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RELIABILITY ASSESSMENT IN
SUGARCANE INDUSTRY USING
MONTE CARLO SIMULATION AND
THE METHODOLOGIES OF
RELIABILITY CENTERED
MAINTENANCE AND TOTAL
PRODUCTIVE MAINTENANCE
Celso Aurelio de Morais Lima (PUC )
engenhariacelso@gmail.com
Emerson de Souza Campos (PUC )
inec4@terra.com.br
Maria Jose Pereira Dantas (PUC )
mjpdantas@gmail.com
Ricardo Luiz Machado (PUC )
drrmachado@gmail.com
The purpose of this article is to develop a reliability evaluation about
the data gaps in a sugarcane production line. The report has a
quantitative approach with hybrid search method: combination of case
study and simulation. The methodoloogy of the study was supported in
Reliability Centered Maintenance (RCM) and Total Productive
Maintenance (TPM), resulting in a proposal for improvement of
maintenance strategies. Through ExpertFit ® software, the Time to
Next Failure (TTNF) of the system was modeled. Through Microsoft
Excel ®, a Monte Carlo simulation (MC) was held. The simulated
reliability was 56.27% for an hour of production.
Palavras-chave: Reliability, Monte Carlo simulation, Centered
Reliability Maintenance, Total Productive Maintenance.
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
João Pessoa/PB, Brasil, de 03 a 06 de outubro de 2016.
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
João_Pessoa/PB, Brasil, de 03 a 06 de outubro de 2016. .
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1. Introduction
The 2nd World War influenced on many global changes envisioned in recent decades. On the
issue of technology, American researches on the arms industry were fundamental, along with
the development of industrial automation, both leveraged by the development of information
technology and telecommunication. In the social sphere, there is the dependence of the
contemporary society on the automatic modes of production on a large scale, with high
quality and low cost. The maintenance engineering has been vital in increasing search for
projects and economical and reliable operations for production systems. (ARAÚJO, 2011).
Pinjala et al. (2006) state that currently the industrial maintenance has become key in the
scenario of industrial businesses, since the maintenance strategy can positively or negatively
affect competitive aspects of manufacturing, such as cost and quality. According to Ramos
Filho, Atamanczuk & Marcal (2010), the importance of manufacturing is increasing in
business, requesting coordination and good strategy maintenance department, mainly due to
the growing demand for availability of machinery and equipment.
According to Fogliatto and Ribeiro (2009), as a tool to aid in the increasing of the reliability
of critical items, it is highlighted the area of knowledge approached by the Reliability
Centered Maintenance (RCM). Its applications have been recognized as effective ways to
increase the availability of equipment, minimizing costs related to defects, repairs and
replacements
Concurrently with the principles of RCM, the concepts Total Productive Maintenance (TPM)
are directed to raise the quality of industrial operations through a high efficiency of
maintenance management. According to Takahashi et al. (1993), TPM is one of the most
effective methods in the evolutionary process of transforming an industry in an operation with
oriented management to equipment.
As mentioned by Sellitto (2005), the reliability functions are dealing with random variables.
Gnedenko (1965) cited Sellitto (2005), explains that random variables do not contain fixed
values, i.e, they vary with occasional factors. The knowledge of a random variable is not
given to the determination of its numerical value, but for, the odds of this variable take each
value. Thus, the need for the application of stochastic methods to study reliability arises. The
Monte Carlo simulation (MC) is one of these methods. According to Martins, Wener & Pinto
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
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(2010), the MC method used in the calculation of probabilities, there is good precision and
low complexity of computational modeling. For each random variable several random values
are generated, which represent the system dynamics.
The objective of this study was to develop a reliability study of a sugarcane production line,
responsible for reception, preparation and extraction of sugarcane. They collected data from
the system failure which resulted in downtime production. With that, the TPM and RCM
concepts were used in the improvement of maintenance strategies, with the preparation of a
maintenance plan. In addition, data for Time to Next Failure (TTNF) were modeled using the
Expertfit ® software, obtaining good results for the Weibull distribution probability. Finally,
the MC simulation model was developed in Microsoft Excel ® envisioning the reliability and
performance of the simulated system.
2. Literature review
2.1. Reliability centered maintenance
From the 60s, it was developed a detailed study for the definition of standards and procedures
for the maintenance of the aviation industry, based on extensive statistical analysis, known as
MSG-3 9 (Maintenance Steering Group), was the essence for what Nowlan & Heap (1978)
named Reliability Centered Maintenance (RCM). (RAPOSO, 2004).
According to Rausand (1998), after the application of the RCM in other industrial sectors,
experiments have shown significant reductions in preventive maintenance costs while
maintaining or improving the availability of their systems.
It is noticed in recent literature the tendency to proposals for improving models for the
traditional RCM methodology. Pexa et al. (2014), proposed a unified application of three
widely used tools: RCM, Safety Instrumented Function Process (SIFpro) and Risk Based
Inspection (RBI). Cheng et al. (2008) suggested a clever method for analysis of traditional
RCM. Selvik & Aven (2010) developed studies integrating uncertainty analysis in assessing
the RCM decision diagram, in which the simple answer procedure based on the traditional
Yes and No is insufficient.
Simply, Selvik & Aven (2010) argue that the RCM is a basic process that consists of two
stages: (i) intuitive analysis of potential failures, which are generally used in a critical Failure
Modes Effect Analysis (FMEA); (ii) application of the logic diagram decision to specify the
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
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type of maintenance. In this study both steps (i and ii) were applied. Figure 1 shows the logic
diagram of decision.
Figure 1 - Reliability centered maintenance: logical diagram of decision
Source: Adapted from Lafraia (2014)
2.2. Failure mode and effect analysis - FMEA
FMEA is a reliability technique that basically has three objectives: (i) identify and analyze
potential failures inherent to a product / process; (ii) recognize actions that mitigate the risks
of such failures occur; (iii) prepare a document to be reference in the next review and
improvements. The FMEA techniques expose weaknesses in the system, offering subsidies
for continuous improvement of activities. Thus, it helps in detecting and eliminating potential
failures. (FOGLIATTO & RIBEIRO, 2009).
In this study, a basic implementation of FMEA was developed as proposed by Lafraia (2014)
in order to conduct the reliability analyzes.
2.3. Total productive maintenance - TPM
Currently the manufacturing facilities of various areas coexist with several types of waste.
Because these waste targets, oriented goals to zero case record, zero tolerance for defects,
breakdowns, accidents and waste - are becoming a prerequisite in factories. To overcome
challenges, the concept of Total Productive Maintenance (TPM) has been adapted in
industries worldwide. (SINGH et al., 2012).
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
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TPM is a synergy among all business functions, particularly between production and
maintenance, focusing facing the continuous development of product quality, as well as to
ensure the capacity and operating efficiency. (CHAN et al., 2003).
2.4. Overall equipment efficiency - OEE
The Overall Equipment Effectiveness (OEE) is a key indicator for TPM. The OEE also covers
aspects of manufacturing. Not only evaluates the reliability and performance, but also the
efficiency related to losses due to rework and income loss. (ANVARI, EDWARDS &
STARR, 2010).
The OEE calculation is defined as the product of three variables: (i) Availability; (ii) speed
and (iii) rate quality. Availability checks the percentage of actual time spent in operation,
ranging between 0 and 1. Speed Rating analyzes the percentage of the relative speed (assumed
during deviation) over the nominal speed, assuming values between 0 and 1. Quality rate
checks the percentage output line of products, ranging between 0 and 1. The calculation is
shown in Equation 1. (FOGLIATTO & RIBEIRO, 2009).
2.5. Reliability and maintainability
According to Morad, Mohammad & Sattarvand (2014), reliability is the appropriate indicator
for quantitative evaluation in a survival analysis of any system. According to Meyer (1983),
the likelihood reliability is the probability of a product / component to develop its role within
the design conditions over a period of time. According to Diedrich & Sellitto (2014), the
reliability function R (t) is the probability that a device works without failures, ranging
between 0 and 1. Three of the variables used most in the reliability study are: MTBF (Mean
Time Between Failure), R (t) (Reliability) and h(t) (Risk Function). The MTBF calculation is
shown in Equation 2.
Maintainability is defined as the ability of a device to be repaired to restore its normal
operating functions after a failure. MTTR (Mean Time to Repair) is a good indicator of
maintainability. MTTR calculation is shown in Equation 3. Through the MTBF and MTTR, it
is calculated the Av (t) Availability as in Equation 4. (FOGLIATTO & RIBEIRO, 2009).
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
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(2) (3) (4)
Where: T – Total time; N – Number of failures; TTNR – Time to Next Repair.
2.6. Weibull probability distribution
According to Meyer (1983) the Weibull distribution was originally proposed by W. Weibull
in 1954, when he conducted studies aimed to the failure time due to fatigue in metals.
According to Fogliatto and Ribeiro (2009), this distribution is appropriate for modeling stable
failure rates, increasing and decreasing ones, in the case of a significant distribution for
reliability modeling. Lafraia (2014) gives formulas for the Weibull distribution, the
distribution for fault f (t) function or risk failure rate λ (t) and reliability R (t):
(5) (6) (7)
Where: β - Parameter form; η - Parameter range; t - Variable time.
In this article, although other probability distributions were also adjusted, it was set to work
with the Weibull due to its flexibility, since it is possible to model all permissible states for
the failure rate λ(t).The simple analysis of the shape parameter allows you to see if the failure
rate (hazard function) is increasing, decreasing or is constant. Under this assumption, the
Weibull modeling becomes increasingly important for management and maintenance
engineering. Figure 2 shows the bathtub curve representing the possible states an equipment
life. It is observed that the value of the shape parameter β easily allows to assess the state in
which its equipment is.
Figure 2 – Bathtub Curve
Source: Adapted by Sellito (2005)
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
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2.7. Monte Carlo simulation
The MC simulation is a statistical method used in stochastic simulations with several
applications. It includes modeling system in question and the production of random numbers
from the probability distributions, which represent the dynamics of the elements system and
the use of results to approximate the results. (MENDES, 2011).
The MC method for probability calculation is supported on random simulations. It shows easy
understanding, good accuracy level and low complexity to computational implementation,
and a lot demanded by engineers. The MC process of modeling and simulation involves the
following: (i) for each output variable, distribution; (ii) a susceptibility list of variable keys
organized according to their correlation with the output variable; (iii) graphics and statistical
reports characterizing the simulated responses. According to the type of problem, the
independent variables may use different probability distributions: Normal, Log-Normal,
Exponential, Triangular, Uniform and Weibull. (MARTINS, WENER & PINTO, 2010).
3. Case study: sugarcane production line
The reliability study was carried out in a sugar, ethanol and electricity industry, located in the
southwest of Goiás, Brazil. For the research, it was selected the production line 01, called by
the company, as reception, preparation and extraction - 01. This line accounted for
approximately 50% of the processing of the raw material (sugarcane), working continuously,
24 hours a day for about eight months (crop).
As it can be seen in Figure 3 flow chart, this case study will be considered, for simplification,
the production line will be divided into four subsystems: Reception and preparation, diffusion
extraction, milling 1 and 2 extractions. Any failure presented in one of the subsystems will be
equivalent to a production downtime.
Figure 3 - Production line flow chart 1
Source: the authors (2016)
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Downtime data were recorded by the company through daily reports, subsequently entered
into the maintenance system, containing the type, reason and duration. Data were collected
from the production line fault 01 from 06.30.2014 (early harvest) to 12/03/2014 (last crop
fortnight), totaling 3768 hours of production (24 h / day of production). The downtime data,
used for charts preparation, are presented in Figure 4.
Figure 4 – Downtime data of subsystems
Time to Next
Failure (h)15,00 108,47 3,23 172,60 258,03 133,33 992,35 711,67 27,40 343,75 753,52
Time to Repair (h) 2,08 1,30 0,32 2,00 0,33 0,60 0,03 1,18 0,22 0,78 0,70
26,23 3,17 150,70 1,92 0,02 2,00 0,00 4,12 1,00 31,12 0,02
303,37 16,13 90,03 0,28 1,53 0,28 0,57 0,37 0,58 0,51 0,55
2,02 127,50 0,62 51,58 0,02 73,15 80,85 50,62 766,28 200,75 752,57
20,50 210,42 30,95 56,63 34,78 202,67 135,75
0,02 0,12 6,00 4,07 4,00 1,00 0,33 0,85 1,60 1,58 4,00
0,68 0,03 0,17 0,33 0,02 0,02 0,02 0,02 0,02 0,02 0,02
0,02 0,97 11,30 19,03 0,33 0,37 0,17 0,30 4,48 1,40 7,33
3,75 0,12 12,83 6,20 3,22 9,07 0,48
26,32 11,93 38,07 0,43 0,77 98,17 16,58 0,12 14,77 4,23 131,35
15,08 120,73 69,70 16,23 15,47 120,53 281,15 0,45 27,82 75,48 1515,87
272,90
1,33 0,08 0,17 0,50 0,75 0,23 0,55 0,23 2,00 0,53 0,05
0,33 0,03 0,45 0,15 0,67 0,20 0,07 0,30 0,05 0,17 1,97
0,25
18,03 0,00 0,02 0,18 6,68 0,00 0,00 32,98 12,58 9,57 84,52
0,53 0,88 0,70 0,17 0,85 6,75 0,15 34,57 25,15 81,93 4,43
0,60 4,78 9,75 5,30 2,97 16,88 8,05 7,32 0,63 1,53 0,05
0,00 57,87 4,97 0,00 53,47 42,73 1,07 4,52 155,33 3,97 1,20
7,38 30,55 43,08 43,50 7,08 53,22 8,43 22,52 29,23 0,78 5,03
18,27 20,60 20,25 8,10 2,77 23,32 11,20 32,48 8,97 0,00 34,68
12,37 479,73 7,25 421,38 93,83 73,55 177,75 84,98 594,10 16,97 16,65
52,15 67,97 0,08 35,88 28,92 253,68 66,90 35,08
4,63 1,32 0,92 0,68 1,70 1,00 2,00 0,25 0,10 0,05 0,08
0,50 0,70 0,07 0,15 1,00 0,22 0,08 0,55 0,37 0,08 0,13
0,08 0,10 0,05 0,07 0,12 0,05 0,08 0,08 0,08 0,15 3,00
0,38 0,30 0,48 2,00 0,07 0,12 0,12 0,20 0,17 0,05 0,67
0,07 0,75 0,17 0,05 0,15 0,08 0,05 0,03 0,03 0,03 0,27
0,07 0,05 0,17 0,08 0,03 0,68 0,12 0,05 0,53 0,33 0,07
0,10 2,97 3,83 0,12 0,12 1,25 0,27 0,42 1,22 0,88 0,03
0,08 0,07 0,07 0,10 0,05 0,12 0,08 0,08
Milling 01 Extraction
Time to Next
Failure (h)
Time to Repair (h)
Milling 02 Extraction
Time to Next
Failure (h)
Time to Repair (h)
Reception and Preparation
Diffusion Extraction
Time to Next
Failure (h)
Time to Repair (h)
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
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Source: the authors (2016)
3.1. Downtime data analysis
The failure data show that July was the most critical month related to the number of failures
and downtime, Figure 5 and Figure 6. Notice that the trend of failure decreases until
September, stabilizing it, and in November increasing again. This feature represents the
behavior system during the harvest, where there is greater amount of failures in the industry
start-up and at the end of the harvest, there is a moderate increase in failures.
About the studied subsystems, Figure 7, there is a high number of failures in Milling 2,
Figure 8. It shows that the Milling 2 has the lowest MTBF, resulting in a low reliability. As
for the downtime, Figure 8, the diffuser is a major cause of the downtime production line,
representing 106.27 hours, or 4.43 days. The diffuser can be considered t the most striking
bottleneck because, despite having fewer failures than Milling 2, it has longer downtime.
Figure 5 - Number of failures x months Figure 6 – Downtime (hours) x months
Figure 7 - Number of failures x subsystem Figure 8 – Downtime (hours) x subsystem
Source: the authors (2016)
Through the failure data, MTBF, MTTR and availability values were calculated for each
subsystem using Equations 2, 3 and 4. The values are presented in Figure 9. As mentioned, it
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was observed that the MTBF of Milling 2 presents the lowest rate. MTTR Diffuser has the
largest value, so its availability is the smallest among the subsystems. Multiplying the
availability of each subsystem, one obtains the overall 95.39% system availability.
Figure 9 - MTBF, MTTR and availability of subsystems
Source: the authors (2016)
3.2. Overall equipment efficiency of subsystems
Following the proposal of this reliability study, in this section the OEE will be calculated. The
result is important to determine which subsystems efforts should be concentrated for
improvements and investments.
Firstly, the availability with MTBF and MTTR data were calculated, using Equation 3, and
they were presented in Figure 9. Then, through the number of failures for each subsystem,
extracted from the failure data, it was estimated speed ratio, given by the relation between the
time that the production system operated at reduced speed (due to a failure) and the time when
operated at normal speed. It is estimated for this production line for each downtime,
regardless of time, it takes on average 10 minutes to reestablish the nominal production speed,
i.e., is a downtime represents a 10 minute reduction in production speed. Thus, it was
calculated on the basis of the failure number, the total time that each subsystem worked at a
reduced speed. Considering the studied period studied - 3768 total hours (24 h / day of
production) – it was possible to find the speed rate through ratio reduced speed time and time
at normal speed. Figure 10 presents the calculation of the speed ratio.
Figure 10 - Speed rate calculation
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
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Source: the authors (2016)
The quality rate is calculated as deviation from the main rate quality of each subsystem index
relative to the expected rate (rate goal): preparation rate for the reception and preparation,
sucrose extraction ratio for the diffuser, bagasse moisture content, common to grinding mill
01 and 02. Figure 11 presents the calculations of quality rate. Finally, OEE is calculated using
Equation 1 presented in subsection 2.4, as presented in Figure 12.
Figure 11 - Quality rate calculation
Source: the authors (2016)
Figure 12 - OEE Calculation
Source: the authors (2016)
3.3. MCC: Failure mode and effect analysis (FMEA) and maintenance plan
In this section the goal is to develop an FMEA and the maintenance plan based on MCC
decision diagram. These tools have been applied only to the extraction through diffusion
subsystem, since it has the lowest OEE, Figure 12, and largest downtime, Figure 8. The
FMEA development was divided into two items. The first one, Figure 13, defines the failure
XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil
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modes shown in the diffusion extraction line. The second one, Figure 14, is intended to
identify the effects of failures, allowing the establishment of a maintenance plan, Figure 15,
based on MCC decision diagram, Figure 1.
Figure 13 – (Part 1) FMEA: definition of failure modes to diffusion extraction
Item Function Functional Failure Fail Mode
a. Unable to transport the
sugarcane bagasse.
1. Electric motor with problem.
2. Mat drive with problem.
3. Reducer mechanical damaged.
4. Automation with problem.
5. Conveyor chain with problem.
b. Inappropriate leveling
sugarcane bagasse.
1. Flaps misfits.
2. Shovels drag with problem.
c. No leftover bagasse for
the return belt.
1. Bagasse input insufficient.
1. Distribute all diffuser
input sugarcane bagasse,
controlling the level.
Input Mat
Source: the authors (2016)
Figure 13 – (Part 2) FMEA: definition of failure modes to diffusion extraction
Item Function Functional Failure Fail Mode
a. Unable to pull the gear
shaft.
1. Electric motor with problem.
2. Diffuser drive with problem
3. Reducer mechanical with problem.
4. Automation with problem.
5. Diffuser shaft with problem.
b. Unable to pull the
conveyor chains.
1. Traction gear with problem.
2. Conveyor chain with problem
Conveyor
chain3. Transporting the bagasse
mattress in unidirectional
sense during the diffusion
extraction cycle.
a. Unable to pull the
bagasse mattress.
1. Chain link with problem.
2. Conveyor chain with inappropriate
length.
3. Pins with problem
4. Shovels drag with problem.
Output
Mat
4. Transporting the bagasse
diffuser to the next step of
the process.
a. Unable to transport the
bagasse.
1. Electric motor with problem.
2. Mat drive with problem.
3. Reducing mechanical damaged.
4. Automation with problem.
5. Mat of belt with problem.
2. Pull the conveyor chains
diffuser.
Diffuser
drive
Source: the authors (2016)
Figure 14 – (Part 1) FMEA: definition of failure effects to diffusion extraction
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Fuctional
FailureFail Mode Basic Failure Cause Effects of Failure
Loss of insulation by high humidity.
Breaking the bearing.
Electronic component with problem.
Disarm devices.
Inefficient lubrication.
Damage by fatigue.
Problem supervisory / logic
Problems with devices and instruments.
Conveyor chain locked.
Damage by fatigue.
1. Flaps misfits. Flaps misfits by fatigue.
Shovels drag locked.
Damage by fatigue.
FF 1.c 1.Bagasse input insufficient. Low speed of the production line. Unevenness of
bagasse.
Bushing water at the
exit of diffuser.
Loss of insulation from excessive
moisture
Breaking the bearing.
Electronic component with problem.
Disarm devices.
Inefficient lubrication.
Damage by fatigue.
Problem supervisory / logic
Problems with devices and instruments.
Damage by fatigue.
Falha de projeto.
3. Reducer mechanical damaged.
4. Automation with problem.
5. Conveyor chain with problem.
FF 1.a
FF 2.a 1. Electric motor with problem.
2. Diffuser drive with problem.
3. Reducer mechanical with
problem.
4. Automation with problem.
Stop feeding the cane
on Diffuser.
Stop Diffuser.
Stop the production
line.
2. Shovels drag with problem.
FF 1.b Unevenness of
bagasse.
Bushing water at the
2. Mat drive with problem.
1. Electric motor with problem.
Stop Diffuser.
Stop the production
line.
5. Diffuser shaft with problem.
Source: the authors (2016)
Figure 14 – (Part 2) FMEA: definition of failure effects to diffusion extraction
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Fuctional
FailureFail Mode Basic Failure Cause Effects of Failure
1. Traction gear with problem. Damage by fatigue.
2. Conveyor chain with problem. Damage by fatigue.
Conveyor chain locked.
Damage by fatigue.
2. Conveyor chain with
inappropriate length.
Damage by fatigue and corrosion.
3. Pins with problem. Damage by fatigue.
Shovels drag locked.
Damage by fatigue.
Loss of insulation from excessive
moisture
Breaking the bearing.
Electronic component with problem.
Disarm devices.
Inefficient lubrication.
Damage by fatigue.
Problem supervisory / logic
Problems with devices and instruments.
5. Mat of belt with problem. Damage by fatigue.
FF 4.a
FF 2.b
FF 3.a
4. Shovels drag with problem.
1. Chain link with problem.Stop Diffuser.
Stop the production
line.
Stop Diffuser.
Stop the production
Stop Diffuser.
Stop the production
line.
1. Electric motor with problem.
2. Mat drive with problem.
3. Reducer mechanical damaged.
4. Automation with problem.
Source: the authors (2016)
Figure 15 – (Part 1) MCC: maintenance plan based on decisions diagram to diffusion extraction
Activity Description Freq
1. Electric motor with problem. PredictiveVibration analysis.
Isolation analysis.
Once a month
Once a year
2. Mat drive with problem. Predictive Thermographic analysis. Once a month
3. Reducer mechanical damaged. Predictive Vibration analysis. Once a month
4. Automation with problem. Preventive Formatting automation computers. Once a year
5. Conveyor chain with problem. Predictive Ultra sonic analysis. Once a quarter
1. Flaps misfits. Preventive Regulation of Flaps. Once a month
2. Shovels drag with problem. PreventiveVisual inspection and replacement of
damaged shovels.Once a quarter
FF 1.c 1. Bagasse input insufficient. InspectionInspect if there are remaining bagasse
and correct operation when necessary.Thrice a day
1. Electric motor with problem. PredictiveVibration analysis.
Isolation analysis.
Once a month
Once a year
2. Diffuser drive with problem. Predictive Thermographic analysis. Once a month
3. Reducer mechanical with
problem.Predictive Vibration analysis. Once a month
4. Automation with problem. Preventive Formatting automation computers. Once a year
5. Diffuser shaft with problem. Predictive Ultra sonic analysis. Once a year
FF 2.a
Maintenance Plan
FF 1.b
Failure Fail Mode Technique
FF 1.a
FF 1.a
Source: the authors (2016)
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Figure 15 – (Part 2) MCC: maintenance plan based on decisions diagram to Diffusion Extraction
Activity Description Freq
1. Traction gear with problem. Preventive Restoration of gears. Once a year
2. Conveyor chain with problem. Predictive Ultra sonic analysis. Once a quarter
1. Chain link with problem. Predictive Ultra sonic analysis. Once a year
2. Conveyor chain with
inappropriate length.Inspection
Inspect the chain size and adjust when
necessary.Once a quarter
3. Pins with problem. Predictive Ultra sonic analysis. Once a quarter
4. Shovels drag with problem. PreventiveVisual inspection and replacement of
damaged shovels.Once a quarter
1. Electric motor with problem. PredictiveVibration analysis.
Isolation analysis.
Once a month
Once a year
2. Mat drive with problem. Predictive Thermographic analysis. Once a month
3. Reducer mechanical damaged. Predictive Vibration analysis. Once a month
4. Automation with problem. Preventive Formatting automation computers. Once a year
5. Mat of belt with problem. InspectionInspect the belt and replace necessary
partsOnce a year
FF 2.b
Fail Mode TechniqueMaintenance Plan
FF 4.a
FF 3.a
Failure
Source: the authors (2016)
3.4. Modeling and Monte Carlo simulation
3.4.1. Time to next failure (TTNF)
The data of TTFN are presented in Figure 4. For data of TTFN, the ExpertFit ® certifies that
the Weibull distribution adequately represent the behavior of 04 subsystems. The data
obtained in the adherence tests are shown in Figure 16. The modeling presented in Figure 16
presents the form of β and η scale values for each subsystem. Notice that the order of values is
less than 1, as bibliographic review, it is concluded that the subsystems have failure rates λ (t)
decreasing.
Figure 16 - Weibull modeling Time to Next Failure
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Source: the authors (2016)
3.4.2. Reliability simulation
As observed in Figure 16, the data from the TTNF were well represented by the Weibull
distribution. Through provided parameters by ExpertFit ® (η and β) it was made MC
simulation generating random values for the TTNF of each subsystem. Thus, it estimated the
reliability of the subsystems and the overall system. The simulation was performed with the
help of Microsoft Excel ® software, generating random numbers by Random () function, with
values ranging between 0 and 1, 10,000 simulations had been performed. In Figure 17 and 18
the used formulas and obtained values from simulations are presented.
Figure 17 - Formulation in excel ® for MC simulation times to next failures
Source: the authors (2016)
Figure 18 - Simulation of time to next failure
Source: the authors (2016)
For the reliability estimation is necessary to check which simulated value is greater than the
set time. This test, in other words, checks failure before the time at which you want to
calculate the reliability defined in cell time for reliability calculation (h) that, in this case is
one hour. The system only works if there are not failures in the four subsystems. Figures 19
and 20 show the formulas and results where (1) means failure.
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Figure 17 - Formulation for checking time to next failure
Source: the authors (2016)
Figure 18 – Checking time to next failure
Source: the authors (2016)
Finally, it was counted the number of simulations that showed zero failure for the four
subsystems and it was calculated the ratio to the total number of simulations that returned
failures in a subsystem or more. The Excel formulation and the results are presented in
Figure 19. As it can be seen, the reliability of the production line running for an hour without
failure in any of its subsystems is 56.27%. Figure 20 shows the reliability chart for each
subsystem and for the production line in general.
Figure 19 - Reliability calculation formulation
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Source: the authors (2016).
Figure 20 – System and subsystems reliability curves
Source: the authors (2016).
4. Conclusions
The methodology used in this study showed interesting results. The use of TPM concepts,
with the OEE calculation, section 3.2, which clearly guided the subsystems studied required
more attention, the extraction through diffusion. Thus, efforts were directed at the
development of MCC methodology for this subsystem, section 3.3.
For determining behavior of line production, the MC modeling and simulation has been
developed, section 3.4, showing good results easily applied, making it possible to define the
simulated reliability that according to Figure 20, was 56.27% for one hour of production. The
Reliability presented too low value for a short time, showing great instability in the system.
During the modeling, it was also concluded section 3.4, that the entire system is in the infant
mortality phase, with decreasing failure rate, since all parameters form (β) of the subsystems
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presented values lower than 1. Generally, during this phase, the nature of breakdowns and
problems (downtime) is coming from design failure and / or installation, and not for the
equipment wearing out or aging. Despite the production line in question has been founded in
2008, classifying it as being in the infant mortality period, makes sense because, in general,
the industrial complex (Sugarcane industry) went through an expansion project in every
production level in the last three years, where every part of industrial automation has been
replaced and rebuilt.
Given these findings, it remains to say that the maintenance plan based on MCC, Figure 15,
should be put into practice only when the system gets mature, i.e, present constant failure rate,
with shape parameter (β) close to 1. While the failure rate is decreasing, smaller form
parameter 1, the company should focus its work on corrective maintenance of their problems,
always seeking to effectively eliminate project errors.
Based on data analysis, section 3.1, the production line presented availability longer than
95%. For continuous production line, this loss of 5% of maintenance downtime, may
implicate irreparable losses. It should be emphasized that, due to the expansion project,
mentioned earlier, it is justified by a 95% availability, because practicing it, after an
expansion of this magnitude, the production line goes through a long period of child maturity,
named "test crop" by sugarcane industry.
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