Robotics and Computer-Integrated Manufacturing 27 (2011) 115–123

Contents lists available at ScienceDirect

Robotics and Computer-Integrated Manufacturing

0736-58

doi:10.1

n Corr

fax: +9

E-m

journal homepage: www.elsevier.com/locate/rcim

Fault detection on robot manipulators using artificial neural networks

Ikbal Eski, Selcuk Erkaya, Sertac- Savas, Sahin Yildirim n

Erciyes University, Engineering Faculty, Mechatronics Engineering Department, Kayseri 38039, Turkey

a r t i c l e i n f o

Article history:

Received 1 February 2010

Received in revised form

17 June 2010

Accepted 23 June 2010

Keywords:

Fault detection

Neural network

Robot manipulator

Vibration analysis

45/$ - see front matter Crown Copyright & 2

016/j.rcim.2010.06.017

esponding author. Tel.: +90 0352 4374901 32

0 0 352 4375784.

ail address: sahiny@erciyes.edu.tr (S. Yildirim

a b s t r a c t

Nowadays, gas welding applications on vehicle’s parts with robot manipulators have increased in

automobile industry. Therefore, the speed of end-effectors of robot manipulator is affected on each joint

during the welding process with complex trajectory. For that reason, it is necessary to analyze the noise

and vibration of robot’s joints for predicting faults. This paper presents an experimental investigation

on a robot manipulator, using neural network for analyzing the vibration condition on joints. Firstly,

robot manipulator’s joints are tested with prescribed of trajectory end-effectors for the different joints

speeds. Furthermore, noise and vibration of each joint are measured. And then, the related parameters

are tested with neural network predictor to predict servicing period. In order to find robust and

adaptive neural network structure, two types of neural predictors are employed in this investigation.

The results of two approaches improved that an RBNN type can be employed to predict the vibrations

on industrial robots.

Crown Copyright & 2010 Published by Elsevier Ltd. All rights reserved.

1. Introduction

Recently, different fault detection, fault diagnoses, faultdetection and isolation, fault identification, fault-tolerance, etc.techniques for robot manipulators have been proposed in theliterature. Some of important survey papers have been presentedfor these techniques for robot manipulators. Caccavale et al. wereproposed a fault diagnosis approach for robotic manipulators,subject to faults of the joints driving systems. A model-baseddiagnostic observer was adopted to detect, isolate and identifyfailures. The effectiveness of the approach was experimentallytested on an industrial robot manipulator [1]. Halder and Sarkarwere presented a new robust nonlinear analytic redundancytechnique to actuator fault detection for input-affine nonlinearmultivariable dynamic systems. Experimental results on a PUMA560 robotic arm were presented to demonstrate the application ofthe theorem [2].

Moreover the joint velocity jump during fault tolerantoperations for redundant manipulators was defined, and theanalytical formulation of joint velocity of the reduced manip-ulator with minimum joint velocity jump was derived by Jing andCheng [3]. Two fault-tolerant control strategies for robot manip-ulators were researched by Siqueira et al. These strategies wereimplemented based on output-feedback HN controllers, andexperimental results of each controller were given [4]. Hassan andNotash were presented a methodology to modify the kinematiclayout of parallel manipulators to provide fault-tolerance to

010 Published by Elsevier Ltd. All

053;

).

active-joint jam at a large number of end-effector poses. Theoptimization procedure was applied successfully on a 6-degree offreedom example manipulator [5]. Similarly, modification ofparallel manipulators to achieve fault-tolerance to active-jointjam was performed by equipping each branch with a backuprevolute joint at a pre-assigned location [6]. Another study,problem of fault-tolerance in cooperative manipulators rigidlyconnected to a load was investigated [7].

In order to stabilize the reinforcement-learning environment,Yasuda and Ohkura were used a neural network for predicting theaverage of the other robots’ postures at the next time step.Computer simulations were conducted to illustrate the fault-tolerance of our MRS against a system change that occurs after theMRS achieves stable behavior [8]. Verma and Simmons werepresented a new algorithm for computationally tractable faultdiagnosis. Experiments with a dynamic simulation of a six-wheelrocker-bogie rover showed a significant improvement in perfor-mance over the classical approach [9]. Fault tolerant motionplanning of two spatial coordinating manipulators having twopossible locked joints, respectively, was researched. Simulationresult for two spatial 4R manipulators was demonstrated thevalidity of this proposed algorithm [10].

Another investigation, Notash and Huang presented meth-odologies for the design of fault tolerant parallel manipulatorsbased on the failure analysis of manipulators and optimum faulttolerant configuration of each class was identified [11]. A strategyof fault tolerant tripod gait was proposed by Yang and a periodicgait was presented, in which hexapod walking machines have themaximum stride length after a locked failure. The adjustmentprocedure from a normal gait to the proposed fault tolerant gaitwas shown to demonstrate the applicability of the proposedscheme [12].

rights reserved.

Nomenclature

b best matching unitbj bias of the jth neuron in the hidden layerbk bias of the kth neurons in the output layercj corresponding coefficientf(t) activation function of the hidden layerFð _qðtÞÞ friction term of robot manipulator’s jointsFv(t) diagonal matrix of viscous friction coefficientsg(t) activation functions of the output layerG(q(t)) vector of gravity termshbi(t) neighborhood kernel centered on the winner unithbj(t) neighborhood kernel centered at unit b

Kdn the coefficients of dynamic frictionm total number of terms in the polynomialmj(t) output function of the jth neuron in the hidden layerM number of map unitM(q(t)) inertia matrix of the robot manipulatorN number of training samplesnI number of neurons in the input layer

nH number of neurons in the hidden layernO number of neurons in the output layerrb position of neurons b on the SOMNN gridri position of neurons i on the SOMNN gridRBNN radial basis neural networksgnð _qnÞ the signum functionSOMNN self organizing map neural networkVmðqðtÞ, _qðtÞÞ vector of centrifugal and Coriolis termswij weight of the connection between the input and

hidden layer neuronswjk weight of the connection between the hidden and

output layer neuronsx vector of input variablesxi center of basis function fyi acceleration output of the joint i

zi the best matching unit of vectortd(t) disturbancess(t) control input vectora(t) adaptation coefficientg momentum term

I. Eski et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 115–123116

The sudden change of joint velocity in fault tolerant operationsfor two coordinating manipulators has been investigated by Jinget al. A performance criterion with the sudden change in jointvelocity and corresponding fault tolerant planning algorithm waspresented [13]. Liu and Coghill were presented a model-basedapproach to an online robotic fault diagnosis and simulationresults were presented in their paper [14].

In this paper, two types of neural network predictors are usedto detect actuator faults on 61 of industrial robot manipulator. Thepaper is organized as follows: Section 2 describes the theory ofrobot manipulators. Section 3 gives some details about neuralnetworks and the proposed neural network predictor. Theexperimental and simulation investigation’s results are given inSection 4. Finally, this paper is concluded with conclusions anddiscussion in Section 5.

Joint 1

Joint 5 Joint 3

Joint 4

Joint 2

Fig. 1. Experimental system setup with measuring joints.

Fig. 2. Schematic joints rotations description of the welding robot manipulator.

600

8001000

12001400

-1000-500

0500650700750800850900950

1000

X axis [mm]Y axis [mm]

Z ax

is [m

m]

Home Position

Fig. 3. Trajectory of end-effectors.

I. Eski et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 115–123 117

2. Representation of welding robot manipulator

The robots described are six-axis industrial robots withjointed-arm kinematics for all point-to-point and continuous-path controlled tasks. Their main areas of application are: (i)handling, (ii) assembly, (iii) application of adhesives, sealants andpreservatives, (iv) machining. This robot has six degrees offreedom. It is employed to analyze the vibration parameters ofjoints as shown in Figs. 1 and 2. The robot manipulator’s joints aredriven by electromechanical, with transistor controlled AC servomotors. Maximum speed of robot manipulator’s end-effectors is6000 mm/s. The positioning repetition accuracy of the robotmanipulator is�0.1 mm. The trajectory of end-effectors is givenin Fig. 3 and the axis properties for the investigated robotmanipulator are given in Table 1. The dynamics of robotmanipulator with five rigid links can be written as

MðqðtÞÞ €qðtÞþVmðqðtÞ, _qðtÞÞ _qðtÞþFð _qðtÞÞþGðqðtÞÞþsdðtÞ ¼ sðtÞ ð1Þ

where M(q(t)) is then n�n inertia matrix of the robotmanipulator, VmðqðtÞ, _qðtÞÞ is the n�1 vector of centrifugal andCoriolis terms, Fð _qðtÞÞ is the n�n friction term, G(q(t)) is n�1 thevector of gravity terms and sd(t) n�1 represents disturbances(n¼6). The control input vector s(t) has n�1 components of

Fig. 4. The welding robot manipulato

Table 1Kinematic parameter variations of the welding robot manipulator joints.

Axis Range of motion Speed

1 71851 1561/s

2 +351 to �1551 1561/s

3 +1541 to �1301 1561/s

4 73501 3431/s

5 71301 3621/s

6 73501 6591/s

torque for revolute joints and force for prismatic joints. It is oftenconvenient to write the robot manipulator nonlinear dynamics as

MðqðtÞÞ €qðtÞþNðqðtÞ, _qðtÞÞþsdðtÞ ¼ sðtÞ ð2Þ

where

NðqðtÞ, _qðtÞÞ �VmðqðtÞ, _qðtÞÞ _qðtÞþFð _qðtÞÞþGðqðtÞÞ ð3Þ

represents a vector of the nonlinear terms. As depicted from Eq.(3), the joints of robot manipulator are affected by friction terms.These terms can be described;

Fð _qðtÞÞ ¼ FvðtÞ _qðtÞþFdðð_qðtÞÞ ð4Þ

with Fv(t), a diagonal matrix of constant coefficients representingthe viscous friction and Fdðð _qðtÞÞ a vector with entries likeKdnsgnð _qnÞ with sgnð _qnÞ the signum function and Kdn thecoefficients of dynamic friction.

2.1. Structure of the welding robot manipulator controller

The welding robot manipulator controller has some propertiesas follows:

�

r co

Performance and expansion over and above the basic controlfunctions.

� Open system for future developments and ease of integration

in any network.

� Recognized standards. � Special functions for increased productivity. � Built-in safety features for greater availability. � Input functions for faster programming. � Ready-made software packages and real-time capable simula-

tions.

� Offline programs with absolutely accurate data.

The welding robot manipulator controller consists of fourcomponents. These

ntroller hardware structure.

I. Eski et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 115–123118

Control PC: the PC performs all the functions of the robotcontroller. The control PC includes the following components:motherboard with interfaces, processor and main memory,hard drive, floppy disk drive, CD-ROM drive, MFC3, KVGA, DSE-IBS-C33, batteries and bus cards.Teach pendant: the teach pendant has all the functions requiredfor operating and programming the robot system.Safety logic and power unit: the safety logic is a dual-channelcomputer aided safety system. It permanently monitors allconnected safety-relevant components. In the event of a faultor interruption in the safety circuit, the power supply to thedrives is shut off, thus bringing the robot system to a standstill.

Hidden Layer

Input Layer

SOMNN Mapping layer

W

: Linear neuron

t [second]

nH

nI

Hidden Layer Bias

1+

Input Layer

ithneuron

jthneuron

: Linear neuron

t [second]

Fig. 5. (a). Schematic representations of the SOMNN analyzer

The hardware of the system is a single processor basis. With alatest generation high-performance processor for two paralleloperates systems. The controller hardware structure of the systemis shown in Fig. 4.

3. The proposed neural network

Feedforward neural network with three layered is used toanalyze acceleration of the welding robot manipulator’s joints inthis study. Schematic representations of an RBNN and SOMNNanalyzers are shown in Fig. 5a and b.

Output Layer

Bias +1

y5[m/second2]

inner-take-all layer

: Nonlinear neuron

y4[m/second2]

y3[m/second2]

y2[m/second2]

y1[m/second2]

Output Layer

nO

saiB 1+

kthneuron

: Nonlinear neuron

y5[m/second2]

y4[m/second2]

y3[m/second2]

y2[m/second2]

y1[m/second2]

and (b). Schematic representations of the RBNN analyzer.

Accelerometers

IDA (IntelligentData Acquisition)

PC

t(second)

en (t) yn (t)

yc (t)

-

+

y(t)

Robot Manipulator

Fig. 6. Flow chart of experimental setup.

I. Eski et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 115–123 119

The output signal of hidden layer of the proposed neuralnetwork, i.e., acceleration of the welding robot manipulator’sjoints, can be described in the following equations:

mjðtÞ ¼ fX10

j ¼ 1

wijðtÞtþbj

0@

1A ð5Þ

where mj(t) is the output of the jth neuron in the hidden layer, wij

is the weight of the connection between the input layer neuronsand the hidden layer neurons, bj is the bias of the jth neuron in thehidden layer. bj can be regarded as the weight of the connectionbetween a fixed input of unit value and neuron j in the hiddenlayer. The activation function which is used as sigmoid function ofthe hidden layer

f ðtÞ ¼1

1þe�tð6Þ

The output signal of the hidden layer can be rearranged in thefollowing form:

mjðtÞ ¼1

1þe�P10

j ¼ 1wijðtÞtþbj

� � ð7Þ

The output signal of the output layer of the proposed neuralnetwork can be expressed in the following form:

ykðtÞ ¼ gX10

j ¼ 1

X5

k ¼ 1

wjkðtÞmjðtÞþbk

0@

1A ð8Þ

where g(t) is activation functions of the output layer and can bedescribed as follows:

gðtÞ ¼ t ð9Þ

The output signal of the output layer can be written by

ykðtÞ ¼X5

k ¼ 1

1

1þe�P10

j ¼ 1wijðtÞtþbj

� �264

375wjkðtÞþbk ð10Þ

where wjk are the weights between jth neurons hidden layer andkth neurons output layer and bk is the bias of the kth neurons inthe output layer. Two learning methods, which are used toanalyze accelerations of the welding robot manipulator’s joints,are briefly described in the following subsections.

3.1. Self organizing map neural network (SOMNN)

SOMNN consists of a regular, usually two-dimensional, gridof map units. Each unit is represented by a prototype vector,where input vector is dimension. The units are connectedto adjacent ones by a neighborhood relation. The number ofmap units, which typically varies from a few dozen up to severalthousand, determines the accuracy and generalization capabilityof the SOMNN. Data points lying near each other in theinput space are mapped onto nearby map units. Thus, the SOMNNcan be interpreted as a topology preserving mapping frominput space onto the 2-D grid of map units. The SOMNN istrained iteratively. At each training step, a sample vector z israndomly chosen from the input data set. Distances betweenz and all the prototype vectors are computed. The best matchingunit, which is denoted here by b, is the map unit with prototypeclosest to z

Jz�mb99¼mini

99z�mi99� �

ð11Þ

The update rule for the prototype vector of unit i is

miðtþ1Þ ¼miðtÞþaðtÞhbiðtÞ½z�miðtÞ� ð12Þ

0 500 1000 1500 2000 2500 3000-10

-505

10152025

Frequency [Hz]

Noi

se [d

B(A

)]

Fig. 7. Noise variation of the welding robot manipulator with 1 rpm running

speed.

0 5 10 15 20 25 30 35 40

Joint 1

Time(Second)

Acc

eler

atio

n(m

/sec

2 )

0 5 10 15 20 25 30 35 40

0

0.2

0.4

0.6

0.8

0.1

0.15

0.2

0.25

0.3

0.35Joint 2

Time(Second)

Acc

eler

atio

n(m

/sec

2 )

Measured SOMNN

Fig. 8. Acceleration variations of the welding robot manipulator joints 1 and 2

with 1 rpm running speed, using the SOMNN structure.

0 5 10 15 20 25 30 35 40

Joint 3

Acc

eler

atio

n(m

/sec

2 )

0 5 10 15 20 25 30 35 40

Joint 4

Time(Second)

Acc

eler

atio

n(m

/sec

2 )

0 5 10 15 20 25 30 35 40

0

0.2

0.4

0.6

0.8

-0.5

0

0.5

1

-0.5

0

0.5

1

1.5Joint 5

Time(Second)

Acc

eler

atio

n(m

/sec

2 )

Measured SOMNN

Time(Second)

Fig. 9. Acceleration variations of the welding robot manipulator joints 3, 4 and 5

with 1 rpm running speed, using the SOMNN structure.

I. Eski et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 115–123120

where a(t) is an adaptation coefficient, hbi(t) is the neighborhoodkernel centered on the winner unit.

hbiðtÞ ¼ exp �Jrb�ri99

2

2s2ðtÞ

!ð13Þ

where rb and ri are positions of neurons b and i on the SOMNNgrid. Both a(t) and s(t) decrease monotonically with time. In thecase of a discrete data set and fixed neighborhood kernel, theerrors function of SOMN is as follows:

E¼XN

i ¼ 1

XMj ¼ 1

hbjJzi�mj992

ð14Þ

where N is number of training samples and M is the number ofmap units. Neighborhood kernel hbj is centered at unit b, which isthe best matching unit of vector zi, and evaluated for unit j [15].

3.2. Radial basis neural network (RBNN)

Radial basis neural network (RBNN) is feedforward and hasonly one hidden layer. The RBNN structure is good at modelingnonlinear data and can be trained in one stage rather than using an

iterative process as in multi-layer perception neural network andalso learn the given application quickly. They are useful in solvingproblems, where the input data are corrupted with additive noise.

The transformation functions used are based on a Gaussiandistribution. If the error of the network is minimized appro-priately, it will produce outputs that sum to unity, which willrepresent a probability for the outputs. The RBNN structure has aninput layer, a hidden layer of radial units and an output layer oflinear units. The RBNN model is mathematically represented asfollows:

f ðxÞ ¼Xn

i ¼ 1

wifðJx�xi99ÞþXm

j ¼ 1

cjpjðxÞ ð15Þ

where n is the number of sampling points, x is the vector of inputvariables, xi is the center of basis function f, wi is the unknownweighting coefficient, m is the total number of terms in thepolynomial and cj is the corresponding coefficient. Therefore, anRBNN is actually a linear combination of n basis functions withweighted coefficients. In this study, Gaussian basis function isused as follows:

fðtÞ ¼ e�ct2

, 0ocr1 ð16Þ

4. Experimental and simulation analyses

Experimental and simulation investigation on robot manip-ulator’s fault detection has been carried out in two stages. First

0 5 10 15 20 25 30 35 40

Joint 1

Time(Second)

0 5 10 15 20 25 30 35 40

0

0.2

0.4

0.6

0.8

0.1

0.15

0.2

0.25

0.3

0.35Joint 2

Time(Second)

Acc

eler

atio

n(m

/sec

2 )A

ccel

erat

ion(

m/s

ec2 )

Measured RBNN

Fig. 10. Acceleration variations of the welding robot manipulator joints 1 and 2

with 1 rpm running speed, using an RBNN structure.

0 5 10 15 20 25 30 35 400

0.2

0.4

0.6

0.8Joint 3

Time(Second)

0 5 10 15 20 25 30 35 40

Joint 4

Time(Second)

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30 35 400

0.5

1

1.5Joint 5

Time(Second)

Acc

eler

atio

n(m

/sec

2 )A

ccel

erat

ion(

m/s

ec2 )

Acc

eler

atio

n(m

/sec

2 )

Measured RBNN

Fig. 11. Acceleration variations of the welding robot manipulator joints 3, 4 and 5

with 1 rpm running speed, using an RBNN structure.

0 500 1000 1500 2000 2500 3000-10-505

10152025

Frequency [Hz]

Nois

e [d

B(A)

]

Fig. 12. Noise variation of the welding robot manipulator with 2 rpm running

speed.

I. Eski et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 115–123 121

stage is experimental measurements on robot manipulator’sjoints. The process consists of an intelligent data acquisition(IDA), 5 accelerometers, a microphone and PC. The experimentalsetup, used to collect the joint accelerations for the case of thetwo different running speeds of welding robot manipulator, isgiven in Fig. 6. On the second stage, the measured experimentalaccelerations values are used as desired signals of neuralnetworks. Neural networks are tested to predict exact signalsof robot manipulator’s joints with two types of learningalgorithms.

The setup used for the experiments and a schematic diagramare represented in Fig. 6. Experimental and simulation results areshown in Figs. 7–16, respectively. By considering the 1 rpmrunning speed, the experimental noise variation of the weldingrobot manipulator is given in Fig. 7. Figs. 8 and 9 present theacceleration results of the desired SOMNN approach andexperimental results of the welding robot manipulator jointswith 1 rpm running speeds for the desired trajectory. It is clear tosee from graphs; that there are differences between experimentaland the SOMNN results for both cases. In particular, thesedifferences occur at the peak values of the experimentalmeasurements.

The other type of neural network, i.e., the RBNN structure isused to predict acceleration variations of the welding robotmanipulator joints with 1 rpm running speed and the results areshown in Figs. 10 and 11. As seen in relevant figures, the RBNNapproach is robust stable to analyze the vibration parameters ofsuch welding robot manipulator joints for different prescribedand joint speeds.

Furthermore, Fig. 12 shows noise variation of the weldingrobot manipulator for the case of 2 rpm running speed. The noisevariations for 1 and 2 rpm are evaluated together; the main factoris not the robot. The system equipments such as control panel,etc. are very effective on the noise generation. Figs. 13 and 14indicate the results of experimental and SOMNN approach for

acceleration variations of the welding robot manipulator jointswith 2 rpm running speed. As pointed out from the figures, theresults of SOMNN approach give poor performance. Again, theRBNN structure is used to predict acceleration of the weldingrobot manipulator joints with 2 rpm running speed (see Figs. 15and 16). Also, Table 2 shows structural, training parameters androot mean square errors (RMSEs) of neural networks. It is clearto see from figures and Table 2, the proposed RBNN approachhas superior performance for predicting accelerations variationof the welding robot manipulator joints for different runningspeeds.

0 2 4 6 8 10 12 14 16 18 20 22

Joint 3

Time(Second)

Acc

eler

atio

n(m

/sec

2 )A

ccel

erat

ion(

m/s

ec2 )

Acc

eler

atio

n(m

/sec

2 )

0 2 4 6 8 10 12 14 16 18 20 22

Joint 4

Time(Second)

0 2 4 6 8 10 12 14 16 18 20 22

-1

0

1

2

3

0

1

2

3

4

-2

0

2

4

6Joint 5

Time(Second)

Measured SOMNN

Fig. 14. Acceleration variations of the welding robot manipulator joints 3, 4 and 5

with 2 rpm running speed, using the SOMNN structure.

0 2 4 6 8 10 12 14 16 18 20 22

Joint 1

Time(Second)

Acc

eler

atio

n(m

/sec

2 )A

ccel

erat

ion(

m/s

ec2 )

0 2 4 6 8 10 12 14 16 18 20 22

0

0.5

1

1.5

0.1

0.2

0.3

0.4

0.5

0.6Joint 2

Time(Second)

Measured RBNN

Fig. 15. Acceleration variations of the welding robot manipulator joints 1 and 2

with 2 rpm running speed, using an RBNN structure.

0 2 4 6 8 10 12 14 16 18 20 22

Joint 1

Time(Second)

Acc

eler

atio

n(m

/sec

2 )

0 2 4 6 8 10 12 14 16 18 20 22

-0.5

0

0.5

1

1.5

0.1

0.2

0.3

0.4

0.5

0.6Joint 2

Time(Second)

Measured SOMNN

Acc

eler

atio

n(m

/sec

2 )

Fig. 13. Acceleration variations of the welding robot manipulator joints 1 and 2

with 2 rpm running speed, using the SOMNN structure.

0 2 4 6 8 10 12 14 16 18 20 22

Joint 3

Time(Second)

Acc

eler

atio

n(m

/sec

2 )

0 2 4 6 8 10 12 14 16 18 20 22

Joint 4

Time(Second)

0 2 4 6 8 10 12 14 16 18 20 22

00.5

11.5

22.5

0

1

2

3

4

0

12345

Joint 5

Time(Second)

Measured RBNN

Acc

eler

atio

n(m

/sec

2 )A

ccel

erat

ion(

m/s

ec2 )

Fig. 16. Acceleration variations of the welding robot manipulator joints 3, 4 and 5

with 2 rpm running speed, using an RBNN structure.

I. Eski et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 115–123122

Table 2Structural and training parameters of the feedforward neural networks with two

learning algorithms.

Running speed of end-effectors

NNtype

g c N nI nH nO RMSEs

1 rpm SOMNN 0.4 0.5 400,000,000 1 10 5 0.0600

1 rpm RBNN 0.4 0.5 400,000,000 1 10 5 0.00042 rpm SOMNN 0.4 0.5 400,000,000 1 10 5 0.0439

2 rpm RBNN 0.4 0.5 400,000,000 1 10 5 0.0004

I. Eski et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 115–123 123

5. Conclusion and discussion

In this study, fault detection based neural network for anexperimental industrial welding robot has been implemented. Jointaccelerations of robot are considered as evaluation criteria. For thispurpose, an experimental setup is used to collect the related values.The accelerations of welding robot, which has six degrees offreedom, are analyzed. In order to design an optimal neuralestimator, two types of neural networks are used for compar-ingeach other. The results obtained for both the running speedsshow that the proposed RBNN has a robust stability to analyze theaccelerations of manipulator joints during a prescribed trajectory.The major advantage of an RBNN uses Gaussian function, gives aresponse that drops off rapidly as the distance between the hiddenunit and the input vector increases and is symmetrical about theradial axis. It has been scientifically proved that the RBNN structureis given the best approach rather than other structure.

The proposed neural network based fault detection techniquecould be employed to predict fault isolation of robot manipulatorjoints in considering with a larger number of degrees of freedom.

References

[1] Caccavale F, Cilibrizzi P, Pierri F, Villani L. Actuators fault diagnosis for robotmanipulators with uncertain model. Control Engineering Practice 2009;17:146–157.

[2] Halder B, Sarkar N, Analysis of order of redundancy relation for robustactuator fault detection, Control Engineering Practice 2009;17:966–73.

[3] Jing Z, Cheng F. On the joint velocity jump during fault tolerant operations formanipulators with multiple degrees of redundancy. Mechanism and MachineTheory 2009;44:1201–10.

[4] Siqueira AAG, Terra MH, Buosi C. Fault-tolerant robot manipulators based onoutput-feedback HN controllers. Robotics and Autonomous Systems 2007;55:785–94.

[5] Hassan M, Notash L. Optimizing fault tolerance to joint jam in the designof parallel robot manipulator. Mechanism and Machine Theory 2007;42:1401–1417.

[6] Hassan M, Notash L. Design modification of parallel manipulators foroptimum fault tolerance to joint jam. Mechanism and Machine Theory 2005;40:559–77.

[7] Tinos R, Terra MH, Bergerman M. A fault tolerance framework for cooperativerobotic manipulators. Control Engineering Practice 2007;15:615–25.

[8] Yasuda T, Ohkura K, Ueda K. A homogeneous mobile robot team that is fault-tolerant. Advanced Engineering Informatics 2006;20:301–11.

[9] Verma V, Simmons R. Scalable robot fault detection and identification.Robotics and Autonomous Systems 2006;54:184–91.

[10] Jing Z, Kailiang Z, Xuebin Y. Study on fault tolerant workspace and fault tolerantplanning algorithm based on optimal initial position for two spatial coordinatingmanipulators. Mechanism and Machine Theory 2006;41:584–95.

[11] Notash L, Huang L. On the design of fault tolerant parallel manipulators.Mechanism and Machine Theory 2003;38:85–101.

[12] Yang JM. Tripod gaits for fault tolerance of hexapod walking machines with alocked joint failure. Robotics and Autonomous Systems 2005;52:180–9.

[13] Jing Z, Xuebin Y, Lei Z. The optimization of initial posture with avoidance ofthe sudden change in joint velocity for fault tolerant operations of twocoordinating redundant manipulators. Mechanism and Machine Theory2005;40:659–68.

[14] Liu H, Coghill GM. A model-based approach to robot fault diagnosis.Knowledge-Based Systems 2005;18:225–33.

[15] Vesanto J, Alhoniemi E. Clustering of the Self-Organizing Map. IEEETransactions on Neural Networks 2000;11:586–600.