Journal ArticleDOI
Practical issues in state estimation using particle filters: Case studies with polymer reactors
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TLDR
In this article, the unscented Kalman filter (UKF) and the particle filter (PF) were compared for the case of significant plant-model mismatch, and the PF was shown to be less robust than the Kalman update-based filters.About:
This article is published in Journal of Process Control.The article was published on 2013-02-01. It has received 37 citations till now. The article focuses on the topics: Extended Kalman filter & Unscented transform.read more
Citations
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Journal ArticleDOI
Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System
Shen Yin,Xiangping Zhu +1 more
TL;DR: In this paper, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed, inspired from the genetic algorithm, which mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF.
Journal ArticleDOI
State Estimation in Nonlinear System Using Sequential Evolutionary Filter
TL;DR: A novel particle filtering technique named sequential evolutionary filter (SEF) is introduced, by which the particle impoverishment problem can be effectively mitigated, and the particle diversity can be maintained.
Journal ArticleDOI
Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes
TL;DR: An adaptive neuro-fuzzy inference system is designed to learn the state transition function in the fault degradation model using the fault indicator extracted from the monitoring data; a particle modification method and an improved multinomial resampling method are proposed to improve the particle diversity in the resamplings process to solve the particle impoverishment problem.
Journal ArticleDOI
Target tracking algorithm based on adaptive strong tracking particle filter
TL;DR: Based on the particle filter, an adaptive strong tracking particle filter algorithm is proposed in this paper, according to the residual between actual measurement values and predicted measurement values of every moment, adjustment of the forgetting factor and the weakening factor is adaptively conducted.
Journal ArticleDOI
Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities
Weijun Li,Hui Li,Sai Gu,Tao Chen +3 more
TL;DR: A review of widely used model- and knowledge-based diagnostic methods, including their general ideas, properties, and important developments, that evaluate their performance in real processes in process industry, including the process types, scales, considered faults, and performance.
References
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Journal ArticleDOI
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Book
Finding Groups in Data: An Introduction to Cluster Analysis
TL;DR: An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.
BookDOI
Finding Groups in Data
TL;DR: In this article, an electrical signal transmission system for railway locomotives and rolling stock is proposed, where a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count, and a spike pulse of greater selected amplitude is transmitted, occurring immediately after the axle count pulse to which it relates, whenever an overheated axle box is detected.
BookDOI
Sequential Monte Carlo methods in practice
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Related Papers (5)
Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System
Shen Yin,Xiangping Zhu +1 more