28 DOI: 10.5545/sv-jme.2024.999
Strojniški vestnik - Journal of Mechanical Engineering   ▪   VOL 71   ▪   NO 1-2   ▪  Y 2025 © The Authors. CC-BY 4.0 Int. Licencee: SV-JME
Quantitative Sequential Modelling Approach  
to Estimate the Reliability of Computer Controlled 
Pneumatically Operated Pick-and-Place Robot
Satheesh Pandian Durairaj 
KLN College of Engineering, India
  satheesh.klnce@gmail.com
Abstract Pneumatically controlled pick-and-place robots are an integral part of contemporary manufacturing processes and have the potential to enhance 
the precision and velocity of numerous production-related tasks. As automation continues to disrupt various sectors, the role of these robots is becoming 
increasingly crucial. Achieving peak performance in robot operations requires a relentless focus on safety and reliability. As a result, subsequence reliability 
should be considered carefully from the initial part of the design phase. Roboticists need to identify the dependable subsequences and parts of the robot’s 
operational framework in prior to access the system’s overall reliability. This analytical technique aids in component identification and demonstrates how to 
measure redundancy to ensure dependability and robustness. Based on recent theories and frameworks, the researchers can understand the factors that have 
more impact towards the reliability of pneumatically driven pick-and-place robots. Thus, this research work improves an exhaustive dependability analysis of a 
computer-controlled pneumatically operated pick-and-place robot. As part of our methodology, we use modern LabVIEW software to conduct a comprehensive 
failure analysis and estimate the reliability of the sequence.
Keywords pneumatically controlled pick-and-place robots, automation, reliability, LabVIEW software, failure analysis
Highlights:
 ▪ Modern factories use pneumatic robots to boost production speed and accuracy.
 ▪ Subsequence reliability ensures safety and optimal system performance.
 ▪ LabVIEW aids in dependability analysis, identifying key parts and redundancies.
 ▪ MRPM model focuses on redundancy and quantitative reliability calculations.
1  INTRODUCTION
Pneumatically operated pick-and-place robots are essential 
components of contemporary manufacturing operations, providing 
accuracy and efficiency in task execution. These robots employ 
compressed air as propulsion, enabling them to execute rapid and 
precise pick-and-place operations within assembly lines and other 
manufacturing settings. The relevance of these entities lies in their 
capacity to optimize efficiency through the reduction of cycle times 
and the augmentation of throughput. Pneumatically controlled 
pick-and-place robots significantly enhance efficiency and quality 
assurance within manufacturing operations by effectively managing 
a wide range of materials and components [1]. 
The importance of automation in manufacturing is growing, as it 
brings about a significant transformation in industries by improving 
efficiency and production. Robots facilitate this shift by optimizing 
industrial processes accurately and efficiently. Their adaptability 
enables the completion of diverse activities, ranging from assembly 
to packaging, with uniformity and precision. In the current market 
landscape, firms are compelled to adopt automation and robots to 
maintain competitiveness, foster innovation, and effectively address 
the changing needs of consumers [2] and [3].
Safety and reliability are of utmost importance in robot activities 
within industrial settings, owing to the substantial hazards associated 
with such operations. Robots frequently collaborate with humans, 
operating extensive machinery and possibly dangerous substances. 
Using stringent safety standards mitigates the likelihood of accidents, 
injuries, and equipment damage. Moreover, ensuring dependable 
robot performance is essential for sustaining output and averting 
expensive periods of inactivity. The prioritization of safety and 
reliability serves the dual purpose of safeguarding workers and assets 
while cultivating a working environment that promotes efficiency 
and success [4]. 
Ensuring the entire reliability of the system is crucial during the 
design phase of robotics, with a particular emphasis on sequence 
reliability. Through careful evaluation of the dependability of 
each subsequence from the beginning, engineers will detect any 
vulnerabilities and enhance them for optimal reliability. This entails 
evaluating individual elements’ dependability and interplay within 
the administrative structure. Through this approach, designers 
can minimize potential hazards and boost the robot’s overall 
dependability, resulting in heightened operational availability, 
decreased expenses associated with maintenance, and improved 
efficacy in practical scenarios [5] to [7]. 
Analytical methodologies are of paramount importance in 
evaluating the dependability of robotic systems. Roboticists employ 
many techniques, including failure mode and effect analysis 
(FMEA), fault tree analysis (FTA), and reliability block diagrams 
(RBD), to detect potential failure modes and assess their influence 
on system reliability. Identifying components is a crucial aspect of 
this process, as it enables the recognition of essential elements and 
their respective probabilities of failure. Furthermore, redundancy 
techniques are employed to improve reliability and resilience, which 
may include the integration of backup components or duplicate 

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systems. By utilizing these analytical methodologies, roboticists can 
proficiently assess and enhance the dependability of robotic systems, 
guaranteeing optimal functionality across a wide range of operational 
contexts [8]. 
Current studies have concentrated on comprehending the variables 
that impact the dependability of pneumatically operated pick-and-
place robots. This entails analyzing the impact of factors such as 
air pressure variations, component deterioration, and environmental 
circumstances on the system’s functioning. Researchers can 
build theories and frameworks to optimize these robots’ design, 
maintenance, and operation by gaining insights into these issues. 
The comprehension of these factors has significant importance 
in enhancing the overall reliability of a system, as it empowers 
engineers to enact proactive steps, such as carefully selecting sturdy 
components and establishing regular maintenance plans, to reduce 
prospective concerns. Eventually, it improves the reliability and 
durability of pneumatically operated pick-and-place robots in many 
industrial settings. 
The primary aim of this study is to conduct a comprehensive 
examination of the reliability of a computer-controlled pneumatically 
operated pick-and-place robot. This seeks to assess multiple elements 
that impact the dependability of the robot, such as its components, 
operational conditions, and environmental factors. Through a 
comprehensive examination, this research aims to offer valuable 
insights that can improve the design, maintenance, and operation 
of comparable robotic systems. Finally, the objective is to enhance 
the reliability and performance of these systems in industrial 
environments. 
The research approach employed in this study entails the utilization 
of contemporary LabVIEW software to perform an exhaustive failure 
analysis and assess the reliability of sequences. LabVIEW is a robust 
programming environment that empowers researchers to create 
customized programs to accomplish their research requirements. In 
our research, it enables the examination of several failure modes and 
their influence on the dependability of the pneumatically controlled 
pick-and-place robot. Using LabVIEW’s functionalities, researchers 
can effectively collect and analyze data, enabling a comprehensive 
assessment of the system’s reliability [9] and [10].
2  METHODS AND MATERIALS
The computer-controlled robot uses three pneumatic cylinders to 
rotate its base, lift things, and clamp them. The movable arm of the 
robotic system is fixed on a rotating base that can move from the 
source point to the target point in the appropriate locations. The base 
rotation is controlled by rack and pinion mechanism, which is fully 
operated by pneumatic cylinders and controlled with computers 
through solenoid direction control valves (DCV) [11]. The electrical 
relays and data acquisition cards (DAQ cards) with hardware 
interfacing circuits are connected with LabVIEW software are used 
to control the robot. The major components in automatic robot are 
shown in the Fig. 3.
The pneumatic cylinders 1, 2, and 3 in the pick-and-place robot 
are sequentially activated by energizing the solenoid coils 1, 2, and 
3. The first cylinder serves the purpose of securing the objects within 
the conveyor line. In contrast, the second cylinder elevates the robotic 
arm to a predetermined height, preventing potential collisions with 
other peripherals linked to the machining center. Subsequently, the 
third cylinder is engaged to rotate the base to the designated position 
to position the components. The operational sequence exhibits 
a uniform temporal delay of five seconds. To obtain the reverse 
sequences in the pick and place robot, the computer consecutively 
energizes solenoid coils 2, 1, and 3 with a time delay of five seconds. 
Fig. 1 depicts the connectivity diagram of a computer-controlled 
pick-and-place robot [12].
The successive parts encompass the steps involved in 
sequence reliability estimation, the collection of failure data for 
each subsequence, the experimental test, the assessment of the 
failure probability function, the analysis of computer reliability, 
the subsequence reliability model, the results obtained, and the 
subsequent analysis and interpretation. 
Fig. 1.  Connectivity diagram of computer-controlled pick and place robot
The concluding section elucidates the outcome of this endeavor. 
The image of the pick and place robot (prototype) is taken for 
reliability analysis is shown in Fig 2.
Fig. 2.  Computer controlled pick and place robotic arm
2.1  Steps in Sequence Reliability Estimation
Sequential reliability estimation is a process used to assess the 
reliability of a system or process over time, often in the context of 
ongoing operation or testing [13] and [14]. Here are the general steps 
involved in sequential reliability estimation:
•	 Define the system: Clearly define the system or process you are 
evaluating for reliability. This includes identifying all compo-
nents, subsystems, and their interconnections.
•	 Identify failure modes: Determine the potential failure modes 
of each component or subsystem within the system. This entails 
knowing the circumstances or stresses that can lead to failure as 
well as how each component can fail.
•	 Define reliability metrics: Establish the reliability metrics that will 
be used to evaluate the system. Common metrics include mean 
time between failures (MTBF), probability of failure within a giv-
en time frame, or reliability function over time.
•	 Data collection: Collect data on system performance and failures 
over time. This data may come from field observations, testing, or 

Mechatronics
30   ▪   SV-JME   ▪   VOL 71   ▪   NO 1-2 ▪   Y 2025
simulations. Ensure that the data is comprehensive and accurately 
reflects the operational conditions of the system.
•	 Model development: Develop a statistical model to represent the 
reliability of the system based on the collected data. This model 
describe factors such as time, usage, environmental conditions, 
and maintenance activities.
•	 Initial reliability assessment: Use the initial data and model to es-
timate the current reliability of the system. This provides a base-
line for comparison as the analysis progresses.
•	 Continuous monitoring: Continuously monitor the system for ad-
ditional failures and collect updated data. This may involve real-
time monitoring, periodic inspections, or ongoing testing
•	 Update model: Incorporate new data into the statistical model and 
update the reliability estimates accordingly. This allows the relia-
bility assessment to adapt to changes in the system’s performance 
over time.
•	 Evaluate trends: Analyze the trend of reliability over time to iden-
tify patterns or anomalies. Look for factors that may be influenc-
ing reliability, such as changes in operating conditions or mainte-
nance practices.
•	 Risk assessment: Assess the implications of the observed reliabil-
ity trends on system performance, safety, and cost. Identify poten-
tial risks associated with ongoing operation and determine if any 
corrective actions are necessary.
•	 Decision making: Use the reliability estimates and risk assessment 
to inform decision-making processes regarding system mainte-
nance, repair, or replacement. Consider factors such as cost-effec-
tiveness, safety, and operational requirements.
•	 Iterative process: Sequential reliability estimation is an iterative 
process that may require periodic updates and adjustments as new 
data becomes available or as the system undergoes changes. Con-
tinuously refine the analysis to improve the accuracy of reliability 
assessments and optimize system performance.
By following these steps, organizations can effectively assess and 
manage the reliability of their systems over time, helping to ensure 
safe and efficient operation. 
Fig. 5 illustrates the systematic failure analysis of each sub-
sequential component in the computer-controlled pick-and-place 
robot as part of the reliability analysis.
2.2  Connectivity of the Sequences
The concept of connectivity within a pick and place robot system 
pertains to the coherent integration and synchronization of diverse 
Fig. 3.  Major components of automatic pick and place robot
Fig. 4.  Connectivity diagram of the sequences

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components and processes that are fundamental to the functioning 
of the system [15]. The major components in the sequences are 
illustrated in Fig. 4.
2.2.1  Sequence 1
The activation of the solenoid DCV is accomplished by the computer, 
which transmits a signal to the universal serial bus (USB) card [16]. 
The solenoid DCV is responsible for starting pneumatic cylinder 1, 
which facilitates the execution of the clamping action via the ball 
joint. Activating the pneumatic module involves using pneumatic 
accessory components, including an air compressor, filter, and 
solenoid DCV .
Fig. 5.  Steps in sequential reliability estimation
Let, P
11
 probability of failure for component C
11
, P
12
 probability 
of failure for component C
12
, P
13
 probability of failure for component 
C
13
, P
14
 probability of failure for component C
14
, P
15
 probability of 
failure for component C
15
, P
16
 probability of failure for component 
C
16
, and P
17
 probability of failure for component C
17
.
2.2.2  Sequence 2
In sequence 2, the robot comprises a pneumatic module and a USB 
acquisition card connected to a computer. This configuration is 
similar to sequence 1, except for the ball joint.
Let, P
21
 probability of failure for component C
21
, P
22
 probability 
of failure for component C
22
, P
23
 probability of failure for component 
C
23
, P
24
 probability of failure for component C
24
, P
25
 probability 
of failure for component C
25
, and P
26
 probability of failure for 
component C
26
.
2.2.3  Sequence 3
Sequence 3 encompasses all the constituent elements included in 
sequence 1 except the ball joint. Additionally, it incorporates the rack 
and pinion, base, and column structures.
Let, P
31
 is probability of failure for component C
31
, P
32 
probability 
of failure for component C
32
, P
33
 probability of failure for component 
C
33
, P
34
 probability of failure for component C
34
, P
35
 probability of 
failure for component C
35
, P
36
 probability of failure for component 
C
36
, P
37
 probability of failure for component C
37
, and P
38
 probability 
of failure for component C
38
.
2.3  Failure Data Collection for Each Sub Sequence
The failure rate for each sub-sequential component of sequences 1, 
2, and 3 is measured in million hours. The fault data is obtained by 
measuring the difference between the necessary output value and 
the actual output value of the subcomponents under the condition of 
random input values. The robot’s overall functioning primarily relies 
on a computer connected to a USB 6008 DAQ card. It is necessary 
to employ a distinct evaluation approach to assess the reliability 
of both the computer and the USB DAQ card. This dependability 
prediction model is beneficial for obtaining accurate outcomes in 
analytical tasks [17]. Except for the failure rate data for the computer 
and USB DAQ card, the remaining component data is presented in 
the subsequent table, arranged sequentially, specifically numbered 1, 
2, and 3.
3  EXPERIMENTAL
Accurate prediction of reliability in mechanical subsystem is crucial 
for the robot as it significantly influences its total functionality. The 
reliability of each material in the mechanical structure is computed 
and utilized as input values to forecast the entire automatic machine 
using the merged reliability prediction model (MRPM). Tension 
and torsion tests are performed on various materials, including 
mild steel, cast iron, and aluminum. Subsequently, to determine the 
failure probability and dependability for each material, obtaining the 
failure rate distribution for different load circumstances is necessary. 
Therefore, the probability distribution is employed to represent 
the distribution of failure rates. The selection of the appropriate 
material for constructing the mechanical frame structure of automatic 
machines based on the failure probability and reliability values. 
Computer-based pneumatically controlled pick and place robots 
utilize materials such as mild steel, cast iron, and aluminum due to 
their adequate mechanical strength and stress resistance for various 
operating load circumstances at each structural point. Furthermore, 
these components can endure thermal characteristics, such as heat 
capacity, thermal expansion, thermal conductivity, and thermal stress, 
at the operational temperature of the automation system within each 
sub-module. Therefore, the materials mentioned above are chosen to 
conduct tension and torsion tests to analyze reliability [18].
3.1  Failure Probability Function
Probabilistic distribution for loads
PX XF X
e
XS S
X
X
() () .  
 










2
2
2
2
 (1)
Standard deviation of load 

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32   ▪   SV-JME   ▪   VOL 71   ▪   NO 1-2 ▪   Y 2025
( σ
load
) = X
S
 – X
X
 . (2)
Failure probability
(P
X
) = 1 – F(X
S
). (3)
The given equation, Eq. (1) uses the normal probability 
distribution function to characterize the distribution of loads on 
the materials. This distribution, which spans the range between the 
ultimate load and the actual load, determines the materials’ failure 
rate. When the load fluctuates within its elastic limit about the mean 
load, every material undergoes a failure rate. The average probability 
distribution determines the probabilistic distribution of failure rates 
for the actual load applied during material testing [19]. The failure 
rate and reliability data for the selected materials and standard 
mechanical accessories are presented in Tables 1 and 2.
Table 1.  Failure rate and reliability of various materials
Material
Predicted 
operating 
load, X
X
 
[kN]
Probabilistic 
distribution 
for actual 
load, P
X
Actual 
stress
[N/mm
2
]
Probabilistic 
distribution for
actual stress
Reliability
1–P(X)
Mild steel 30 9.973×10
–3
149.2 2.005×10
–3
0.99899
Cast iron 5 0.1101 10.1859 0.06228 0.8899
Aluminum 0.8 0.054200 15.915 0.07574 0.92426
Table 2.  Failure rate and reliability data for common Mechanical parts
Components
Time
[10
6
 h]
No. of
failures
Failure
rate
Probability of 
failure rate
* 
×10
–4
Reliabiltiy
**
Average 
reliability
Directional
control
valves
(solenoid),
C
14
1 8.993 8.993 0.09 0.999991
0.999901
2 35.972 17.986 0.36 0.999964
3 80.937 26.979 0.81 0.999919
4 143.888 35.972 1.44 0.999856
5 224.825 44.965 2.25 0.999775
Pneumatic
actuators,
C
15
1 11.498 11.498 0.12 0.999988
0.9998848
2 45.992 22.996 0.46 0.999954
3 103.482 34.494 1.04 0.999896
4 183.968 45.992 1.84 0.999816
5 287.45 57.490 2.3 0.999770
*
(P
kl
 = 1– e
–λt
),  
**
(R
kl
 = 1– P
kl
)
Table 3.  Probability of failure and reliability data for accessories
Component Probability of failure (P) Reliabiltiy (R)
Compressor 0.00621 0.99379
Filter 0.00600 0.99400
Ball joint 0.00300 0.99700
Rack and pinion 0.10194 0.89806
Base and column 0.10840 0.89150
Table 4.  Failure rate and reliability data for DAQ card
Components Failure rate ( λ)
Probability of failure
(P
kl
 = 1– e
–λt
) × 10
–4
Reliabiltiy
(R
kl
 = 1– P
kl
)
DAQ card (C36) 0.035 0.0343 0.9657
All components’ reliability and failure statistics, except for the ball 
joints, are extracted from the table above and recorded in sequence 2. 
The compressor and filter unit failure rate was obtained from Tables 
3 and 4 and recorded.
3.2  Reliability Analysis of Computer
The assessment of computer reliability comprises the analysis of the 
stability and dependability of computer systems, hardware, software, 
and networks to ensure consistent and error-free operation over 
the course of their lifetime [20] to [22]. Computer engineering and 
system design play a crucial role in various businesses, mainly where 
downtime or malfunctions can result in substantial financial losses, 
safety risks, or data breaches.
3.2.1  Reliability Prediction Model for Computer
The non-Poisson process model has been chosen as the framework 
for assessing the performance and measuring the reliability of the 
computer system. To evaluate the reliability of a computer system, 
the likelihood of failure is determined by calculating the anticipated 
number of failures. During the testing phase, data is collected 
periodically and utilized in the non-homogeneous Poisson process 
(NHPP) and programmable logic controller (PLC) prediction models.
3.2.2  Failure Data Collection of Computer
The calendar testing method involves providing input values to the 
system and collect the data on certain computer failure. This aids in 
identifying certain failure issues under vaious scenarios within the 
system. The quantity of disparities between the actual and desired 
production is recorded and organized in the Table 5.
Table 5.  Fault data of computer in testing phase
Week
No. of  
failures
Actual failure 
rate ( λ)
Week
No. of  
failures
Actual failure 
rate ( λ)
1 4 0.066 9 3 0.05
2 3 0.05 10 5 0.0833
3 2 0.033 11 6 0.100
4 6 0.100 12 8 0.133
5 1 0.0166 13 4 0.066
6 7 0.1166 14 3 0.05
7 4 0.066 15 2 0.033
8 2 0.033
3.3  Goodness of Fit Test
The goodness of fit test is used to determine whether the failure data 
obtained is sufficient to anticipate the computer’s reliability. In this 
experiment, the hypothesis is chosen based on the adequacy of the 
acquired data for predicting dependability.
As computed, the total difference between F and F* is compared 
with the standard values in the Table 6. The computed (F – F*) value 
is lower than the critical value of 3.5 (0.3740) found in the Table 
6. As a result, the above hypothesis is accepted and the reliability 
calculation use the data above. The hypothesis test’s outcomes 
indicate that the failure data gathered has enough predictive power 
to estimate the computer’s reliability. Therefore, the reliability model 
that follows predicts the computer’s reliability [23].
The model computes the reliability R
i
 and the P
i
(x) probability of 
failure.
Expected number of failures is defined in Eq. (4) as follows
 

ww
t 
1
. (4)
Failure probability is calculated by Eq. (5) as
P
t
w
w
CPU


, (5)

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Mechatronics
and the reliability as in Eq. (6)
R
w
 = 1 – P
w
 , (6)
where, t
CPU
 is CPU execution time for week, and θ probability 
parameter (assumed as 0.16). Sample calculation to find R
w
 using Eq. 
(6), and to find P
w
 we have to calculate µw as given in Eq. (5) as 
follows. For t
w
 = 5, λ = 0.0714 and θ = 0.16, µw is calculated as

w

 

(. .)
.
.,
0 0166 01 65 1
01 6
6 333
where t
CPU
 = 8×7×5 = 280, P
w
 = 6.333/280 = 0.0226, and  
R
w
 = 1 – 0.0226 = 0.9774.
Table 6.  Goodness of fit test
Week
Number 
of failures
F
Expected number 
of failures
F
*
D = |F – F
*
|
1 4 0.066 6.316 0.0619 0.0041
2 3 0.05 6.35 0.0623 0.0123
3 2 0.033 6.349 0.0623 0.0293
4 6 0.100 6.650 0.0652 0.0348
5 1 0.0166 6.333 0.0621 0.0455
6 7 0.1166 6.949 0.0682 0.0484
7 4 0.066 6.712 0.0658 0.0002
8 2 0.033 6.514 0.0639 0.0309
9 3 0.05 6.70 0.0657 0.0157
10 5 0.0833 7.083 0.0695 0.0138
11 6 0.100 7.35 0.0721 0.0279
12 8 0.133 7.846 0.0770 0.0560
13 4 0.066 7.108 0.0697 0.0037
14 3 0.05 6.95 0.0682 0.0182
15 2 0.033 6.745 0.0662 0.0332
Total (F – F
*
) 0.3740
Table 7.  Failure rate and reliability data of computer
Week
(w)
No. of 
failures
Actual failure 
rate for CPU 
hours ( λ)
Expected
failures 
( μ)
Pobability 
of failure 
(P
w
)
Reliabiity 
(R
w
)
Reliabilty of 
computer
(R
com
)
1 4 0.066 6.316 0.1127 0.8873
0.9742
2 3 0.05 6.35 0.0566 0.9434
3 2 0.033 6.349 0.0377 0.9623
4 6 0.100 6.650 0.0296 0.9704
5 1 0.0166 6.333 0.0226 0.9774
6 7 0.1166 6.949 0.0206 0.9794
7 4 0.066 6.712 0.0171 0.9829
8 2 0.033 6.514 0.0145 0.9855
9 3 0.05 6.70 0.0132 0.9868
10 5 0.0833 7.083 0.0126 0.9874
11 6 0.100 7.35 0.0119 0.9881
12 8 0.133 7.846 0.0116 0.9884
13 4 0.066 7.108 0.0097 0.9903
14 3 0.05 6.95 0.0088 0.9912
15 2 0.033 6.745 0.0080 0.992
3.4  The Reliability of Computer, R
com
The reliability values for each week execution hours are taken from 
the Table 7 and average reliability of computer is calculated as R
com
 
= 0.9742.
3.5  The Reliability Model for Sub Sequences
For sequence 1:
RS
PPPPPP P
1
11 12 13 14 15 16 17
111111 1

               

      
2
2
7
11 12 13 14 15 16 17
RRRRRR R
.( )
For sequence 2:
RS PPPP PP
22 12 22 52 62 32 4
1111 11         
    
. (8)
here, P
21
 = P
11
, P
22
 = P
12
, P
23
 = P
13
, P
24
 = P
14
, P
25
 = P
15
, and P
26
 = P
16
.
RS RRRR RR
22 12 22 52 62 32 4
     
   
. (9)
For sequence 3:
RS
PPPPPP
P
3
31 32 33 34 35 35
3
111111
1

            

7 73 8
1
2
   








P
, (10)
here, P
31
 = P
11
, P
32
 = P
12
, P
33
 = P
13
, P
34
 = P
14
, P
35
 = P
15
, and P
36
 = P
16
.
RS
RRRRRR RR
3
31 32 33 34 35 36 37 38
2

      
   
. (11)
Table 8: Reliability for all the sequences
Sequences Components (C
kl
)
Reliability of each 
component
Reliability of 
sequences (RS)
Sequence 1
Air compressor (C
11
) 0.9938
0.9631
Filter (C
12
) 0.9940
Computer (C
13
) 0.9742
USB 6008 DAQ (C
14
) 0.9657
Solenoid DCV (C
15
) 0.9999
Pneumatic actuator (C
16
) 0.9999
Ball joint (C
17
) 0.9970
Sequence 2
Air compressor (C
21
) 0.9938
0.9291
Filter (C
22
) 0.9940
Computer (C
23
) 0.9742
USB 6008 DAQ (C
24
) 0.9657
Solenoid DCV (C
25
) 0.9999
Pneumatic actuator (C
26
) 0.9999
Sequence 3
Air compressor (C
31
) 0.9938
0.8650
Filter (C
32
) 0.9940
Computer (C
33
) 0.9742
USB 6008 DAQ (C
34
) 0.9657
Solenoid DCV (C
35
) 0.9999
Pneumatic actuator (C
36
) 0.9999
Rack and pinion (C
37
) 0.8981
Base and column (C
38
) 0.8915
The equation provided above is used to evaluate the reliability of 
each sequence for the computer-controlled pick-and-place robot by 
assessing the chance of failure with reliability. Table 8 displays the 
computed dependability of sequences 1, 2, and 3.
3.6  Reliability Calculation for Each Sequence 
Reliability calculation for each sequence can be calculated using Eqs. 
(7) to (10): 
For sequence 1:
 RS
1
 = {{0.9938×0.9940×0.9742×0.9657×0.9999×0.9999}
  +0.9970} / 2 = 0.9631.
For sequence 2:
 RS
2
 = {0.9938×0.9940×0.9999×0.9999} × {0.9742×0.9657}
        = 0.9876×0.9408 = 0.9291.

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34   ▪   SV-JME   ▪   VOL 71   ▪   NO 1-2 ▪   Y 2025
For sequence 3:
 RS
3
 = {{0.9938×0.9940×0.9742×0.9657×0.9999 ×0.9999}
         +{0.8981×0.8915}}/2 = 0.8650.
The average values of the above sequential reliabilities are used to 
quantify the overall robot system reliability as follows:
Total Reliability of robot
 R = (RS
1
 +RS
2
 +RS
3
)/3 = (0.9631+0.9291+0.8650)/3= 0.9191.
4  RESULTS AND DISCUSSION
The equation provided above is utilized to evaluate the reliability of 
each sequence for the computer-controlled pick-and-place robot by 
assessing the chance of failure. Figs. 6 to 8 display the computed 
dependability of sequences 1, 2, and 3.
Fig. 6.  Reliability of each sub component in sequence  1
Fig. 7.  Reliability of each sub component in  sequence 2
Fig. 8.  Reliability of each sub component in sequence 3
The Figs 6 and 7 presents the specifics of sequences 1 and 2. 
The USB DAQ card (C
14
) exhibits a imperfect level of reliability 
compared to other subcomponents utilized in the primary rotation 
of sequence 1 within the pick and place robot. In sequence 1, the 
solenoid DCV (C
15
) and pneumatic actuators (C
16
) exhibit superior 
dependability compared to other components, including the 
compressor (C
11
), filter (C
12
), and ball joint (C
17
). 
The Fig. 8 presents the following data for sequence 3. The base 
and column structure (C
38
) and rack and pinion (C
37
) exhibit the 
lowest reliability compared to the other components. 
The USB DAQ card (C
34
) demonstrates the second lowest 
reliability in this sequence when compared to other components 
such as compressors (C
31
), filters (C
32
), solenoid DCVs (C
35
), and 
pneumatic actuators (C
36
).
Fig. 9.  Reliability of computer (C
13
, C
23
, and C
33
) from testing data
Fig. 9 illustrates that a computer’s reliability increases as 
the testing duration is extended. This is because faults or bugs 
are identified and rectified weekly during testing. Hence, the 
dependability of a computer is enhanced before its integration with 
operational components.
Fig. 10.  Reliability for each sequence
In computer-controlled pick-and-place robots, the graph presented 
above serves the purpose of identifying the essential sequence. The 
pick and place robot utilizes the sequence 3 for base rotation, which 
exhibits comparatively lower reliability when compared to other 
sequences such as lift and down actuation and clamping. The crucial 
sequence for the autonomous pick-and-place robot in this case study 
is sequence 3. The key subcomponents of a computer-controlled 
pick-and-place robot are determined by utilizing the prior probability 
of failures derived from expert data and the failure rate data obtained 
from testing [24] to [25]. The failure probability distribution function 
and previously computed failure probability are used to rank all the 
modules in the provided graph in Fig. 10.

SV-JME   ▪   VOL 71   ▪   NO 1-2 ▪   Y 2025   ▪   35
Mechatronics
5  CONCLUSION
To enhance the dependability of the automation system, it is necessary 
to identify the intricacies of its functional components and develop a 
reliability model to measure its reliability. Using the MRPM model 
enables the designer to focus on the redundancy design. Quantitative 
reliability calculations are employed to ascertain the sequences with 
the highest criticality level. Qualitative and quantitative evidence 
can also be used to forecast prior and posterior reliability. The 
total reliability of the real-time mechatronic system was assessed 
throughout the design phase through a comprehensive failure analysis 
and a merged reliability prediction model. The absolute reliability of 
the robot was evaluated by estimating the reliability of its mechanical 
accessory components and materials. A number of materials, such as 
aluminium, cast iron, and mild steel, have their likelihood of failure 
assessed using the failure probability distribution function that was 
created from experimental test data from the tensile test. The test is 
performed on all materilas under operational load situation to find 
the probability distribution function for failure. The reliability of 
all materials pertaining to mechanical components is also evaluated 
by the computation of failure probability. The selection of the 
appropriate material for the material’s structure was determined 
using a dependability calculation, as outlined in this research article. 
Furthermore, it was determined that the base rotation sequences 
(sequence 3) hold the utmost significance in the computer control 
pick and place robot.
REFERENCES
[1] Li, S., Rameshwar, R., Votta, A.M., Onal, C.D. Intuitive control of a robotic arm and 
hand system with pneumatic haptic feedback. IEEE Robot Autom Let 4, 4424-
4430 (2019) DOI:10.1109/LRA.2019.2937483
[2] Bakrania, K., Patel, K. Failure mode and effect analysis of pneumatically operated 
pick and place robot. Int J Adv Res Comp EngTech 492-498 (2020)
[3] Jha, M.K., Raval, N.S. Analytical study on pneumatically controlled pick and place 
robot. Int J Res Appl Sci Eng Tech 6, 2384-2390 (2018)
[4] Liu, Z., Hu, J., Li, Y. Research on the reliability analysis of pneumatically controlled 
pick-and-place robot in manufacturing. IEEE 4
th
 International Conference on 
Mechanical, Control and Computer Engineering 170-174 (2021)
[5] Rahman, M.H., Haque, S., Monir, M.F.A., Uddin, M.F., Alam, S.B. Fabrication of 
a revolutionary pick-and-place robot with omnidirectional mobility. International 
Conference on Image Processing and Robotics 1-6, (2024) DOI:10.1109/
ICIPRoB62548.2024.10544036
[6] Borrell, J., Perez-Vidal, C., Segura, J.V. Optimization of the pick-and-place 
sequence of a bimanual collaborative robot in an industrial production line. Int J 
Adv Manuf Tech 130, 4221-4234 (2024) DOI:10.1007/s00170-023-12922-9
[7] Zhou, J., Huang, J., Ma, X., Lee, A., Kosuge, K., Liu, Y.-H. Design, modeling, 
and control of soft syringes enabling two pumping modes for pneumatic 
robot applications. IEEE-ASME T Mech 29 889-901 (2024) DOI:10.1109/
TMECH.2023.3345458
[8] Maheshwari, S., Jain, V.K. (2019). Design and analysis of pneumatic pick and 
place robot. Int J EngAdv Tech 9, 197-201
[9] Yu, J., Ren, Y. Reliability analysis and improvement of pneumatic pick-and-place 
robot based on fault tree analysis. J Mech Eng Resea Develop 43, 30-36 (2020)
[10] Patel, A., Desai, S. Study of reliability analysis of pneumatically operated pick and 
place robot. Int J Eng Inn Techn 8 50-55 (2018)
[11] Amuthak Kannan, R., Kannan, S.M. Satheesh Pandian, D. (2005). Reliability 
analysis of software-based automation system. 2
nd
 International Conference on 
Mechatronics 805-811.
[12] Kotaiah, B. Prasad, M.V.S., Khan, R.A. An analysis of software reliability 
assessment with neuro fuzzy based expert systems. Procedia Comput Sci 62, 92-
98 (2015) DOI:10.1016/j.procs.2015.08.418
[13] Dai, R., Guo, C. Researches and investigation on manufacturers reliability test 
data of electronics parts. Enrgy Proced 127 242-246 (2017) DOI:10.1016/j.
egypro.2017.08.125
[14] Habchi, G., Barthod, C. An overall methodology for reliability prediction of 
mechatronic systems design with industrial application. Reliab Eng Syst Safe 
155, 236-254 (2016) DOI:10.1016/j.ress.2016.06.013
[15] Kaczor, G., Mlynarski, S., Szkoda, M. Verification of safety integrity level with 
the application of Monte Carlo simulation and reliability block diagrams. J Loss 
Prevent Proc 41, 31-39 (2016) DOI:10.1016/j.jlp.2016.03.002
[16] Iqbal, J., Ullah, M.I., Khan, A.A., Muhammad, I. Towards sophisticated control of 
robotic manipulators: An experimental study on a pseudo-industrial arm. Stroj 
vest-J Mech Eng 61 465-470 (2018) DOI:10.5545/sv-jme.2015.2511
[17] Mahkovic, R. Position sensor for a mobile robot. Stroj vest-J Mech Eng 53 285-
296 (2007)
[18] Yan, H., Li, J., Kou, Z., Liu, Y., Li, P., Wang, L. Research on the traction and 
obstacle-surmounting performance of an adaptive pipeline-plugging robot. Stroj 
vest-J Mech Eng 68 14-26 (2022) DOI:10.5545/sv-jme.2021.7361
[19] Omrčen, D., Nemec, B. Measuring knee movement using an industrial robot - 
gravity compensation for the automatic gripper. Stroj vest-J Mech Eng 48 87-97 
(2002)
[20] Finžgar, M., Podržaj, P. Machine-vision-based human-oriented mobile robots: A 
review. Stroj vest-J Mech Eng 63 331-348 (2017) DOI:10.5545/sv-jme.2017.4324
[21] Škorc, G., Čas, J., Brezovnik, S., Šafarič, R. Position control with parameter 
adaptation for a nano-robotic cell. Stroj vest-J Mech Eng 57 313-322 (2011) 
DOI:10.5545/sv-jme.2009.017
[22] Bansal, S., Cheung, S.H. Analysis with multiple performance functions. J Reliab 
Eng Syst Safe 58 137-152 (2017)
[23] Yang, I.T., Hsieh, Y.-H., Kuo, C.-G. Integrated multiobjective framework for 
reliability-based design optimization with discrete design variables. Automat 
Constr 63 162-172 (2016) DOI:10.1016/j.autcon.2015.12.010
[24] Vartanov, M., Martynovich, N. Reliability for the robotic assembly of cylindrical 
parts. Procedia Engineer 150, 376-383 (2016) DOI:10.1016/j.proeng.2016.06.727
[25] Pandian, S.D., Asha, A. Predicting and enhancing the reliability of computer 
operated robot by considering software, hardware and interfacing modules. Int J 
Chem Tech Res 10 163-173 (2017)
[26] Pandian, S.D., Asha, A. Design for reliability of PLC based pneumatic operated 
pick and place robot by considering the mechanical properties and factor of 
safety - A case study. Int J Appl Eng Res 10 22-26 (2015)
Received 2024-03-26, revised 2024-07-03, accepted 2024-08-28 
Original Scientific Paper.
Data availability The data used in this study are not available 
for sharing or distribution, as they are subject to confidentiality 
agreements and restrictions.
Author contributions Satheesh Pandian Durairaj: Conceptualization, 
Methodology, Data curation, Writing – original draft, Software, 
Formal analysis, Visualization.
Kvantitativno modeliranje sekvenc za ocenitev zanesljivosti 
računalniško vodenega pnevmatskega prijemalno-polagalnega 
robota
Povzetek Pnevmatski prijemalno-polagalni (pick-and-place) roboti so 
ključni v sodobni proizvodni industriji, saj omogočajo natančno in hitro 
delo. Ker avtomatizacija še naprej spreminja različne sektorje, postaja 
vloga teh robotov vse pomembnejša. Doseganje vrhunske učinkovitosti pri 
delovanju robotov zahteva nenehno osredotočanje na varnost in zanesljivost. 
Zato je treba želeno zanesljivost skrbno preučiti že v začetnem delu faze 
načrtovanja. Izziv pa predstavlja identifikacija zanesljivih podsekvenc in 
kritičnih komponent v operativnem okvirju robotov za zagotavljanje celotne 
zanesljivosti in robustnosti sistema. Za naslovitev omenjenega izziva in 
potencialnih dvomov v zvezi z zanesljivostjo je potrebna podrobna analiza 
načinov odpovedi in ukrepov za zagotavljanje redundance. V raziskavi je bila 
zato opravljena identifikacija kritičnih komponent in delovnih podsekvenc, 
ki vplivajo na zanesljivost robotov. Razviti so bili ukrepi za redundanco, ki 
izboljšujejo robustnost sistema. Celovita analiza odpovedi je bila opravljena 
s programskim paketom LabVIEW. S simulacijo in analizo sekvenc robota je 
bila ocenjena zanesljivost različnih delovnih podsekvenc in komponent za 
podrobno oceno celotne zanesljivosti sistema. 
Ključne besede pnevmatski prijemalno-polagalni (pick-and-place) roboti, 
natančnost, hitrost, avtomatizacija, varnost, zanesljivost, analiza odpovedi