efficient fault detection using support vector machine based hybrid expert system

ORIGINAL ARTICLE

Efficient fault detection using support vector machine basedhybrid expert system

Buddha Kishore • M. R. S. Satyanarayana •

K. Sujatha

Received: 15 March 2014 / Revised: 6 June 2014

� The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and

Maintenance, Lulea University of Technology, Sweden 2014

Abstract This paper demonstrates the methodology of

fault classification of rotating machinery using support

vector machine (SVM) in combination with genetic algo-

rithm and particle swarm optimization. In order to detect

the machine health condition, classifier uses the features as

the inputs from the preprocessed raw signal of a machine.

Support vector machine classifier prepared in combination

of hybrid adaptive particle swarm optimization and adap-

tive genetic algorithm (HAPAG) proposed for proficient

flaw detection. An industrial case study of a centrifugal

pump is considered and the data is given for both training

and testing of the classifier. A similar study with compa-

rable existing fault classifiers on the identification triumph

is investigated. SVM based HAPAG system results in

clustering the various faults with more than 90 % accuracy

when compared with adaptive tuning of SVM based tech-

niques like SVM—adaptive particle swarm optimization

and SVM—adaptive genetic algorithm. The outcome

indicates the adequacy of choosing the classifiers in finding

the machine health condition.

Keywords Adaptive tuning � Fault detection � Support

vector machine � Genetic algorithm � Hybrid adaptive

particle swarm optimization and adaptive genetic algorithm

1 Introduction

Industries all over the world entered the era of high tech-

nology maintenance to achieve minimum downtime and to

maximize production. The task of condition monitoring

and fault diagnosis of rotating machinery faults is both

significant and important, but often the failure diagnosis

process by human operators is time consuming and human

error may lead to a faulty diagnosis. Various machinery

faults can be detected by comparing the vibration signals in

normal and fault conditions.

Many progressive works have taken place in the recent

works and listed in (Shiroishi et al. 1997; McFadden 2000;

Randall 2001; Antoni and Randall 2002; Dellomo 1999)

related to monitoring using the signature analysis. Even

though the vibration signature analysis is playing a dominant

role in the condition monitoring area, industries and

researchers are interested in developing most trustworthy,

rapid and automatic procedure of fault diagnosis mainly to

identify the incipient failures.

Fault detection is a crucial step in running centrifugal

pumps efficiently. Fault detection is to be effectively

utilized to decide that a problem has occurred within a

certain area of operation (Martin 1994; Dalpiaz et al.

2000). The software application may identify that the

system is operating productively by performing at a level

that is optimal to the specified target. This application

spots the reason for the fault and basing on that the

organization can locate the fault to fix it. Automation of

B. Kishore (&)

Department of Mechanical Engineering, GITAM University,

Hyderabad, AP, India

e-mail: [email protected]

M. R. S. Satyanarayana

Department of Mechanical Engineering, GITAM University,

Visakhapatnam, AP, India

K. Sujatha

Department of CSE, Miracle Engineering College,

Vizianagaram, AP, India

123

Int J Syst Assur Eng Manag

DOI 10.1007/s13198-014-0281-y

fault detection increases efficiency and flexibility (Roemer

et al. 2001).

Various fault detection models are specified in Fig. 1.

Initially manual systems are used where faults are identi-

fied by noise detection using the bare human ear. Then

sensing tools are being used by which readings are taken

and mathematical calculations, are being performed to

detect faults. Both methods are time taking and error prone.

Next, artificial neural network (ANN) based models are

used in fault detection in many problems. However, ANN

uses empirical risk minimization (ERM) principle, which

suffers from local minimum traps and the difficulty of

determining the hidden layer size and learning rate. SVM

uses structural risk minimization (SRM) principle to min-

imize an upper bound on the expected risk, The difference

in RM leads to better generalization performance for SVMs

than ANNs. (Vapnik 1995; Boser and Guyon 1992).

2 Support vector machine based hybrid expert systems

Hybrid expert systems are developed by using combined

adaptive genetic algorithm and particle swarm optimization

with support vector machine. Proposed techniques are

useful in detecting faults in rotating machinery (Yang and

Tran 2012).

2.1 Support vector machine

SVM has the prospective to handle substantial character-

istic spaces (Grimmelius et al. 1995) as the preparation of

SVM is completed with the goal that the estimation of

arranging vectors does not have a notable impact on the

SVM execution as it has on the traditional classifier exe-

cution. Subsequently, this is effective in extending classi-

fication problems and Faults clustering. SVM-based

classifier has superb generalization properties when con-

trasted with routine classifiers, for the reason that in pre-

paring SVM classifier the Structural to minimize the

misclassification hazard.

SVM classifier used in this context is a supervised learning

algorithm based on statistical learning theory, whose aim is to

determine a hyper plane that optimally separates the two

classes by using train data sets. As shown in Fig. 2 (Samanta

et al. 2003) the optimum separating hyper plane can be found

by minimizing ||w||/2 under the constraintyi(wT:Xi + b)� 1,

i = 1,2,…,n. Thus, determination of optimum hyper plane is

required to solve optimization problem.

Minimize :1

2jjwjj2

Subject to yi(wT:Xi + b)� 1 i ¼ 1; 2; . . .nð1Þ

.

Given data input xi(i = 1, 2, yn), n is the number of

samples. The samples are assumed to have two classes’

namely positive class and negative class. Each of classes

associate with labels be yi = 1 for positive class yi = -1

for negative class, respectively.

Equation 2 can be used to separate the given data by

using the hyper plane f(x) = 0.

f(x) = wTx + b =Xn

j�1

wjxj + b = 0 ð2Þ

Where w is m-dimensional vector and b is a scalar. The

vector w and scalar b are used to define the position of

separating hyper plane. The decision function is made

using sign f(x) to create separating hyper plane that clas-

sifies input data in either positive class or negative class. A

distinct separating hyper plane should satisfy the following

constraints (Samanta et al. 2003).

f xið Þ� 1 if yi ¼ 1 and

f xið Þ� � 1 if yi ¼ �1:ð3Þ

SVM has tremendous performance in generalization and

can produce high accuracy in classification of fault

diagnosis.

Fig. 1 Fault detection types

Fig. 2 The classification process of SVM


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2.1.1 Multi class SVM

Multi class SVM is possible by combining one or more

simple SVMs. For example, in fault diagnosis of rotating

machineries there are several fault classes (Widodo and

Yang 2007) such as mechanical unbalance, misalignment,

bearing faults, etc. Therefore this requires multi-class

classification strategy. Suppose if the SVM has to check if

the category falls into three classes, then initially it checks

if SVM results either Class 1 or not. Next it checks SVM

results either Class 2 or not. Then finally it checks if SVM

results either class 3 or not.

2.2 Support vector machine with adaptive genetic

algorithm (SVMAGA)

AGA is applied to the improvement of SVM learning

parameters. Adaptive parameter (Samanta et al. 2003;

Aihong and Lizhe 2010) control in the AGA is carried out

by the type of feedback from the inquiry that serves as

inputs to a component used to focus the change to the

strategy (Youwen et al. 2012) parameter. The task of the

quality of the strategy parameter is resolved relying upon

the nature of results ran across by diverse operators/

parameters and the upgrading component can recognize the

benefits of contending techniques. An external mechanism

is used to update the control parameters. r and C are the two

important parameters of the SVM which are the standard

deviation and logistic regression coefficients. Based on

cross validation error the fitness value of AGA is calculated.

The algorithm of SVMAGA shown in Fig. 3 is as follows:

Step 1 Set initial population

Step 2 Regenerate population till condition is satisfied

Step 3 Train multiclass SVM and find fitness value

Step 4 Rank individuals basing on fitness

Step 5 Apply selection

Step 6 Apply crossover

Step 7 Perform adaptive mutation

Step 8 Replace worst individuals in population

Step 9 Iterate from Step 3 based on condition criteria

Step 10 Predict fault type with best weights

2.3 Support vector machine with adaptive particle

swarm optimization (SVMAPSO)

The learning or training to estimate the parameters in the

SVM becomes difficult or inefficient if there is noise in the

data. APSO is used in combination with SVM in order to

get better results.

The Inertial weight has been balanced adaptively (Gang

2013) in SVMAPSO. The past development of the particles

is great and proceeds the development when the particles of

swarm have been enhanced in past emphasis which indi-

cates the inertia weight (Fei et al. 2008) must be high. In

the event that the particles of swarm have been fizzled, it

demonstrates that their past development is bad enough and

is better that these particles don’t proceed the past devel-

opment so the inertia weight must be diminished. They

may plunge in the local optimum if all swarms use this

technique. To counteract this, algorithm ought not decrease

all swarms inertias weight all that could possibly be nee-

ded. The swarms or groups that have better fitness (Huang

2011) may be closer to the value of global optimum. So

these swarms need to have the low inertial weight to search

for the local optima than the swarms (Samanta and Natraj

2009) which have most terrible worst values. The adaptive

measure is linked to an individual value. The precision

requirement for fitness is the basic parameter to choose the

critical constraint. Here the values basically depend on the

calculation of pbest (personal best position) and gbest

(global best position). The Algorithm of SVMAPSO indi-

cated in Fig. 4 is as follows:





Step 5 Update pbest of each individual

Step 6 Update gbest of population

Step 7 Update position and velocities


Step 9 iterate from Step 3 based on condition criteria


2.4 Support vector machine with hybrid adaptive

particle swarm optimization, adaptive genetic

algorithm (SVMHAPAG)

SVM has the possibility to handle extensive characteristic

spaces, in light of the fact that the preparation of SVM is

completed so the extent of arranged vectors does not have

as dissimilar an impact on the execution of SVM as it has

Fig. 3 SVMAGA working process


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on the execution of tried and true classifier (Leung et al.

2012).

HAPAG uses a combination of APSO and AGA which

are mentioned in the Sects. 2.2 and 2.3. Here fitness values

are calculated using both the techniques. Then the best

fitness value is considered from the results of the two

techniques. The algorithm is as given below:





Step 5 Update pbest of each individual

Step 6 Update gbest of population

Step 7 Update position and velocities

Step 8 Apply selection

Step 9 Apply crossover

Step 10 Perform adaptive mutation


Step 12 Iterate from Step 3 based on condition criteria


Hybrid adaptive particle swarm optimization and genetic

algorithm (HAPAG) technique as shown in Fig. 5 is used

Fig. 4 SVMAPSO working process

Fig. 5 SVM HAPAG flow

chart


123

to train SVM which is used to detect the faults, the fit-

ness value denotes the error in the machine. Hence the

individual with the minimum fitness value is considered

to be the individual with less amount of error. But only

with the fitness value of an individual in the current

iteration it will not be fair to predict and consider that

particular individual as the best individual to survive for

the next consecutive iteration. This uses a combination of

both AGA and APSO and the best is selected as the

fitness value.

3 Results

The case study on centrifugal pump in LG polymers is

considered. Inputs and relevant data are gathered from the

worksite and nearly 500 samples are used for training the

model. Table 1 shows a part of a sample data set of the

training input set and another 150 samples are used for

testing the algorithm developed.

The inputs are the velocity, displacement and speed. The

output of the model is the diagnosed fault. In this case three

Table 1 Input data from a Centrifugal pump

Date Time RPM Velocity Displacement

V. H. A. V. H. A.

1-May-12 8.30 AM 4,400 17 8 14 4 4 3

2-May-12 13.00 PM 4,500 13 9 9 2.8 2 2

3-May-12 18.45 PM 4,600 15 8 10 3.5 1.8 2.5

4-May-12 9.30 AM 4,700 14 10 12 3.5 2.2 3.2

5-May-12 10.00 AM 4,700 15 8 13 3.4 3 4.2

7-May-12 9.00 AM 4,745 18 12 17 5 4.4 4.2

8-May-12 9.00 AM 4,730 12 11 10 4 4 4

9-May-12 9.00 AM 4,720 11 10 11 4 3 3

10-May-12 9.00 AM 4,720 10 10 10 5 3 3

11-May-12 9.30 AM 4,720 12 10 11 3 3 3

12-May-12 8.30 AM 4,720 17 15 15 4 5 5

14-May-12 10.00 AM 4,700 15 14 14 5 5 5

15-May-12 9.00 AM 4,700 14 14 13 5 5 4

16-May-12 9.00 AM 4,700 12 8 8 14 9 10

17-May-12 9.30 AM 4,675 10 10 7 10 10 10

18-May-12 8.15 AM 3,800 13 11 11 10 12 7

19-May-12 4.15 AM 3,800 15 10 10 11 8 5

21-May-12 8.00 PM 3,800 13 10 8 13 6.8 6.2

22-May-12 10.00 PM 3,800 15 15 9 14 8 6

23-May-12 8.00 AM 3,850 15 12 10 14 12 10

Fig. 6 Results of sensitivity and specificity

Fig. 7 Results on false positive rate and false discovery rate

Fig. 8 Results on false predicted value and negative predicted value

Fig. 9 Results on accuracy


123

major faults (Classes) are considered unbalance, mis-

alignment and bearing fault, basing on this the output

shows a value 1, 2 or 3 respectively.

Power and process industries use pumps as vital part of

engineering systems. Due to the imposed rotation by the

impeller and its interaction with the volute casing, flow

through the centrifugal pump is complex. The importance

of performance and condition monitoring of these pumps is

seen in identifying the decreased performance, avoiding the

unplanned shutdowns, predicting and planning preventive

maintenance and enhancing service life. The operating

stability and service life are directly influenced by the

vibration analysis. The hybrid expert system is developing

as a handy computational instrument for the forecast of

pump execution and in identification of faults.

Sensitivity is defined as the likelihood where the test

says machinery equipment has the deficiency when indeed

they do have the issue. Specificity is characterized as the

likelihood that the test says machinery equipment does not

have the flaw when actually they are fat free. A perfect test

ought to have high affectability and high specificity. Off

and on again, there are tradeoffs regarding affect sensitivity

and specificity. For instance, a test could be made high

sensitivity; however, this frequently brings about low

specificity. As shown in Fig. 6, the proposed SVMHAPAG

has the highest sensitivity of 74 %, whereas other tech-

niques have sensitivity of around 71 to 72 %. Specificity

plot, Fig. 6 shows that, HAPAG yields 81 %, whereas

remaining techniques yield around 70–75 %.

A false positive rate is a result that indicates that there is

a fault, whereas actually there is no fault. False positive

rate of all the techniques is specified in Fig. 7.

Positive and negative predictive values are influenced by

the prevalence of disease in the population that is being

tested. When this is tested in a high prevalence setting, it is

more likely that equipment whose test positive truly has fault

than if the test is performed in a population with low prev-

alence. Positive predicted value is the portion of the antici-

pated positives which are right. Negative predicted value is

the division of the negative expectations which are right. The

positive predicted value and negative predicted value of

SVMHAPAG are shown in comparative figures in Fig. 8.

Accuracy is defined as the ability to work or perform

without mistakes. The accuracy for SVMHAPAG has a

very high accuracy of 78 % compared to other systems

which have an accuracy rate in the range 70–75 % as

shown in Fig. 9.

Mean, SD, Max and Min specify the mean value, stan-

dard deviation, maximum value and minimum value

respectively. Error rate in the tables refers to the percentage

of wrong classification produced by the trained ANNs on

the training set and the testing set. The time taken by the

CPU for executing instructions is the CPU time. The CPU

stands idle much of the time while the computer fetches

input from the keyboard or disk or sends output to an

output device. Hence the CPU time of an executing pro-

gram is much less than the total execution time of the

program. CPU execution time is one criterion where a

process holds CPU while other processes are waiting for

execution. SVMHAPAG requires less CPU time compared

to other process as shown in Table 2.

4 Conclusion

SVMHAPAG algorithm used as hybrid adaptive particle

swarm optimization and genetic algorithm with support

vector machine is more effective than the recent standard

optimized algorithms like SVMAGA, and SVMAPSO. The

results show a better understanding of the comparative

analysis of the existing techniques based on AGA and

APSO and show that the combined AGA and APSO give

better optimization results. This can be effectively used in

problems in fault detection. This procedure can also be

applied to similar problems where sufficient training data is

available and testing analysis has to be done.

Acknowledgments The authors would like to thank the professors

of GITAM University for all the support and reviewers for their

valuable suggestions.

References

Aihong J, Lizhe Y (2010) Fault diagnosis based on adaptive genetic

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Table 2 Results of AGA, APSO and HAPAG

Algorithm Training set error

rate

Testing set error

rate

Running time

(s)

AGA

Mean 0.3486 0.4535 350.21

SD 0.0159 0.4192 9.43

Max 0.3952 0.7629 421.54

Min 0.0098 0.3952 320

APSO

Mean 0.2983 0.3139 319

SD 0.0079 0.1593 18.51

Max 0.3521 0.5123 394.63

Min 0.1031 0.1215 296.35

HAPAG

Mean 0.1842 0.1985 129

SD 0.0062 0.1021 16.43

Max 0.2015 0.2159 252.1

Min 0.0049 0.0312 160.43


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Buddha Kishore

M. R. S. Satyanarayana

K. Sujatha


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efficient fault detection using support vector machine based hybrid expert system

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