International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 8- August 2013
A Novel Architecture of Perception-Based Decision
Making System for Fault Diagnosis of Complex
Systems
Dr. Tarun Chopra
Associate Professor, Department of Electrical Engineering, Govt. Engineering College Bikaner,
(India)-334004
Abstract— This research paper is motivated by the fact that
perception based information is important for fault diagnosis
applications, particularly for localizing the fault before explicit
diagnostic tests are carried out and computational models are
applied .
I. INTRODUCTION
analysis, e.g. auto-correlation functions, spectrum
analysis etc. But these methods do not allow an indepth fault diagnosis and do not simulate the human
reasoning activity. Most of these methods require
special sensors which increase costs and
maintenance effort. Further, they require extensive
experimentation and data acquisition, which is a
difficult task in complex systems.
Natural growth and technological advances have
led to development of physical, biological and
engineering systems with increasing size and
functionality along with increasing demands on
their performance. Hence, such systems have
increased complexity and enhanced potential to fail,
despite safe designs and improved quality control
techniques .Thus, system failures have become
unavoidable and a growing area of concern.
Therefore, modern methods, which rely heavily
on simulative analysis, have been developed in the
recent years. The main difficulty in applying
analytical models is the fact that mathematical
models available are generally imprecise. Therefore,
the most essential requirement for analytical modelbased fault detection and isolation is to provide
robustness to different kinds of unmodelled
disturbances and modelling errors.
Further, the growing complexity of industrial
plants, such as chemical, petrochemical, sugar and
power etc, has also resulted in serious problems in
process control which cannot be easily handled by
operators. Industrial statistics show that about 70%
of the industrial accidents are caused by human
errors [1].
In order to overcome these problems, the soft
computing methods [2] viz. neural networks, fuzzy
models, probabilistic and neuro-fuzzy models, etc.
have been investigated in recent years. These
techniques can model a much wider class of
nonlinear systems. Mathematical models used in the
traditional methods are potentially sensitive to
modelling errors, parameter variation, noise and
disturbances.
Mathematical
modelling
has
limitations, especially when the system is uncertain
and the data are ambiguous. Soft computing
methods have overcome them to some extent.
Keywords— Perception, DAMADICS
As a result, there has been an increasing interest
in fault diagnosis in recent years.
II. PROBLEM STATEMENT
Early automation systems were based on a simple
However, all of the soft computing models have
limit and trend value check. These were followed shown limited success in dealing with the problems
by the methods based on non-parametric signal of intelligent decision making where system
ISSN: 2231-5381
http://www.ijettjournal.org
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 8- August 2013
modelling is difficult and limited operational data is through user interface. Perception based rules are
available. The situation is further aggravated, when formulated by granulation of the measured
decision making has to be fast.
parameters. The primary fuzzy classifier takes
decision about the normal or fault condition on the
III. PROPOSED SOLUTION
basis of perception based rules.
The theory of perceptions has proved to be useful
in dealing with such situations [3]. Perception is a
powerful mental process for knowledge acquisition
from sensed data. The strength of perception lies in
the fact that it is capable of representing the
knowledge contained in information in a more
subtle manner as compared to measurement, which
quantifies it mathematically with attendant
limitations and approximations.
The use of perception based knowledge for
operation and control of complex systems paves the
way for implementation of epistemological decision
making. The aim of epistemological decision
making is to arrive at rational decisions based not
only on merely numerical data but also on the
conceptual knowledge contained in the data.
The perception based information in natural
language will help operators, in process engineering
involving decision making procedure with analysis
of time series databases, to recall a similar looking
situation from the past experience associated with a
known fault and recommend corresponding
diagnostic tests. Thus, the operators will not be
required to consider an exhaustive set of diagnostic
tests but just the most probable ones. Inference
procedures of Computational Theory of Perceptions
can be used for efficient modeling of human
perception-based reasoning mechanisms.
FIGURE 1: PROPOSED ARCHITECTURE
BASED DECISION M AKING
OF
PERCEPTION-
However, there may be some misclassified cases
due to close resemblance of incipient fault
parameters with those of normal operation. Hence,
to further ascertain the correctness of diagnosis of
Normal or Fault condition, Secondary level
Decision making system has been proposed. It
confirms about the Normal or Fault state of
operation by taking into account epistemic rules
formulated on the basis of granulation of trend.
Output from both the stages is fed to Interval type-2
fuzzy classifier[4], which further separates Normal
condition from Fault condition and abrupt fault
from incipient fault, respectively. The fault cases
thus detected are again fed back to secondary
decision making for fault condition so as to have
confirmation about it being incipient fault or
otherwise.
A possible architecture of perception-based
decision making system has been proposed in this
paper. The proposed system as shown in Figure 1 is
relevant to decision making problems like fault
diagnosis and has been applied for diagnosis of
different types of faults in actuator of evaporator
section of sugar industry.
As depicted in Figure 1, plant data obtained from
sensor measurements is given to the Primary Level
IV. CONCLUSIONS
Decision Making System. Diagnostic information
obtained from experts is also fed to this System New approaches for decision making involving
fault diagnosis and intelligent control of complex
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 8- August 2013
systems have emerged recently. These techniques
often use expert systems, fuzzy logic, neural
networks etc. but they all suffer from the limitation
that a single approach is inadequate for meeting
with all the challenges posed by a complex system.
A general perception based epistemological
framework is, hence, ardently required to be
established for this field. This work has attempted
to provide such a framework and brought into focus
the significant issues of epistemological decision
making in complex systems which is emerging as a
fertile research area.
REFERENCES
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[2]
[3]
[4]
Venkatasubramanian et al, “A review of process fault detection and
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Chemical Engineering 27, 2003, 293-311
Witczak M., “Modelling and estimation strategies for fault diagnosis of
non-linear systems: From analytical to soft computing approaches”,
Lecture notes in control and information sciences Vol. 354, Springer,
Berlin, Germany, 2007.
Zadeh L.A., “From computing with numbers to computing with
words—from manipulation of measurements to manipulation of
perceptions”, IEEE Transactions on Circuits and Systems 45, 1999,
105–119.
Karnik N. N. and Mendel J. M., “Introduction to Type-2 Fuzzy Logic
Systems,” presented at IEEE FUZZ Conf., Anchorage, AK, May 1998
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