Hybrid System State Tracking and Fault Detection Using Particle Filters

2006 ◽  
Vol 14 (6) ◽  
pp. 1078-1087 ◽  
Author(s):  
S. Tafazoli ◽  
X. Sun
Keyword(s):  
Fault Detection ◽  
Hybrid System ◽  
Particle Filters ◽  
System State ◽  
State Tracking

Bayesian hybrid automata: Reconciling formal methods with metrology

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Paul Kröger ◽  
Martin Fränzle
Keyword(s):  
System Dynamics ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Formal Model ◽  
Continuous Control ◽  
Hybrid Automata ◽  
Physical Processes ◽  
Natural Domain ◽  
State Tracking ◽  
Smart Technologies

Abstract Hybrid system dynamics arises when discrete actions meet continuous behaviour due to physical processes and continuous control. A natural domain of such systems are emerging smart technologies which add elements of intelligence, co-operation, and adaptivity to physical entities. Various flavours of hybrid automata have been suggested as a means to formally analyse dynamics of such systems. In this article, we present our current work on a revised formal model that is able to represent state tracking and estimation in hybrid systems and thereby enhancing precision of verification verdicts.

Engineering System Fault Detection Using Particle Filters

2009 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Fault Detection ◽  
Bayesian Learning ◽  
Particle Filters ◽  
State Space Model ◽  
Engineering System ◽  
Process Noise ◽  
Machine Vibration ◽  
Relationship Of ◽  
The Relationship ◽  
Train System

This paper presents an approach to machinery fault detection using particle filters (PF). The machine vibration signals are processed using morphological signal processing (MSP) to extract a novel entropy based health index (HI) characterizing the signal shape-size complexity. The evolution of HI is approximated as a nonlinear state space model using a computational intelligence (CI) technique. PF is used to estimate the progression of HI in presence of observation and process noise. The PF based approach is illustrated for estimation of state and parameters of a chaotic system. The feasibility of the approach is demonstrated through vibration dataset of a helicopter drive-train system gearbox. The results help understand the relationship of the system condition, the corresponding HI, the level of degradation and its progression in a stochastic environment using Bayesian learning.

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