Particle filters are non-Gaussian filters, which means that the assumption that the error distribution of the ensemble should be Gaussian is unnecessary. Like the ensemble Kalman filter, particle filters are based on the Monte Carlo approximation to represent the distribution of model states. It requires a substantial number of particles to...

journal article 2020

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Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

Ma, Hua (author), Smal, I.V. (author), Daemen, Joost (author), Walsum, Theo van (author)

Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the...

journal article 2020

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Particle filter for aircraft mass estimation and uncertainty modeling

Sun, Junzi (author), Blom, H.A.P. (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)

This article investigates the estimation of aircraft mass and thrust settings of departing aircraft using a recursive Bayesian method called particle filtering. The method is based on a nonlinear state-space system derived from aircraft point-mass performance models. Using only aircraft surveillance data, flight states such as position,...

journal article 2019

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Aircraft Mass and Thrust Estimation Using Recursive Bayesian Method

Sun, Junzi (author), Blom, H.A.P. (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)

This paper focuses on estimating aircraft mass and thrust setting using a recursive Bayesian method called particle filtering. The method is based on a nonlinear state-space system derived from aircraft point-mass performance models. Using solely ADS-B and Mode-S data, flight states such as position, velocity, and wind speed are collected and...

conference paper 2018

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A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

Xie, X. (author), van Lint, J.W.C. (author), Verbraeck, A. (author)

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from...

journal article 2018

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Data-driven hazardous gas dispersion modeling using the integration of particle filtering and error propagation detection

Zhu, Zhengqiu (author), Qiu, S. (author), Chen, Bin (author), Wang, Rongxiao (author), Qiu, Xiaogang (author)

The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order...

journal article 2018

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An Ultrasonic Sensor for Human Presence Detection to Assist Rescue Work in Large Buildings

van Groeningen, T. (author), Driessen, J.N. (author), Söhl, J. (author), Voûte, R.L. (author)

When the fire brigade arrives at a burning building, it is of vital importance that people who are still inside can quickly be found. Smart buildings should be able to expose this location data to the fire brigade working in a smart city. In this paper the feasibility is researched of using ultrasonic sound sensors for human presence detection...

journal article 2018

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A particle filter-based data assimilation framework for discrete event simulations

Xie, X. (author), Verbraeck, A. (author)

With the advent of new sensor technologies and communication solutions, the availability of data for discrete event systems has greatly increased. This motivates research on data assimilation for discrete event simulations that has not yet fully matured. This paper presents a particle filter-based data assimilation framework for discrete event...

journal article 2018

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Nonlinear State and Parameter Estimation for Hopper Dredgers

Stano, P.M. (author)

A Trailing Suction Hopper Dredger (TSHD) is a ship that excavates sediments from the sea bottom while sailing. In situ material is excavated with a special tool called the Drag-Head, then it is hydraulically transported through a pipe to the hopper where it is temporarily stored. After the dredging is completed the collected material is...

doctoral thesis 2013

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A Buried Vertical Filter for Micro and Nanoparticle Filtration

Li, S.J. (author), Shen, C. (author), Sarro, P.M. (author)

This paper presents a silicon micromachined filter for micro- and nanoparticles. The filter is vertical and completely buried beneath the surface. The buried aspect allows additional features to be integrated above the filter, while the vertical aspect allows the creation of highly uniform pores and efficient use of die area. The filter is...

journal article 2011

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The ensemble particle filter (EnPF) in rainfall-runoff models

Van Delft, G. (author), El Serafy, G.Y. (author), Heemink, A.W. (author)

Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the particle filter (PF) both have their own...

journal article 2009