The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
Using artificial intelligence technique based on neural network in gas turbine monitoring Mohamed Benrahmoune (1)
(1)
, Ahmed Hafaifa
(1*)
, Mouloud Guemana
(2)
(3)
and Fatima Bekkadour
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria
Emails :
[email protected],
[email protected],
[email protected] (2)
Faculty of Science and Technology, University of Médéa, Algeria Email:
[email protected] (3)
SCAL Team, MISC Laboratory, Constantine, Algeria. Email:
[email protected]
Abstract The detection of failures in an industrial plant requires a thorough knowledge of the behaviors of the system which complicate and modernize in new technology. The objective of this work aims at the design of an intelligent diagnostic tool based on the network of artificial neurons, this technique with its capacities of generalization and memorization gives a tool of diagnosis and the efficient modeling, applied to A MS 3002 gas turbine, in order to facilitate the interventions on the turbine examined as well as to avoid the prognosis of the operators and to reduce the cost of preventive maintenance. Keywords: Artificial intelligence, gas turbine monitoring, neural network, vibration. 1. Introduction Industrial production is characterized by ever-increasing complexity, which leads to complexities in diagnostic systems and can be critical throughout the industrial plant. For this purpose and in view of the importance of gas turbines in petroleum installations and in other industrial fields, these systems require a reliable diagnostic system to deal with the fugitive defects affecting these processes, which can put the turbine in critical or in a fully stopped state of the machine. In the need for speed to detect and locate these failures in these gas turbine systems, this work uses artificial intelligence techniques based on artificial neural networks for the treatment of diagnostic problems in these machines, Since neural networks have a great capacity for generating and storing information. By these techniques, this work proposes to treat the various failures, which can lead to dangerous vibrations and which degrades the operating state of these types of machines. The vibrations can then be seen as symptoms of failures which would allow to translate the mechanical state of a gas turbine through tools to aid in the detection and diagnosis of failures. Indeed, a large quantity of the breakdowns causes a decrease in the production appearing in the industrial systems, where the failure to detect breakdowns leads to a degradation of the turbine itself
The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
with the redundancy of maintenance interventions. All these disadvantages are caused by the use of conventional diagnostic methods, in contrast to the use of artificial intelligence based methods based on the neural network which possesses the ability to memorize and generate knowledge by the turbine system at gas. However, the integration of the neural network in the diagnosis of industrial systems allows a reliable and immediate diagnosis of failures occurring fugitively on the system as well as ensuring a good operating condition of this turbine system and decreases the costs of preventive maintenance. 2. Gas turbine The gas turbine is a machine which converts heat energy into mechanical energy. These transformations are carried out by the basic elements of the operation of the gas turbine, whatever its design, a combustion turbine, is a machine which is currently in vogue, this machine is composed of three main elements; The axial compressor, the combustion chamber and the power turbine. The examined turbine in this work is a turbine of model MS 3002 with double shaft and single cycle, it consists of a 15-stage axial compressor with 6 combustion chamber arranged (90 °) as a function of axial direction of high pressure turbine (HP) has a single stage which is the first step and in the second stage there is the pressure-based turbine which drives the load. This turbine is installed at the gas compression station in Hassi Massoud in southern Algeria, the monitoring configuration of this machine is shown in Figure 1.
Fig 1. Gas turbine MS 3002 The input pressure to the compressor P1 is given by the pressure drop in the intake channel decreased by the pressure drop of the turbine installation area, given by:
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
P1 = Pa − ∆Pa
(1)
With Pa is the atmospheric pressure of the turbine installation area, ∆Pa is the pressure drop in the intake channel, determined by P2 = P1τ ,
τ
is the compression ratio.
The temperature of the compressor output is given by:
P T2 = T1 ( 2 ) P1
(γ −1)
γ
(2)
With the isentropic exponent γ is defined by:
γ =
C p (T1 −T2 ) C p (T1 −T2 ) − r
(3)
C p (T1 −T2 ) defines the average specific heat of the air between the temperatures T1 and T2 . 3. Intelligent fault diagnosis of a turbine Neural networks are capable of generating behavioral models from the input-output data of dynamical systems. They have been widely used in the control, modeling and monitoring of industrial systems. Neural networks are nowadays a well-understood and mastered data processing technique that allows the engineer to extract, in many situations, the maximum amount of relevant information from the data he possesses : Process control, property prediction, function modeling, pattern recognition, etc. Moreover, in the diagnosis phase of industrial systems and precisely in the residue generation step between the system studied with the neural model, the network possesses the ability to obtain the behavior of the system to evaluate the residue. In the case of the gas turbine examined, detection of neural network-based defects is proposed, this approach is shown in Figure 2.
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
Fig 2. Detection by neural network-based turbine defects In this work we select the multilayer perceptron , shown in Figure 3, which is an extension of the monolayer perceptron which has one or several hidden layers. The neurons are arranged in successive layers: the first layer that forms the vector of the input data is called the input layer, while the last layer that produces the results is called the output layer, all the other layers in the middle are called layers. The neurons in the input layer are connected only to the next layer while the neurons in the hidden layers have the particularity of being connected to all the neurons of the previous layer and the next layer. The selection of the number of hidden layers generally depends on the complexity of the problem to be solved, in theory a single hidden layer may be sufficient to solve the problems, but it is possible that having several hidden layers can solve the diagnostic problems more easily.
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
Fig 3. Multilayer perceptron configuration The error of a neuron k in a layer j is calculated from the errors of the weight-weighted neurons in the layer j + 1 , as following :
ε k( j ) = f ( ∑ w ki x i ). i
∑ w ki( j + i ) .ε i( j +1)
i∈ j +1
(4)
4. Application results After several tests, we have developed a multi-layer neural network with a hidden layer of 18 neurons and sigmoid-type activation function and the linear type activation function output layer as well as minimizing the function of costs, we train our network 800 times, with each iteration we get an error and the network repeat the process in order to have a zero error. The Figure 4 shown the axial vibration of the turbine with the associated network model, the Figure 5 shown the mean squared error of the network modelling, the Figure 6 present the histogram error between the actual signal and the network output.
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
Variation temporelle du vibration axial du Turbine
30 25
V ib r a t io n u m
20 15 10 Sortie réel Sortie du réseau
5 0 4
E rre u r
2 0
-2 -4 50
100
150
200
250
300
350
400
450
500
550
Time sec Fig 4. Axial vibration of the turbine with the associated network model Best Validation Performance is 0.45348 at epoch 8
3
Mean Squared Error (mse)
10
Train Validation Test Best
2
10
1
10
0
10
-1
10
0
2
4
6
8
10
12
14
14 Epochs
Fig 5. The mean squared error of the network modelling
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
300
Training Validation Test Zero Error
250
In s ta n c e s
200 150 100
5.574
4.973
4.371
3.769
3.167
2.565
1.963
1.361
0.7587
0.1567
-0 . 4 4 5 3
-1 . 0 4 7
-1 . 6 4 9
-2 . 2 5 1
-2 . 8 5 3
-3 . 4 5 5
-4 . 0 5 7
-4 . 6 5 9
-5 . 2 6 1
0
-5 . 8 6 3
50
Errors = Targets - Outputs
Fig 6. Histogram error between the actual signal and the network output The Figure 7 shown the radial vibration of the turbine and the associated network model, the Figure 8, present the mean squared error of the turbine radial vibration modelling and the Figure 9, shown the histogram error between the actual signal and the network output.
Variation temporelle du vibration radial du Turbine
35 30
V ib r a t io n u m
25 20 15 10
Sortie réel Sortie du réseau
5 0
E rre u r
5 0
-5 50
100
150
200
250
300
350
400
450
500
Time sec Fig 7. Radial vibration of the turbine and associated network model
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
Best Validation Performance is 0.92448 at epoch 4
3
Mean Squared Error (mse)
10
Train Validation Test Best
2
10
1
10
0
10
-1
10
0
1
2
3
4
5
6
7
8
9
10
10 Epochs
Fig 8. The mean squared error of the turbine radial vibration modelling
180
Training Validation Test Zero Error
160 140
In s ta n c e s
120 100 80 60 40
4 .8 6 8
4 .3 4 7
3 .8 2 6
3 .3 0 6
2 .7 8 5
2 .2 6 4
1 .7 4 4
1 .2 2 3
0 .7 0 2 1
0 .1 8 1 4
-0 . 3 3 9 3
-0 . 8 6
-1 . 3 8 1
-1 . 9 0 1
-2 . 4 2 2
-2 . 9 4 3
-3 . 4 6 3
-3 . 9 8 4
-4 . 5 0 5
0
-5 . 0 2 6
20
Errors = Targets - Outputs
Fig 9. Histogram error between the actual signal and the network output The Figure 10, shown the radial vibration of the compressor and associated network model, the Figure 11 present the mean squared error of the compressor radial vibration modelling, the Figure 12 shown the histogram error between the actual signal and the network output and the Fig 13 shown the variation of the high pressure shaft rotation speed NGP model of the examined turbine.
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
Variation temporelle du vibration axial du Compresseur
40 35
V ib r a t io n u m
30 25 20 15 Sortie réelle Training Targets
10 5
E rre u r
0 10 0
-10
50
100
150
200
250
300
350
400
450
500
550
Time sec Fig 10. Radial vibration of the compressor and associated network model Best Validation Performance is 0.70946 at epoch 5
3
Mean Squared Error (mse)
10
Train Validation Test Best
2
10
1
10
0
10
-1
10
0
1
2
3
4
5
6
7
8
9
10
11
11 Epochs
Fig 11. The mean squared error of the compressor radial vibration modelling
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
300
Training Validation Test Zero Error
250
In s ta n c e s
200 150 100
7.869
7 .2 2
6.571
5.922
5.273
4.624
3.976
3.327
2.678
1 .3 8
2.029
0.7313
-0 . 5 6 6 4
0 .0 8 2 4 3
-1 . 2 1 5
-1 . 8 6 4
-2 . 5 1 3
-3 . 1 6 2
-4 . 4 6
0
-3 . 8 1 1
50
Errors = Targets - Outputs
Fig 12. Histogram error between the actual signal and the network output
R o t a t io n a l S p e e d %
120
100
80
Radial Vibration HP rotation Axial Vibration
A m p lit u d e u m
60
40
threshold
Alarme
20
0
0
100
200
300 Time (sec)
400
500
600
Fig 13. High pressure shaft rotation speed NGP model
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
In this system, in view of the fact that the turbine is subjected to a radial vibration which exceeds the tolerance threshold, this detection is made by the neural network which possesses the control capacity of any signal. In view of their frequent use in industry and their important role in the national economy, we develop a neural model with the ability to monitor the turbine safely using data from transmitters and sensors. This study can realize to make another integrated control system in the turbine. 5. Conclusion According to the results obtained, note that our network is playing an important role in the monitoring of large rotational range machines, as well as the neural network capable of generating information from the processed signals. The second interest is to integrate the artificial intelligence methods based on the neural network to the super- vision of industrial processes to facilitate maintenance interventions and to ensure the reliability of the turbine. The results obtained encouraging to integrate intelligent techniques in the monitoring and diagnosis of the gas turbine. References [1].
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
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The 2sd International Conference on Applied Automation and Industrial Diagnostics, Djelfa on 16-17 September 2017, Algeria
Conference on Control, Engineering & Information Technology CEIT’2015, on 25-27 May 2015 Tlemcen, Algeria. [23]. Neural Network Toolbox™ R2013a Mark Hudson Beale Martin
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