BTP Stage1 Final

8/13/2019 BTP Stage1 Final

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B tech Project Presentation

on

Remaining Useful Life Prediction

Presented

by

Janam Shah(0900305)

Astha Jain(0900313)

Guided

by

Dr. Bhupesh Kumar Lad

Manish Rawat (PhD Scholar)

Mechanical Engineering Department

School of Engineering

INDIAN INSTITUTE OF TECHNOLOGY INDORE


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Objective

Prediction of Remaining useful life of

mechanical component (Bearing) to achieve

goals of maintenance using two

models/methods.

ANN Model (Stage I)

Markov Model (Stage II)

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Introduction to Maintenance

All actions necessary for retaining an item, or restoringto it to a serviceable condition, include servicing, repair,modification, overhaul, inspection and conditionverification.

The main objective is to become consistent with the goalsof production.

Cost

Quality

Delivery

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Types Of Maintenance

Corrective or Breakdown maintenance

Preventive maintenance

Predictive (Condition-based) maintenance

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Predictive Maintenance

In predictive maintenance, machinery conditions areperiodically monitored and this enables the maintenance

crews to take timely actions such as, machine adjustment,

repair or overhaul.

One can make use of predictive maintenance by using a

technique called signature analysis, which is intended to

continually monitor the health of equipment by recording

systematically signals derived in the form of vibration, noise,

pressure, relative displacement, etc.

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Introduction To Prognostics

Prognostics is an engineering discipline focused on predicting

the time at which a system or a component will no longerperform its intended function.

Prognostics is, or should be, performed at the component or

sub-component level.

Prognostics involves predicting the time progression of a

specific failure mode from its incipience to the time of

component failure.

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Mahamad, et al, 2010, proposed ANN model which

uses the time and weibull hazard rate of rms and

kurtosis for predicting the RUL of bearing.

Normalized life percentage is selected as the output. We tried to replicate the results of this journal.


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Failure Parameter - Vibration

Vibration is widely used to predict bearing failure

because a distinctive frequency known as the

defective frequency is excited.

Thus, vibration analysis becomes the most suitablecondition monitoring technique for investigating the

evolution of these defective frequencies over time.

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Failure Signature

RMS Value

Kurtosis factor

where is the mean, s is the standard deviation, and Nis thenumber of data points.

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Test set-up

Four bearings were installed on one shaft. On each bearing one accelerometer was installed for a total of 4

accelerometers (one for horizontal X on each). The Figure belowshows the test rig and illustrates sensor placement.


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Each data file consists of 4 columns. Each column

corresponds to one accelerometer signal.

The column headers for the 4 columns represent the

data coming from the 4 accelerometers. Vibration data was collected every 10 minutes till the

bearing failure occurs.

It is observed that the bearing 1 fails.

Data is collected from the website of NASA www.ti.arc.nasa.gov

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Data Collection
http://www.ti.arc.nasa.gov/http://www.ti.arc.nasa.gov/


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Kurtosis curve of bearing 1

0

2

4

6

8

10

12

14

16

18

0 200 400 600 800 1000 1200

Series1


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Weibull Hazard rates of RMS and Kurtosis Using Weibull++ software, we calculate shape and scale

parameter of rms and kurtosis both which are taken as inputs

to neural network.

The values of scale parameter() and the shape parameter()

is found out, which comes in close proximity to the actual

values got in the journal.

Weibull hazard Function is (/)(t/)^-1

Bearings RMS Kurtosis

Bearing 1 =0.119929

=1.948683

=4.176532

=3.008815

Bearing 2 =0.105442

=4.965653

=3.227931

=9.762985

Bearing 3 =0.111056

=5.878543

=4.577640

=7.872255

Bearing 4 =0.065338

=3.892966

=3.209184

=12.901483


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Inputs used in model

6 inputs are used in the model:

Time t-1

Time t

Weibull hazard function of rms Z1(t-1)

Z2(t)

Weibull hazard function of kurtosis Z2(t-1)

Z2(t)


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Future work-plan

The predicted RUL will be used for condition based

maintenance planning.

Opportunistic maintenance of a group of items can

be done with the help of this model. Prediction of RUL by using Markov model.

Comparison of the RUL predicted from ANN and

Markov model


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Gantt Chart


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Thank you!!!

BTP Stage1 Final

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