Download Temporal Bayesian Networks of Events for Fault Diagnosis and

Arroyo-Figueroa et al.
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Temporal Bayesian Network of Events for Fault Diagnosis and Prediction
in Thermal Power Plants
Gustavo Arroyo-Figueroa
Instituto de Investigaciones Electricas
P.O. Box 475-1, 62001 Cuernavaca, Mor., Mexico
email: [email protected]
L. Enrique Sucar
ITESM-Campus Cuernavaca
P.O. Box C-99
62020 Cuernavaca, Morelos, Mexico
email: [email protected]
Introduction
The last 15 years the operating conditions of thermal power plants have changed. Today, the
operation of thermal power plants must be optimal considering higher productions profits, safer
operation and stringent environment regulation. An additional factor is the increment of the age
of the plants. The reliability and performance of the plants is affected by its age. This means an
increase in the number of equipment failures, thus increasing the number of diagnoses and
control decisions which the human operator must make. Under this conditions, the complexity
of the operation of thermal power plants have been increased significantly.
As a result of these changes, computer and information technology have been extensively used
in thermal plant process operation. Distributed control systems (DCS) and management
information systems (MIS) have been playing an important role to shows the plant status.
However, in non-routine operations such as equipment failures and extreme operation (star up
phase, changes in the load, etc.), human operators have to rely on their own experience. During
disturbances, the operator must determine the best recovery action according to the type and
sequence of the signals received. In a major upset, the operator may be confronted with a large
number of signals and alarms, but very limited help from the system, concerning the underlying
plant condition. Faced with vast amount of raw process data, human operators find it hard to
make timely and effective solutions.
The process industry demands new computer integrated technologies that reduce the operator’s
working burden, by providing operation support systems. Process operations are knowledgeintensive work task because thermal plants are large, complex and influenced by unexpected
disturbances and events over the time. Artificial Intelligence (AI) has been considered
promising to deal with problems that require human expertise and heuristic knowledge. The
Electrical Research Institute of Mexico (IIE), and the Monterrey Institute of Technology
(ITESM) have been working in various projects related to operational support of thermal power
plants, such as sensor validation [Ibargüengoytia, 98], diagnostic systems for complex
operations [Arroyo, 99], operational planning systems [Ibargüengoytia, 01], and real time
advisory control systems [Arroyo, 00].
Diagnosis and Prediction System
We are developing an intelligent system, based on network of probabilistic events in discrete
time, for fault diagnosis and prediction in thermal power plants. The framework is based on a
novel methodology for dealing with uncertainty and time called Temporal Nodes Bayesian
Networks [Arroyo, 99]. In a TNBN each variable represents an event that can take place at most
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once. Time is discretized by adopting the appropriate temporal unit for each case (seconds,
minutes, etc); therefore, the temporal granularity depends on the particular problem. Temporal
reasoning is based on probability propagation and gives the time occurrence of the events or
state changes with some probability value. The main difference with dynamic Bayesian
networks, is that representation is based on state changes instead of state values at different
times. This makes the model much simpler in many applications in which there are few changes
for each variable in the temporal range of interest.
The proposed network of probabilistic events in discrete time is applied for fault diagnosis and
prediction of events in the drum level control system of a thermal power plant. The following
figure shows the TNBN that represents the events and their prior probabilities in this
application.
FWPF
Occur=058
¬Occur=0.42
FWVF
Occur=0.57
¬Occur=0.43
SVWF
Occur=0.18
¬Occur=0.82
FWPF
FWVF
SWV
True,[28-41]=0.30
True,[41-66]=0.27
False,[28-66]=0.43
FWP
FWV
SVWF
FWF
SWV
SWF
True,[108-170]=0.75
True,[170-232]=0.21
False,[108-232]=0.04
LI
Occur=0.88
¬Occur=0.12 FWP
True,[10-29]=0.36
True,[29-107]=0.57
LI
False,[10-107]=0.07
SWF
FWF
True,[25-114]=0.77
True,[114-248]=0.18
False,[25-248]=0.05
DRP
True,[30-70]=0.58
True,[70-96]=0.40
False,[30-96]=0.02
STV
STF
DHL
DRP
STT
STV
True,[0-18]=0.69
True,[18-29]=0.20
False,[0-29]=0.11
STF
True,[52-72]=0.36
True,[72-105]=0.57
False,[52-105]=0.07
DHL
Increment,[10-27]=0.49
Increment,[27-135]=0.09
Decrement,[22-37]=0.28
Decrement,[37-44]=0.12
False,[10-135]=0.02
STT
Decrement,[10-42]=0.37
Decrement,[42-100]=0.14
Decrement,[100-272]=0.47
False,[10-272]=0.02
STV = steam valve opening increase STF = steam flow increase
SWV = spray valve opening increase SWF = spray water flow increase
DRL = drum level (DRL)
DRP = drum pressure decrement
STT = steam temperature.
Preliminary Results
The TNBN model of the drum level control system has been implemented and
evaluated using a power plant simulator. The following table shows the result of the
evaluation of 972 registers. In each test some of the variables are instantiated, and the
other estimated, considering 3 cases: diagnosis, the bottom nodes are known,
prediction, the top nodes are known, diagnosis and prediction, bottom and top nodes
are known. The model was evaluated empirically using two scores: accuracy and a
measure based on the Brier score (total square error, RBS) [Arroyo 99], and the results
are shown in the following table. In general, the results are better for prediction than for
diagnosis.
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Parameter


Prediction
% of RBS
% of Accuracy
87.37
84.48
9.19
14.98
Diagnosis
% of RBS
% of Accuracy
84.25
80.00.
8.09
11.85
Diagnosis and
Prediction
% of RBS
% of Accuracy
95.85
94.92
.
4.71
8.59
Future Work
An important problem is the construction of this type of models. For this experiment, the model
and the time intervals for each node were defined by a an expert, but this is a very time
consuming process. So an issue for future work is how to learn the model from data, including
the structure and temporal intervals. Another issue that has to be consider to use this model for
helping the operator, is how to translate the probabilities obtained for each event in advice to the
operator.
References
G. Arroyo-Figueroa., L. E. Sucar, (1999). “Temporal Bayesian Network for diagnosis and
prediction”. In Laskey K., Prade H.: Proc. 15th Conference on Uncertainty on Arti-ficial
Intelligence, 13-20.
G. Arroyo-Figueroa., L. E. Sucar and A. Villavicencio, (2000). “Fuzzy Intelligent system for the
operation of fossil power plants”, Engineering Applications of Artificial Intelligence, 13, 431439.
P. Ibargüengoytia, L.E. Sucar, S. Vadera, (1998). “Any-time probabilistic reasoning for sensor
validation”, In Proc. 14th Conference on Uncertainty in Artificial Intelligence, Madison,
Morgan-Kaufmann, San Mateo, CA, 266-273.
P. Ibarguengoytia and Alberto Reyes (2001). “Continuous Planning for the Operation of Power
Plants”, In Proc. 3er Encuentro Nacional de Computación, Aguascalientes, Mexico, 199-208.