BTP Stage 2 Final

8/13/2019 BTP Stage 2 Final

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Indian Institute of Technology Indore

2012-13

B-Tech Projecton

Prognosis And Maintenance Planning

For Mechanical Components

Submitted by: Guided by:Janam Shah Dr. Bhupesh K. Lad

0900305

Astha Jain

0900313


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Objective

Predicting remaining useful life of a component by using ANN.

Developing optimal maintenance strategies in the framework

of RCM

Developing optimal maintenance strategies for multi-component system based on RUL

Developing optimal maintenance strategies for multi-

component system based on age of the components


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Predicting Remaining Useful Life

Neural Network Training And Validation

Conversion Of Data Into Signature

Identification Of Failure Signature

Identification Of Failure Parameter

Data Collection


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Data Collection:


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Failure Parameter:

Vibration Signals

Failure Signature:

RMS and Kurtosis

)2....23

22

21

(n1 YNYYY

Yrms

4

1

4

)1(

)(

sN

yyY

N

i i

kurtosis


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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 200 400 600 800 1000 1200

RMSofBear

ingA

Data points

RMS Vs Time(Data Points)

Conversion Into Data Signature:


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0

2

4

6

8

10

12

14

16

18

0 200 400 600 800 1000 1200

KurtosisOfBearingA

Data points

Kurtosis Vs Time(Data Points)


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Neural Network Training & Validation:

Network Data Manager


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Training Window


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Result:

So, from the above graph, we can see that our training error is comingin the range of 10-11, whereas our validation error is coming to be

equal to 10-4.


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Reliability Centered Maintenance

RCM is defined as an approach to maintenance that combinescorrective, preventive, predictive, and design out

maintenance practices and strategies so that the equipment

functions in the required manner.

RCM incurs minimum maintenance cost.

It is a philosophy that decides on which component which

technique is to be applied.


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RUL Based Group Maintenance

Problem Statement:

To minimize the cost of group maintenance of a machine having 5components on basis of RUL with constant MTTR.

Assumptions:

1. Remaining Useful Life (RUL) of components follow a normal

distribution.2. Components are in series, even if one fails the machine will bedown, hence downtime cost is taken constant.

3. Assembling-dissembling time of the machine is constant, if it isopened once all components can be replaced/repaired as

components are assumed to be structurally independent.4. The components which are not included in preventive groupmaintenance are correctively maintained.


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Given:

1.Mean () and standard deviations () for all the components

2.Cost of the components (C1-C5) (Rs)3.Mean Time To Replace (MTTR1) of the components (hrs)

4.Mean Time To Assemble-Dissemble for machine (MTTR2)

(hrs)

5.Mean Time To Assemble-Dissemble for individualcomponents for corrective maintenance (MTTR3) (hrs)

6.Labour Rate (CL) (Rs/hr)

7.Downtime cost (CDC)(Rs/hr)


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Cost calculation:

1. Preventive maintenance

2. Corrective Maintenance

3. Total Cost

CT= CPM+ CCM

)()( 2#

1

#

DCLPM CCMTTRMTTRCC

)()( 31 DCLiCM CCMTTRMTTRCC i


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Model Window:


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Result

On simulating, we get minimum cost by preventively maintaining

components 1, 3 and 5.

Probab

ilityofoccurrence

Total Cost


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RUL based group maintenance with

varying parameters

It is similar to the previous model only the Mean Time To

Repairs (MTTR1, MTTR2and MTTR3) are varying, i.e. they are

taken with log-normal distribution.

d l d


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Model Window


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Result:

Minimum cost= 1,03,351

Mean cost= 1,46,682

Optimum solution occurs by doing preventive maintenance of 1, 3 and 5

M

in.Cost

No. of trials


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Age based group maintenance

Objective:To find optimum grouping of components (having initial age)for preventive maintenance on the basis of:

minimum cost

maximum availability


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Given:

Cost of components (C1-C5) (Rs 5000)

Current age of components (V1-V5)

Shape parameter of all the components (1- 5)

Scale parameter of all the components (1- 5hrs)

Corrective task duration (normal distribution- and )

Preventive task duration = 8 hrs

Labour Rate (CL) (Rs/hr)

Downtime cost (CDC)(Rs/hr)

Simulation time = 1yr = 8760 hrs

Component (hrs) (hrs) (hrs)

1 2000 2 8 2

2 3000 3 12 2

3 2500 3 16 4

4 3500 2 14 3

5 1800 3 20 6


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Assumptions:

Components are in series, even if one fails the machine will bedown, hence downtime cost is taken constant

Components follow weibull failure distribution

Scheduled time is varying for preventive maintenance, which

is from 1,2,3.....,11 months The components which are not included in preventive group

maintenance are correctively maintained


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Model:

The Cost values, weibull parameters and maintenance parameters

are fed in the block properties of all the components in the BlockSim

software. The model window is:


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Block properties


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Scheduled task properties are given for one of the block and for adding

other blocks to the group, a maintenance group is created and the other

blocks are assigned the same group.


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For adding components to the group:


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Simulation Window:


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General summary result window:


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After the simulations, cost of maintenances (both correctiveand preventive) and the total cost is calculated in MS-Excel.

The cost formulas used are: Cost of preventive group maintenance:

Cost of Corrective maintenance of a component:

Total Cost of group maintenance:

)(DCLPMiPMPM

CCMTTRCnC

)]()[(DCLCMCMCM

CCMTTRCnC

CMPMT CCC


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Excel Cost File:


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Now, taking maximum availability and minimum cost of every

possible grouping in account, here 0 means we are doing corrective

maintenance for that component and 1 represents we are

performing scheduled maintenance for that component. The finaltable will be:


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Combination Maximum Availability Minimum Cost

0 0 0 0 0 0.971157 253285.6

1 0 0 0 0 0.970634 259143

0 1 0 0 0 0.970947 256845

0 0 1 0 0 0.971174 255618.5

0 0 0 1 0 0.970884 257779.9

0 0 0 0 1 0.971465 254694.9

1 1 0 0 0 0.97128 253124.3

1 0 1 0 0 0.971921 252201

1 0 0 1 0 0.971191 254273.2

1 0 0 0 1 0.973196 249203.3

0 1 1 0 0 0.973067 248755.9

0 1 0 1 0 0.971664 252057.7

0 1 0 0 1 0.974385 241248.9

0 0 1 1 0 0.972471 250401.7

0 0 1 0 1 0.975743 232523.60 0 0 1 1 0.973782 247590

1 1 1 0 0 0.974647 231140.2

1 1 0 1 0 0.971794 245508.5

1 1 0 0 1 0.976008 223069

1 0 1 1 0 0.974141 237251.3

1 0 1 0 1 0.978142 213981.4

1 0 0 1 1 0.975541 229783.8

0 1 1 1 0 0.975298 229607.7

0 1 1 0 1 0.978911 205676.1

0 1 0 1 1 0.97663 221441

0 0 1 1 1 0.9784 212602

1 1 1 1 0 0.97694 210976.2

1 1 1 0 1 0.981598 186883.6

1 1 0 1 1 0.979017 203149.5

1 0 1 1 1 0.9811 194006

0 1 1 1 1 0.981951 185903.8

1 1 1 1 1 0.984601 162981.9


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Results and discussion:

After simulating, it was observed that number of preventivemaintenance decreases with increasing schedule time whereas the

number of corrective maintenance increases with it. Similar trend is

observed in the downtime costs of preventive and corrective

maintenances respectively.

Minimum Cost is achieved when all the components are collectively

prevented also the system has maximum availability at that time.

The scheduled maintenance task should be performed every month

to get the optimum result.

Min cost incurred= Rs. 1,62,981.9

Max. System availability= 98.46%


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Conclusion & Future Scope

ANN is an accurate tool to predict remaining useful life withan error of 10-4.

RCM approach is useful for optimizing the cost during thegroup maintenance of multi-component system.

For future work, we can take some more failure parameterslike noise to predict RUL accurately.

RUL based and age based maintenance models can becombined to make it more realistic, which will help inwarranty estimation.

We can also take imperfect maintenance or crew effect oravailability of repaired equipment.


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References

Refngah F. N. Ahmad, Abdullah S, Jalar A, Chua L.B, Life Assessment of a

Parabolic Spring Under Cyclic Strain Loading, European Journal ofScientific Research, Vol.28 No.3 , pp.351-363, 2009.

KainulainenPerttu, Analysis of Parabolic Leaf Spring Failure, Bachelordissertation Savonia University of Applied Sciences, 2011.

Gebraeel Nagi, Lawley Mark, Liu R, Parmeshwaran Vijay, Residual lifepredictions from vibration based degradation signals: A Neural Network

Approach, IEEE Transaction of Industrial Electronics, Vol. 51, No. 3 2004. Mahamad A K, Saon S, Hiyama T, Predicting remaining useful life of

rotating machinery based artificial neural network, Computers andMathematics with Applications, Vol.60, pp 1078-1087, 2010.

https://www.ti.arc.nasa.gov

Rommert Dekker, Wildeman Ralphe, A Review of Multi-Component

Maintenance Models with Economic Dependence, MathematicalMethods of Operations Research45:411-435, 1997.
http://www.ti.arc.nasa.gov/http://www.ti.arc.nasa.gov/


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Moghaddam Kamran S & Usher John S., A new multi-objectiveoptimization model for preventive maintenance and replacementscheduling of multi-component systems, Department of IndustrialEngineering, University of Louisville, 2010.

Zhigang Tian, Youmin Zhang, and Jialin Cheng, Condition BasedMaintenance Optimization for Multi-component Systems,ConcordiaUniversity, Montreal, Quebec, H3G2W1, Canada

https://www.palisade.com

https://www.reliasoft.com

Yuo-Tern Tsai, Kuo-Shong Wang, Lin-Chang Tsai (2004) A study ofavailability-centered preventive maintenance for multi-componentsystems,Reliability Engineering and System Safety, 84, 261270.

Zhigang Tian, Tongdan Jin, Bairong Wu, Fangfang Ding (2011), Conditionbased maintenance optimization for wind power generation systemsunder continuous monitoring,Renewable Energy, 36, 5, 1502-1509.

B. Castanier, A. Grall, and C. Berenguer, (2005), A condition-basedmaintenance policy with non-periodic inspections for a two-unit seriessystem, Reliability Engineering & System Safety, 87, (1), 109-12
http://www.palisade.com/http://www.reliasoft.com/http://www.reliasoft.com/http://www.palisade.com/


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Thank You

BTP Stage 2 Final

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