Expectation maximization approach to data-based fault diagnostics

Authors:
Magdi S. Mahmoud;Haris M. Khalid
Affiliations:
Systems Engineering Department, King Fahd University of Petroleum and Minerals, P.O. Box 5067, Dhahran 31261, Saudi Arabia;Systems Engineering Department, King Fahd University of Petroleum and Minerals, P.O. Box 5067, Dhahran 31261, Saudi Arabia
Venue:
Information Sciences: an International Journal
Year:
2013

Citing 4
Cited 2

Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results

Automatica (Journal of IFAC)
Diagnosis and Fault-Tolerant Control

Diagnosis and Fault-Tolerant Control
Isolation and handling of actuator faults in nonlinear systems

Automatica (Journal of IFAC)
An EM-Based Forward-Backward Kalman Filter for the Estimation of Time-Variant Channels in OFDM

IEEE Transactions on Signal Processing - Part II

Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis

Information Sciences: an International Journal
Quantifying the reliability of fault classifiers

Information Sciences: an International Journal

Quantified Score

Hi-index	0.07

Visualization

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.