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ICIEAM.2017.8076400

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2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
Data Mining in the Diagnostics of Oil Extraction
Equipment
Tagirova K.F., Vulfin A.M., Ramazanov A.R.
Department of Computer Science and Robotics
Ufa State Aviation Technical University
Russia, Ufa
Abstract—We present the key steps in the dynamogram
classification algorithm development. These are data processing,
procedures of generation and selection of features, constructing
of a neural network classifier and estimation of its work quality.
To estimate the possibility to single out complex defects
(subclasses), we analyzed the structure of the input pattern
sample with the aid of clusterization algorithms. Possibilities of
the diagnostic algorithm for submersible equipment on the basis
of the pretreated dynamometer cards recognition that
characterize the current object state were explored. The key steps
of the algorithm implementation for the dynamometer card
classification are: feature generation, feature selection and
classifiers building. The classifiers based on single neural
networks and hierarchic neural network committees with
different architectures have demonstrated the accuracy of
recognition of the equipment condition classes at the level of 8094%. The reliability of recognition for the equipment condition
classes is at the level of 78-98% for the data that were not
included in the training set.
Keywords—submersible equipment; neural network classifiers;
data mining
I.
INTRODUCTION
The most common method of oil production from marginal
wells is the use of a downhole sucker-rod pump (DSRP). The
work of the majority of wells equipped with DSRP is
controlled by portable and stationary dynamographs.
DSRP oil production diagnostics is often based on
dynamometer card analysis [1]. Analysis of the dependence of
F force at the rod string point of suspension on polished rod
stroke S allows classifying equipment condition into classes of
normal duty or malfunction by using F(S) signature and set of
features. Closed curve of dynamometer card contains sections
describing each phase of polished rod stroke movement.
Diagnosis algorithms of aggregate current status classification
can operate the dynamometer card entirely by applying feature
generation and feature selection, or they can analyze separate
parts and feature points of dynamometer card [2].
A generalized approach of the development of the
algorithm of DSRP submersible equipment diagnostics
according to dynamometry data by using the data mining
methodology is considered in previous communications [1-8].
These methods are based on recognition of pretreated
dynamometer cards using single neural networks and
hierarchic neural networks committees with different
architectures with the validity of equipment condition class
recognition at the level of 80-94%.
II. DYNAMOMETRY DATA ANALYSIS AND SELECTION OF
ESSENTIAL DYNAMOMETER CARD FEATURES
Previous paper [3] discusses possibilities of the neural
network classifiers based on multilayer perceptron. These
classifiers use as an input vector following parameters:
• Nɋ1 –discrete counts of force and stroke parameter
( ) taken regularly in quantity of 16, 32, 64,
x = F d sd
128, 256, 512. It was implemented a normalization of
the counts to obtain s d , F d , that describe the
dynamometer card, in a range s n ∈ [ −1;1] , F n ∈ [ −1;1] ,
spline interpolation of the normalized counts suite and
the following sampling to build the suite s d , F d with
the l = 2k , k = 4, 5, " , 9 length.
• Nɋ2 – counts
( ( ))
taken as a
( ( ))
taken as a
h = haar F d s d
concatenation of approximation and specification
vectors for several levels of discrete Haar wavelettransform decomposition.
• Nɋ3 – counts
db = db4 F d s d
concatenation of approximation and specification
vectors for all levels with using a first order of
Daubechies wavelet.
( ) using
• Nɋ4 – counts taken from the vector x = F d s d
principal component analysis.
• Nɋ5
–
counts
taken
using
principal
counts
taken
from
using
principal
analysis.
• Nɋ6
–
d
( ( s ))
db = db4 F
analysis.
from
( ( s ))
h = haar F
d
d
d
the
vector
component
the
vector
component
Guided by the classification results, according to the
procedure of sliding window with 10 runs, it is found that the
procedure of feature generation using Haar wavelet-transform
is the most suitable for building a classifier: it uses a vector of
978-1-5090-5648-417$31.00 ©2017 IEEE
2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
128 approximation and specification coefficients for several
levels of decomposition [3]. Original multilayer perceptron
NC2 (with the 128-32-8 neurons architecture) can be replaced
by the massively smaller network with 8-8-8 neurons
architecture that significantly accelerates training process and
increases classifier generality because the amount of training
weights is decreasing.
and interpolating by b-spline to suppress highfrequency noise related to the work of the sensors of
stroke movement and force; and preparing data to
obtain the set of equidistant counts.
3) Obtaining the required amount of discrete counts
s d , F d at regular time intervals with a period
l = 2k , k = 4, 5, " , 9 .
4) Expanding the curves F(t) and S(t) in time for the
counts sets independent analysis (Fig. 1).
5) Multilayer discrete decomposition of the set of counts
x = F d ( t ) on the basis of Haar wavelet-functions
Hierarchic neural network classifiers based on the
committees of multilayer perceptrons were discussed in [2]
and showed the same results. Implementation of the proposed
diagnosis algorithms as a part of hardware-software DSRP
control station complex assumes limited computational
resources of the controller, so it is more preferable to
implement the single net.
forms the coefficient vector ª¬ca j , cd j , cd j −1 , " , cd1 º¼ ,
where ca, cd – approximation and specification
coefficients respectively to the levels j, j-1,…, which is
required to generate dynamometer card multipurpose
features related to the different classes [3, 10]. As
shown in previous researches, such approach allows
obtaining the set of features that increase the classifier
accuracy of the test set unlike the features, which were
obtained as a result of Fourier transform. Estimated
quality of signal approximation is based on ɫaj
coefficients and amount of the ca, cd nonzero
coefficients, and can be analyzed visually from the
given chart. As a result, there is a vector of features
from which the most informative features have to be
selected using a principal component analysis.
6) Selecting the most important features from the vector
of Haar wavelet-coefficients using principal component
analysis (PCA). Variance of the first ten principal
components is more than 90% of total variance so the
8-10 principal components are enough for compact
description of dynamometer cards and build the
classifier. Selection the necessary principal components
number and parameters of the neural network classifier
is based on multilayer perceptron, that is specified in
[2, 3, 11, 12]. Original multilayer perceptron with the
input dimension of 128 is replaced by the network
taking the input vector of 8 features.
7) Based on the results of applying PCA the feature vector
is made in a new space of fewer dimensions for the
following classifier training.
Thus, the procedure of selecting parameters of feature
generation and feature selection (wavelet decomposition for
feature generation, the type of wavelet, the depth of
decomposition,
coefficients
that
encodes
essential
dynamometer card features, and principal component analysis
for feature selection) described completely.
There is still the question of the classifier type. Multilayer
neural networks were used in [3, 9] and amount of hidden
layers and neurons was substantiated but there wasn’t any
comparison with the other types of classifiers.
In this paper an analysis of possibilities of the classifiers
based on support vector machine ensemble and decision trees
ensemble has been preformed.
The problem of submersible equipment diagnostics was
formulated in [2] as a problem of classification of the patterns
described with the aggregate of process variables
(dynamometry data). Each class characterizes the equipment
condition as a normal duty or one of malfunctions.
Generalized approach of development the submersible
equipment diagnosis methods and algorithms is based on data
mining methodology [3]. Thus, the process of dynamometer
card pretreatment and classifier building consists of the
following steps:
1) Scaling the dependences of rod stroke movement and
force to the range s n ∈ [ −1;1] , F n ∈ [ −1;1] .
2) Filtering the normalized counts by using median filter
S
F
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
20
40
60
80
100
a)
120
140
160
180
200
0
0
50
100
150
200
250
300
n
n
b)
Fig. 1. a) normalized polished rod stroke dependence on time S(n); b) normalized force at the rod string point of suspension dependence on time F(n)
2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
Therefore, main steps of implementation the algorithm of
dynamometer card classification are:
estimations should be found to make k separations of the
available samples amount in a training set and a test set [13].
1) Feature generation – selecting features the most
informative describing the signal.
2) Feature selection – locating features which have the
best classification characteristic for the set of
dynamometer cards samples.
3) Building the classifier.
The generalizing possibilities of classifiers were compared
using counts as an input vector which was selected by using
Classifiers are based on single neural networks and
hierarchic neural network committees with different
architectures. They have demonstrated the accuracy of
recognition of the equipment condition classes at the level of
80-94% [1-3].
A. Experiment
Equipment condition classes took into account small,
medium and extra datasets and are shown in Tables 1, 2 and 3
respectively.
TABLE 1. Equipment condition classes in small training dataset
ʋ
Equipment condition class
1
2
3
4
5
Gas influence
High plunger fit
Plunger extension out of the pump
Degree of dirt-choking pump
Significant friction forces
Total:
Number of
samples
32
51
21
52
34
190
TABLE 2. Equipment condition classes in medium training dataset
ʋ
1
2
3
4
5
6
Equipment condition class
Gas influence
High plunger fit
Plunger extension out of the pump
Degree of dirt-choking pump
Plunger jamming
Significant friction forces
Total:
Number of samples
32
24
21
52
51
34
214
TABLE 3. Equipment condition classes in extra training dataset
ʋ
Equipment condition class
1
2
3
Large paraffin deposit
Gas influence
High plunger fit
Number of
samples
21
51
24
4
5
6
7
8
Plunger extension out of the pump
Degree of dirt-choking pump
Plunger jamming
Significant friction forces
Low level, no pumping
21
32
25
54
52
Total:
280
Three dynamometer cards sets were used for testing. Each
class contains more than 20 samples. This increases the classes
representability and the general set parameters. Against [3]
there is no asymmetry in samples number [13].
K-fold cross-validation was used to estimate the
generalizing abilities of classifiers. An average value of the
classification accuracy and standard deviation of the obtained
PCA from
( ( ))
h = haar F d s d
– composition of the
approximation and specification vectors for all levels of
discrete Haar wavelet-transform decomposition [14, 15, 16,
17].
•
Neural network classifier (NK1) –
perceptron [2, 18].
•
Support vector machine ensemble (MK2);
•
Decision trees ensemble (DK3).
multilayer
B. Support vector machine in the problem of classification
into several classes
Single support vector machine solves the problem of
building an optimal separating hyperplane in case of binary
classification. The one-vs-all type classifier can be built using
an ensemble of binary classifiers. Single SVM separates
samples into sets of belonging to class i (output feature
encoding by 1) or not belonging to this class (-1).
Matrix is shown in table 4. It was built and analyzed to obtain
the result of sample classification.
TABLE 4. Matrix of ensemble classifiers output values
Sample
Classifier 1
Classifier 2
Classifier i
Classifier m
1
-1
-1
1
-1
2
-1
-1
1
-1
j
…
n
1
-1
-1
-1
Sample would be related to the class with the index which
is matched by the output vector element with maximum value.
The problem of using SVM resides in building rectifying
planes or cores , which are the most relevant for a specific
target. Several standard cores were tested for dynamometer
cards classification. These cores narrow SVM down to:
• Polynomial
quadratic),
separating
hyperplanes
(linear
and
• Two-layers neural networks,
• Potential functions (radial basis networks or radial basis
functions).
Ensemble of SVM-RBF classifiers with the Gaussian
showed the best results of classification. Quadratic
programming method was used to select the separating
hyperplanes parameters.
The other type of classifier is the decision tress ensemble. It
is built according to the method of the continual improvement
of classification result. A variation of the busting algorithm
was used, because the arbitrary complex compositions can be
built from weak classifiers. Such compositions can demonstrate
2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
good results in classification problems if they are configured
correctly. Number of classifiers is 10, method of forming the
classifiers ensemble is boosting [13].
Results of the k-fold cross-validation procedure with k=10
shown in tables 5 and 6.
TABLE 5. Results of classifier training
Classifier
Training
Average value of
type
dataset
ɫorrectly
recognized samples
from training
dataset and
standard deviation,
%
Multilayer
small
94.743 ± 3.621
perceptron
medium
91.742 ± 1.719
[3]
extra
80.792 ± 1.776
SVM-RBF
small
98.363 ± 0.369
ensemble
medium
97.611 ± 0.744
extra
93.691 ± 0.675
Trees
small
100.000 ± 0.000
decision
medium
99.948 ± 0.164
ensemble +
extra
97.936 ± 0.251
boosting
Average value of
ɫorrectly
recognized
samples from test
dataset and
standard
deviation, %
95.196 ± 4.710
90.763 ± 6.903
78.175 ± 9.072
80.910 ± 1.616
70.734 ± 2.649
67.259 ± 2.845
78.293 ± 3.276
72.273 ± 4.460
69.104 ± 1.626
TABLE 6. Assessments of precision, recall and their F-score
Classifier type
Taining
precision
recall
dataset
Multilayer
perceptron [3]
SVM-RBF
ensemble
Trees decision
ensemble +
boosting
F-score
small
0.9420
0.9345
0.9382
medium
extra
small
0.9120
0.7747
0.8021
0.9059
0.7656
0.7688
0.9089
0.7701
0.7851
medium
extra
small
0.7535
0.6258
0.6880
0.5446
0.7192
0.5824
medium
extra
The following concepts are used to assess the quality of
classifiers performance [13].
Precision of classification within the class is the part of
samples which belong to this class in relation to all of the
samples related to this class by the classifier:
Pr ( ci ) =
pin
pin + nout
pin – number of samples of the class that were related to the
class by the classifier,
pout – number of samples of the same class that were not
related to ,
nout – number of remain samples of the class that belong
to other classes.
Recall of classification within the class is the part of
samples which belong to this class
Balanced F1-measure is the harmonic mean of the
precision and recall:
F1 ( ci ) =
2·Pr ( ci )·Rc ( ci )
Pr ( ci ) + Rc ( ci )
As shown in table 5 and 6, multilayer perceptron gives the
best results in the limited and symmetric number of samples
datasets, which called small, as its classification accuracy on
the test dataset is much higher than accuracy of the other
architectures. On the other hand, classification accuracy of the
trees decision ensemble and SVM is higher on the training
dataset than accuracy of the multilayer perceptron. Such
results show a good generalizing capability of neural network
decisions (approaches) and the overtraining effect that consists
in ensemble structures undue sensitivity to the noise contained
in the training set. It is in good agreement with the information
[13] about SVM fluctuation in relation to the noise in the
source data as the influence of outliers on building the
separating hyperplane is significant. Therefore, to suppress
this effect it is necessary to investigate the relevance vector
machine (RVM) possibilities or further dataset refinement
techniques those are associated with significant technical
difficulty connected with accumulation of full-size dataset of
all submersible equipment conditions.
As for ensembles of weak classifiers built on the basis of
continual improvement algorithms, it is difficult to configure
them and select the architecture on the limited datasets.
Overcoming of overtraining effect is failed.
III.
RESULTS AND DISCUSSIONS
Conclusions made on the basis of [1-3, 19, 20] about the
promising direction for further improvement of the approaches
to the construction of the automated dynamometer card
classifier in the problem of DSRP submersible equipment
diagnosis are:
1) Expert labeling of the equipment condition class should
include a degree of expert certainty about the sample
belonging to the particular class (or it should be an
information about the results of repairing or
disassembling equipment);
2) At the stage of dynamometer card pretreatment it is
necessary to allocate a set of feature points of the curve
[20], to split F(S) curve into a certain number of areas
that characterize the processes taking place in the
facility, and to apply the described procedures of
feature generation and selection for particular areas;
3) Neuro-fuzzy classifier is the good prospect because it
considers the description of
particular parts of
dynamometer card curve as a set of features selected by
plant engineer or in result of the analysis of numerical
schemes of equipment submersible part performing.
IV.
CONCLUSIONS
Problem discussed in this paper is about selecting the type
of classifier by comparing generalizing possibilities of trained
systems. Paper analyzes the possibilities of classifiers based
on a support vector machines ensemble, multilayer perceptron
and trees ensembles solutions.
Possibilities of diagnostic algorithm of DSRP submersible
equipment on the basis of recognition the pretreated
dynamometer cards that characterize the current state of the
object were explored. The reliability of recognition the
equipment condition classes is at the level of 78-98% for the
data that were not a part of the training set.
2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
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