Paper Info
Title
Expectation maximization approach to data-based fault diagnostics
Abstract
The data-based fault detection and isolation (DBFDI) process becomes more potentially challenging if the faulty component of the system causes partial loss of data. In this paper, we present an iterative approach to DBFDI that is capable of recovering the model and detecting the fault pertaining to that particular cause of the model loss. The developed method is an expectation-maximization (EM) based on forward-backward Kalman filtering. We test the method on a rotational drive-based electro-hydraulic system using various fault scenarios. It is established that the developed method retrieves the critical information about presence or absence of a fault from partial data-model with minimum time-delay and provides accurate unfolding-in-time of the finer details of the fault, thereby completing the picture of fault detection and estimation of the system under test. This in turn is completed by the fault diagnostic model for fault isolation. The obtained experimental results indicate that the developed method is capable to correctly identify various faults, and then estimating the lost information.
Year
DOI
Venue
2013
10.1016/j.ins.2012.01.031
Inf. Sci.
Keywords
Field
DocType
expectation maximization approach,various fault,model loss,fault isolation,various fault scenario,critical information,rotational drive-based electro-hydraulic system,data-based fault detection,data-based fault diagnostics,fault diagnostic model,fault detection,developed method,kalman filter,dynamical systems,expectation maximization
Data mining,Fault coverage,Control theory,Artificial intelligence,Fault model,Stuck-at fault,Automatic test pattern generation,System under test,Fault detection and isolation,Kalman filter,Mathematics,Machine learning,Fault indicator
Journal
Volume
ISSN
Citations 
235,
0020-0255
8
PageRank 
References 
Authors
0.57
8
2
Name
Order
Citations
PageRank
Magdi S. Mahmoud179098.50
Haris M. Khalid2183.88