3. IJMPERD - CONDITION MONITORING OF TURBO GENERATORS …
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www.tjprc.org [email protected] CONDITION MONITORING OF TURBOGENERATORS OF A THERMAL POWER PLANT USING FUZZY LOGIC CH. SAI AMULYA 1 & M. SRINIVASA RAO 2 1 M.Tech Student, Department of Mechanical Engineering, GMRIT, Rajam, Srikakulam, Andhra Pradesh ,India 2 Professor, Department of Mechanical Engineering, GMRIT, Rajam, Srikakulam, Andhra Pradesh, India ABSTRACT Turbogenerator is one of the main components in thermal power plant and its maintenance plays avital role in many applications. Condition monitoring is the process of determining the condition of machinery while in operation which lessens the probability of failure by allowing the maintenance to be scheduled.In this work,vibration monitoring analysis of steam turbo generators is studied. In this study Fuzzy Logic Toolbox in MATLAB is used for analysing the health condition of the turbo generators considering displacement and velocity as parameters.For this purpose, a thermal power plant (TPP) of the Visakhapatnam Steel Plant has been chosen as a case study.This type of analysis is very useful for assessing the condition of complex systems of thermal power plants. KEYWORDS:Condition Monitoring, Vibration Analysis, Thermal Power Plant, Fuzzy Logic, Health Condition Received: Jun 19, 2016; Accepted: Jul 14, 2016; Published: Jul 19, 2016; Paper Id.: IJMPERDAUG20163 INTRODUCTION A condition monitoring program plays an essential role in industries that have plant machinery. It gives the health condition of the components of the power system. There are various types of condition monitoring techniques depending on the industry and application which include vibration analysis, signal processing, analysis, acoustic analysis, etc. Vibration condition monitoring analysis is carried out on Turbogeneratorsin TPP, Visakhapatnam Steel plant. Bently Nevada 3500 series condition monitoring vibration system is used to measure the vibration. Cheng et al.[1] stated that fuzzy distributions were used instead of the classical probability distribution for the components, and calculate the fuzzy numbers to solve the fuzzy system reliability via non-linear programming techniques. He demonstrated that this approach and its computation algorithm are efficient and simple to implement.Shaaban et.al[2] proposed a decision tree method to improve steam turbine generator fault diagnosis. He stated that the method is an effective fault data disposal method for steam turbine generator fault diagnosis. Pavan Kumar et al.[3] stated that vibration-monitoring technique is very powerful tool for assessing the condition of rotary machine and if used with proper precautionary methods it can reduce the breakdowns of power plants. Lun et al.[4] described a reliable condition monitoring system to prevent machinery malfunction and which predicts the useful life of the machine. For this purpose a bearing case from NASA data repository was taken as a case study to validate the feasibility of the proposed method which showed that degradation of bearing can be effectively monitored. Original Article International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN(P): 2249-6890; ISSN(E): 2249-8001 Vol. 6, Issue 4, Aug 2016, 25-34 © TJPRC Pvt. Ltd
Transcript of 3. IJMPERD - CONDITION MONITORING OF TURBO GENERATORS …
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CONDITION MONITORING OF TURBOGENERATORS OF A
THERMAL POWER PLANT USING FUZZY LOGIC
CH. SAI AMULYA 1 & M. SRINIVASA RAO 2
1M.Tech Student, Department of Mechanical Engineering, GMRIT, Rajam, Srikakulam, Andhra Pradesh ,India 2Professor, Department of Mechanical Engineering, GMRIT, Rajam, Srikakulam, Andhra Pradesh, India
ABSTRACT
Turbogenerator is one of the main components in thermal power plant and its maintenance plays avital role in
many applications. Condition monitoring is the process of determining the condition of machinery while in operation
which lessens the probability of failure by allowing the maintenance to be scheduled.In this work,vibration monitoring
analysis of steam turbo generators is studied. In this study Fuzzy Logic Toolbox in MATLAB is used for analysing the
health condition of the turbo generators considering displacement and velocity as parameters.For this purpose, a
thermal power plant (TPP) of the Visakhapatnam Steel Plant has been chosen as a case study.This type of analysis is
very useful for assessing the condition of complex systems of thermal power plants.
KEYWORDS:Condition Monitoring, Vibration Analysis, Thermal Power Plant, Fuzzy Logic, Health Condition
Received: Jun 19, 2016; Accepted: Jul 14, 2016; Published: Jul 19, 2016; Paper Id.: IJMPERDAUG20163
INTRODUCTION
A condition monitoring program plays an essential role in industries that have plant machinery. It gives
the health condition of the components of the power system. There are various types of condition monitoring
techniques depending on the industry and application which include vibration analysis, signal processing,
analysis, acoustic analysis, etc. Vibration condition monitoring analysis is carried out on Turbogeneratorsin TPP,
Visakhapatnam Steel plant. Bently Nevada 3500 series condition monitoring vibration system is used to measure
the vibration.
Cheng et al.[1] stated that fuzzy distributions were used instead of the classical probability distribution
for the components, and calculate the fuzzy numbers to solve the fuzzy system reliability via non-linear
programming techniques. He demonstrated that this approach and its computation algorithm are efficient and
simple to implement.Shaaban et.al[2] proposed a decision tree method to improve steam turbine generator fault
diagnosis. He stated that the method is an effective fault data disposal method for steam turbine generator fault
diagnosis.
Pavan Kumar et al.[3] stated that vibration-monitoring technique is very powerful tool for assessing the
condition of rotary machine and if used with proper precautionary methods it can reduce the breakdowns of
power plants. Lun et al.[4] described a reliable condition monitoring system to prevent machinery malfunction
and which predicts the useful life of the machine. For this purpose a bearing case from NASA data repository was
taken as a case study to validate the feasibility of the proposed method which showed that degradation of bearing
can be effectively monitored.
Original A
rticle International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN(P): 2249-6890; ISSN(E): 2249-8001 Vol. 6, Issue 4, Aug 2016, 25-34 © TJPRC Pvt. Ltd
26 Ch.Sai Amulya & M. Srinivasa Rao
Impact Factor (JCC): 5.7294 NAAS Rating: 2.45
Ariavie et al.[5] developed a Fuzzy Inference System for predicting the performance of steam turbine based on analyzing
the generated data for different inlet and outlet conditions. The result of efficiency for different types of membership
functions and defuzzification method was obtained. He stated that Fuzzy logic can be used to effectively predict the
performance of a steam turbine.Ignatio et al. [6]described how online remote condition monitoring increases the
maintenance system. His findings specified the several aspects of online remote condition monitoring system which
thermal power plants can consider to improve on plant safety.Shen et al.[7] developed a diagnostic model algorithm based
on the Fuzzy logic modeling method that estimated engine health condition analysis for prediction of engine operation
behavior. The resulted models were nonlinear in nature and could predict the performance and could be examined for
abnormality.
Yang et al.[8] proposed integrated fuzzy multiple criteria decision making (MCDM) method for vendor selection
problem that included triangular fuzzy numbers,interpretive structural modeling, the fuzzy analytical hierarchy process,
non-additive fuzzy integral to determine the best vendor based on fuzzy weights with fuzzy synthetic utilities.Rui
Francisco et al.[9] described a detection method for rotating machine failures based on a change in vibration pattern. He
stated that a Fuzzy System was adjusted to detect and analyze the evolution of severity and decision making about
maintenance of rotating machines.Malay et al. [10] explained that a Fuzzy base maintenance system (FBMS) has been
considered for his study taking frequency and downtime asinput parameters because fuzzy logic technique is best suited for
adapting multi variable parameters. His study aimed at reducing the causes of breakdowns and this model can be used to
analyze maintenance practice cost and maintenance time.
From the above literature survey, it was observed that as the time passes the health condition of the machine,
occurrence frequency of failures and its effect vary. Hence to mitigate these limitations, vibration analysis technique has
performed in this work for condition monitoring of turbo generators in thermal power plant of the Visakhapatnam Steel
plant. After that the results obtained from the vibration analysis have been further analysed by using Fuzzy logic tool in
MATLAB to evaluate the health condition of turbo generators.In this work Visakhapatnam Steel Plant has been taken as a
case study for the analysis.
CONDITION MONITORING OF TURBOGENERATORS USING VIBRA TION ANALYSIS
Panel In this work the displacement and velocity of five turbo generators are collected from maintenance log
books of the thermal power plant. The health condition of five turbo generators is analysed and modelled by using Fuzzy
tool box in MATLAB as described below.
Condition monitoring of turbogenerators in Visakahapatnam steel plant is carried out by Bently Neveda 3500
series vibration monitoring system (BNVMS) as shown Figure 1.The vibration probes are connected to the turbogenerator
bearings at four positions i.e., at turbine front, turbine rear, geneator front and generator rear. Voltage from BNVMS is sent
to all probes and the vibration is taken in vertical, horizontal and axialdirections at all four positions which is sent to the
Distributed Control System (DCS). The DCS connected to Human Machine Interface takes the vibration trends at each
position of the system at every interval of time and the technical data of the system is also displayed.
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Figure 1: Bently Neveda Vibration Monitoring System in TPP, Vizag Steel Plant
By using the Bently NevedaVibration Monitoring System, the vibration data of turbo generators were collected
from the maintenance log books. Velocity and displacements of turbo generators are taken in three directions i.e., axial,
vertical and horizontal directions along turbine front,turbine rear, generator front and generator rear positions. The sample
data of TG 1 is shown in Table 1.
Table 1: Displacements and Velocities at Different Positions on TG 1
TG 1 Position Displacement Velocity
Vertical
TF 22 1.7
TR 18 1.5
GF 24 2.1
GF 23 2.1
Horizontal
TF 15 2.3
TR 41 4.6
GF 35 3.9
GF 16 1
Axial
TF 21 1.5
TR 31 3.6
GF 42 4.9
GF 27 3.3 CONDITION MONITORING OF TURBO GENERATORS USING FUZZ Y LOGIC
In this work, the data obtained from Bently NevedaVibration Monitoring System the displacement and velocity of
turbo generators are further analysed using fuzzy logic technique.The displacement and velocity of five turbo generators
are taken as the input parameters and health condition is taken as the output parameter in the fuzzy logic. In fuzzification,
each input parameter has sub divded into five triangular membership functions and the output parameter has sub divided
into trapezoidal membership functions. Fuzzy rules have been developed for each parameter and finally, after the
defuzzifcation process the health condition of each turbo generator has evaluated.
THE FUZZY SYSTEM
The fuzzy inference system contains following five steps : (i) Fuzzification (ii) Rule base (iii) Fuzzy inference
system (iv) data base and (v) Defuzzification. The fuzzy inference system is shown in Figure 1.
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The function of each block is mentioned briefly as below
A fuzzification interface which converts the crisp
consisting a number of fuzzy if-then rules
inputs (features in the case of fuzzy classification) to outputs
fuzzy sets used in the fuzzy rules.A defuzzification interface which converts the fuzzy results of the inference into a crisp
output.
FUZZIFICATION OF INPUT AND OUTPUT
The application of fuzzy logic is to identify the variation in ranges of input and output variables. Then the range of
each process variable is divided into group of fuzzy subsets. Each fuzzy sub set is given a proper name and assigned a
member ship function. The membership function is assigned without depending on the results of the experiments. In
general membership functions are classified into trapezoidal, triangular etc. The Mamdani type of Fuzzy Inference System
was chosen for this work. The displacement and velocity of the each turbogenerator are taken as the input parameters while
the health condition is taken as the output of the Fuzzy Inference System (FIS) for vibration monitoring of turbo
generators. The fuzzification process of a fuzz
Displacementrange of TG 1:The maximum to and fro motion of the vibrating part when force is applied;
measured in “Microns”. The displacement is in the range of {0 50} from dat
membership functions as Very high, High, Medium
{25 50}, {37.5 62.5}.
Ch.Sai Amulya
The function of each block is mentioned briefly as below :
A fuzzification interface which converts the crisp inputs into degrees of match with linguistic values
then rules.A fuzzy inference system (FIS) is a system that uses fuzzy set theory to map
in the case of fuzzy classification) to outputs. Database which defines the membership functions of the
A defuzzification interface which converts the fuzzy results of the inference into a crisp
Figure 2: Fuzzy Inference System
FUZZIFICATION OF INPUT AND OUTPUT PARAMETERS
The application of fuzzy logic is to identify the variation in ranges of input and output variables. Then the range of
each process variable is divided into group of fuzzy subsets. Each fuzzy sub set is given a proper name and assigned a
ship function. The membership function is assigned without depending on the results of the experiments. In
general membership functions are classified into trapezoidal, triangular etc. The Mamdani type of Fuzzy Inference System
e displacement and velocity of the each turbogenerator are taken as the input parameters while
the health condition is taken as the output of the Fuzzy Inference System (FIS) for vibration monitoring of turbo
generators. The fuzzification process of a fuzzy logic system shown below in Figure 3
Figure 3 Fuzzification Process of TG 1
Displacementrange of TG 1:The maximum to and fro motion of the vibrating part when force is applied;
measured in “Microns”. The displacement is in the range of {0 50} from data table 1 and is classified into
membership functions as Very high, High, Medium, Low and Very Low with ranges {-12.5 12.5},
Ch.Sai Amulya & M. Srinivasa Rao
NAAS Rating: 2.45
inputs into degrees of match with linguistic values. Rule base
A fuzzy inference system (FIS) is a system that uses fuzzy set theory to map
se which defines the membership functions of the
A defuzzification interface which converts the fuzzy results of the inference into a crisp
The application of fuzzy logic is to identify the variation in ranges of input and output variables. Then the range of
each process variable is divided into group of fuzzy subsets. Each fuzzy sub set is given a proper name and assigned a
ship function. The membership function is assigned without depending on the results of the experiments. In
general membership functions are classified into trapezoidal, triangular etc. The Mamdani type of Fuzzy Inference System
e displacement and velocity of the each turbogenerator are taken as the input parameters while
the health condition is taken as the output of the Fuzzy Inference System (FIS) for vibration monitoring of turbo
Displacementrange of TG 1:The maximum to and fro motion of the vibrating part when force is applied;
and is classified into five triangular
12.5 12.5}, {0 25}, {12.5 37.5},
Condition Monitoring of Turbogenerators of a Thermal Power Plant Using
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Figure 4:
Velocity range of TG 1:The maximum speed at which the motion takes place in a vibrating part;
“mm/sec”. The velocity parameter is in the range of {0 20} from data table 1 and
membership functions as Very high, High, Medium,
25}.
Figure
Health condition of TG 1: The range of health condition is considered between {0 10} and
trapezoidal membership functions as Very good, good, fair, rough and Very rough.with ranges {
7.2}, {5.3 9.7}, {7.8 12.2}.
Condition Monitoring of Turbogenerators of a Thermal Power Plant Using Fuzzy Logic
4: Membership Functions of Displacement of TG 1
of TG 1:The maximum speed at which the motion takes place in a vibrating part;
“mm/sec”. The velocity parameter is in the range of {0 20} from data table 1 and is classified into
membership functions as Very high, High, Medium, Low and Very Low with ranges {-5 5}, {0 10}, {5 15},
Figure 5: Membership Functions of Velocity of TG 1
Health condition of TG 1: The range of health condition is considered between {0 10} and
membership functions as Very good, good, fair, rough and Very rough.with ranges {
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of TG 1:The maximum speed at which the motion takes place in a vibrating part; measured in
is classified into five triangular
5 5}, {0 10}, {5 15}, {10 20}, {15
Health condition of TG 1: The range of health condition is considered between {0 10} and is classified into five
membership functions as Very good, good, fair, rough and Very rough.with ranges {-2.2 2.2}, {0.3 4.7}, {2.8
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Impact Factor (JCC): 5.7294
Figure 6: Membership
RULE EVALUATION
The relationship between input and the out
fuzzy rules. If all the fuzzy rules are saved in a data base, a fuzzy rule base will be established. The number of fuzzy rule
in fuzzy system is related to the number of fuzzy set for ea
7. In this paper two input parameters are taken
based on if-then statement to describe this model.
Figure
DEFUZZIFICATION
The final step is the defuzzification process. It is a process of converting fuzzification value to an equivalent crisp
value of actual use. This process is based on the idea of deriving a crisp value
the collection of membership function values to a single scalar quantity.From the defuzzification process the health
condition of TG 1 at displacement, velocity
of TG 1 is said to befairas shown in Figure 8.
Ch.Sai Amulya
Membership Functions of Health Condition of TG
The relationship between input and the output in fuzzy system is given by a set of statements which are called
fuzzy rules. If all the fuzzy rules are saved in a data base, a fuzzy rule base will be established. The number of fuzzy rule
in fuzzy system is related to the number of fuzzy set for each input variable. The fuzzy rules for TG 1 are shown in Figure
7. In this paper two input parameters are taken each has five subdivisions and one hundred twenty five rules rules are taken
then statement to describe this model.
Figure 7: Rules For Membership Functions of TG 1
The final step is the defuzzification process. It is a process of converting fuzzification value to an equivalent crisp
value of actual use. This process is based on the idea of deriving a crisp value for a fuzzy function. Defuzzification reduces
the collection of membership function values to a single scalar quantity.From the defuzzification process the health
condition of TG 1 at displacement, velocity range of {25 10} is {5}. From the defuzzification
Figure 8.
Ch.Sai Amulya & M. Srinivasa Rao
NAAS Rating: 2.45
TG 1
put in fuzzy system is given by a set of statements which are called
fuzzy rules. If all the fuzzy rules are saved in a data base, a fuzzy rule base will be established. The number of fuzzy rules
ch input variable. The fuzzy rules for TG 1 are shown in Figure
twenty five rules rules are taken
The final step is the defuzzification process. It is a process of converting fuzzification value to an equivalent crisp
for a fuzzy function. Defuzzification reduces
the collection of membership function values to a single scalar quantity.From the defuzzification process the health
range of {25 10} is {5}. From the defuzzification process the health condition
Condition Monitoring of Turbogenerators of a Thermal Power Plant Using
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Turbo Generator 1
RESULTS & DISCUSSIONS
The defuzzification process as performed in the previous section has been performed
and the obtained results are incorporated
Turbo Generator 2
From the defuzzification process the health condition of TG 2 at displacement, velocity
From the defuzzification process the health condition of TG 2 is said to be fair.
Turbo Generator 3
Condition Monitoring of Turbogenerators of a Thermal Power Plant Using Fuzzy Logic
Figure 8: Defuzzified Valuesof TG 1
defuzzification process as performed in the previous section has been performed
results are incorporated from Figure 9 to Figure 12.
Figure 9: Defuzzified Valuesof TG 2
From the defuzzification process the health condition of TG 2 at displacement, velocity
the defuzzification process the health condition of TG 2 is said to be fair.
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defuzzification process as performed in the previous section has been performed for other turbo generators
From the defuzzification process the health condition of TG 2 at displacement, velocity range of {75 10} is {5}.
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Impact Factor (JCC): 5.7294
From the defuzzification process the health condition of TG 3 at displacement, velocitydefuzzification process the health condition of TG 3 is said to be fair.
Turbo Generator 4
From the defuzzification process the health condition of
From the defuzzification process the health condition of TG 4 is said to be fair.
Turbo Generator 5
From the defuzzification process the health condition of TG 5 at displacement, velocity
From the defuzzification process the health condition of TG 5 is said to be fair.
Ch.Sai Amulya
Figure 10: Defuzzified valuesof TG 3
From the defuzzification process the health condition of TG 3 at displacement, velocity range of {50 10} is {5}. From the defuzzification process the health condition of TG 3 is said to be fair.
Figure 11: Defuzzified Valuesof TG 4
From the defuzzification process the health condition of TG 4 at displacement, velocity
defuzzification process the health condition of TG 4 is said to be fair.
Figure12: Defuzzified Valuesof TG 5
From the defuzzification process the health condition of TG 5 at displacement, velocity
defuzzification process the health condition of TG 5 is said to be fair.
Ch.Sai Amulya & M. Srinivasa Rao
NAAS Rating: 2.45
range of {50 10} is {5}. From the
TG 4 at displacement, velocity range of {125 10} is {5}.
From the defuzzification process the health condition of TG 5 at displacement, velocity range of {25 10} is {5}.
Condition Monitoring of Turbogenerators of a Thermal Power Plant Using Fuzzy Logic 33
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CONCLUSIONS
Vibration Monitoring and Analysis using Fuzzy tool box is the easiest way to analyse the complex systems.In this
work turbogenerators of thermal power plant are taken as a case study and a brief description of how to fuzzy the vague
condition monitoring data to get the health condition of the system easily than manual technique has been performed. In
this work, vibration monitoring analysis of steam turbo generators is studied. In this study Fuzzy Logic Toolbox in
MATLAB is used for analysing the health condition of the turbo generators considering displacement and velocity as
parameters. For this purpose, a thermal power plant (TPP) of the Visakhapatnam Steel Plant has been chosen as a case
study.by using fuzzy logic, the vibration analysis data has been further analysed. The obtained results shows that exact
condition monitoring of turbo generators with less effort than the conventional vibration analysis. This type of analysis is
very useful for assessing the condition of complex systems of thermal power plants.
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