05-07-2012, 02:23 PM
On-Line Fault Detection Techniques for Technical Systems: A Survey
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INTRODUCTION
There has been an increasing interest in fault detection in recent years, as a result of the
increased degree of automation and the growing demand for higher performance,
efficiency, reliability and safety in industrial systems. Diagnosis can be a complex
reasoning activity, which is currently one of the domains where Artificial Intelligence
techniques have been successfully applied. The reason is that these techniques use
association, reasoning and decision making processes as would the human brain in solving
diagnostic problems.
Classical fault detection methods are based on limit value checking of some important
measurable variables and a lot of valuable research work has been done in this direction.
These methods do not allow an in-depth fault diagnosis and do not simulate the human
reasoning activity.
NUMERICAL METHODS
The classical way for detecting faults consists of checking the measurable variables of a
system in regard to a certain tolerance of the normal values and triggering alarm messages
if the tolerances are exceeded or taking an appropriate action when they exceed a limit
value which signifies a dangerous process.
Fault detection and isolation schemes are basically signal processing techniques employing
state estimation, parameter estimation, adaptive filtering, variable threshold logic, statistical
decision theory and analytical redundancy methods.
ARTIFICIAL INTELLIGENCE METHODS IN FAULT DETECTION
In the case of very complex time-varying and non-linear systems, where reliable
measurements are very complicated and valid mathematical models do not exist, a number
of different methods have been proposed by researchers. These methods come from the
area of Artificial Intelligence and allow the development of new approaches to fault
detection in dynamical systems.
Qualitative simulation in on-line Fault Detection
In this method fault detection is performed by comparing the predicted behaviour of a
system based on qualitative models with the actual observation. Qualitative models of
normal and faulty equipment are simulated to describe the range of possible behaviours of
the operation of a system without numeric models. The modelling of physical situations
contains a set of qualitative equations derived from a set of quantitative equations or from
C. Angeli, A. Chatzinikolaou 17
qualitative descriptions about relationships among the process variables and contains
knowledge about structure, function and behaviour. Sensor data from actual processes are
used to select between the different developed models. The fault diagnosis is realised by
matching between predicted and observed behaviour.