Particle Filtering Algorithm for Fault Diagnosis of Multiple Model Hybrid Systems with Incomplete Models

2008 ◽  
Vol 34 (5) ◽  
pp. 581-587 ◽  
Author(s):  
Zhuo-Hua DUAN
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Systems ◽  
Particle Filtering ◽  
Multiple Model ◽  
Filtering Algorithm ◽  
Incomplete Models

A Particle Filtering-Based Approach for Distributed Fault Diagnosis and Estimation of Multi-Robot Systems

2016 ◽  
Author(s):  
Elaheh Noursadeghi ◽  
Ioannis Raptis
Keyword(s):  
Fault Diagnosis ◽  
Particle Filtering ◽  
Estimation Algorithm ◽  
Parameter Learning ◽  
Reduced Order ◽  
Filtering Algorithm ◽  
Progression Model ◽  
Robot Systems ◽  
Diagnostic Nodes ◽  
Multi Robot

This paper deals with the problem of designing a distributed fault diagnosis and estimation algorithm for multi-robot systems that are subject to faults in the form of abrupt velocity biases. To solve this problem, the multi-robot collective is converted to a network of interconnected diagnostic nodes (DNs) that is deployed to monitor the health of the system. Each node consists of a reduced-order estimator with relative state measurements and an online parameter learning filter. The local estimator executes a distributed variation of the particle filtering algorithm using the local sensor measurements and the fault progression model of the robots. The parameter learning filter is used to obtain an approximation of the severity of faults. Numerical simulations demonstrate the efficiency of the proposed approach.

scholarly journals Mixed approach for fault diagnosis and fault location of hybrid systems

2016 ◽  
Vol 49 (12) ◽  
pp. 1002-1007 ◽  
Author(s):  
B. Maaref ◽  
Z. Simeu Abazi ◽  
H. Dhouibi ◽  
H. Messaoud ◽  
E. Gascard
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Systems ◽  
Fault Location ◽  
Mixed Approach

The Improved Particle Filtering Algorithms for Tracking the Signals

2011 ◽  
Vol 403-408 ◽  
pp. 2341-2344
Author(s):  
Xiu Ying Zhao ◽  
Hong Yu Wang ◽  
Shou Yu Tong ◽  
De You Fu
Keyword(s):  
Kalman Filtering ◽  
Particle Filtering ◽  
Sequential Monte Carlo ◽  
Stochastic Methods ◽  
Sequential Importance Sampling ◽  
Robust Tracking ◽  
Random Set ◽  
Filtering Algorithm ◽  
Alternative Hypotheses ◽  
Visual Clutter

The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The PF(Particle Filtering) algorithm uses “sequential importance sampling”, previously applied to the posterior of static signals, in which the probability distribution of possible interpretations is represented by a randomly generated set. PF uses learned “sequential Monte Carlo” models, together with practical observations, to propagate and update the random set over time. The result is highly robust tracking of agile motion. Not withstanding the use of stochastic methods, the algorithm runs in near Real-Time.

Sign in / Sign up

Export Citation Format

Share Document