A NEURAL NETWORK APPROACH FOR MACHINE...

A NEURAL NETWORK APPROACH FOR MACHINE BREAKDOWN

REPAIR TIME

CHANTHURU A/L THEVENDRAM

UNIVERSITI TEKNOLOGI MALAYSIA

A NEURAL NETWORK APPROACH FOR MACHINE BREAKDOWN

REPAIR TIME

CHANTHURU A/L THEVENDRAM

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Masters of Engineering (Mechanical Engineering)

Faculty of Mechanical Engineering

Universiti Teknologi Malaysia

JANUARY 2013

iii

Specially dedicated to mom and dad

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ACKNOWLEDGEMENT

As with any other text, the number of individuals who have made it possible

for exceeds those whose names grace the cover. At the hazard of leaving someone

out, I would like to explicitly thank the following respective individuals for their

priceless contribution and commitment towards in accomplishing this project

proposal.

First and foremost, I would like to thank my supervisor, Assoc. Prof. Dr.

Wong Kuan Yew, for his patience and guidance throughout the whole year for the

completion my project.

I would also like to thank my family members for always being there to

support me all the time and give me the courage and strength that are necessary for

me to carry on with this project.

Last but not least, I would like to thank all the lecturers that have taught me

throughout my education in Universiti Teknologi Malaysia, and also to my fellow

friends in workplace and hometown.

Once again, a sincere thanks.

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ABSTRACT

Research on neural network applications have been carried out very extensively in

recent days. The current trends in manufacturing sectors for solving their business

operational problems have been very difficult and subjective. Many organizations

have used various methods to solve machine breakdown's repair time, either reducing

the time taken to repair or eliminate the particular occurrence. The traditional way

for solving these machine breakdown issues was to predict the machine breakdown

occurrence through preventive maintenance. Hence, in the present study, a neural

network method was proposed to optimize the mean repair time for machine

breakdown with regression models were evaluated from the trained neurons. The

neurons were represented by the samples of repair time of previous years' record of a

single machine. The results shows that the set of samples of repair time have

critically influenced the optimized mean repair time for the machine. Various

methodologies were used by comparing several grouped machine breakdown

phenomena which showed more accurate regressions. The use of neural network, in

the end of the study, gives significant changes in predicting machine breakdown

repair time for the future years.

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ABSTRAK

Penyelidikan ke atas aplikasi rangkaian neural telah dijalankan sangat meluas di hari

baru-baru ini. Trend semasa dalam sektor pembuatan untuk menyelesaikan masalah

perniagaan operasi mereka telah amat sukar dan subjektif. Banyak organisasi telah

menggunakan pelbagai kaedah untuk menyelesaikan masa pembaikan kerosakan

mesin, sama ada mengurangkan masa yang diambil untuk membaiki atau

menghapuskan kejadian tertentu. Cara tradisional untuk menyelesaikan isu-isu

kerosakan mesin adalah untuk meramal berlakunya kerosakan mesin melalui

penyelenggaraan pencegahan. Oleh itu, dalam kajian ini, kaedah rangkaian neural

telah dicadangkan untuk mengoptimumkan masa pembaikan min untuk kerosakan

mesin dengan model regresi telah dinilai dari neuron terlatih. Neuron diwakili oleh

sampel masa pembaikan rekod tahun-tahun sebelumnya untuk mesin tunggal.

Keputusan menunjukkan bahawa set sampel masa pembaikan telah amat

mempengaruhi masa pembaikan min yang dioptimumkan bagi mesin. Pelbagai

kaedah telah digunakan dengan membandingkan beberapa kerosakan dengan

dikumpulkan fenomena kerosakan mesin yang menunjukkan regresi-regresi yang

lebih tepat. Penggunaan rangkaian neural, di akhir kajian, memberikan perubahan

ketara dalam meramalkan masa membaiki kerosakan mesin untuk tahun-tahun akan

datang.

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TABLE OF CONTENTS

CHAPTER TOPIC PAGE

TITLE

SUPERVISOR’S DECLARATION

TITLE PAGE i

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xii

1 INTRODUCTION

1.1 Introduction

1.2 Background and Rationale

1.3 Problem Statement

1.4 Objectives of the Project

1.5 Research Questions

1.6 Scope of Study

1.7 Significance of Study

1

1

2

4

4

5

5

6

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1.8 Structure of Thesis – Chapters Overview 7

2 LITERATURE REVIEW

2.1 Concepts and Definitions

2.2 Machine

2.2.1 Machine Utilization Rate

2.2.2 Machine Breakdown

2.2.3 Machine Repair Time

2.2.4 Machine Maintenance

2.3 Scheduling and Production Scheduling

2.3.1 Effects in Production Scheduling

2.3.1.1 Advantages of Production Scheduling

2.3.1.2 Disadvantages of Production Scheduling

2.4 Neural Network System

2.4.1 Artificial Neural Network

2.4.2 Neural Network Training and Learning

2.4.3 Machine Breakdown Effect

2.5 Conclusion on Literature Review

8

8

8

9

10

11

11

12

14

14

15

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18

21

22

22

3 RESEARCH METHODOLOGY

3.1 Flow Chart (General)

3.2 Neural Network Training

3.3 Conclusion

24

24

25

27

4 DEVELOPMENT OF THE SYSTEM

4.1 Neural Network Tool

4.2 Selection of Data (Inputs and Targets)

4.2.1 Machine Breakdown Phenomena

28

28

31

33

5 DATA COLLECTION AND NEURON TRAINING

5.1 Machine Breakdown: Heater push cylinder block

jammed (Y1)

5.2 Machine Breakdown: Cutter’s tip cutting dented (Y2)

35

35

45

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5.3 Machine Breakdown: Cutting cylinder oil leaking (Y3)

against Y56 (Ideal)

5.4 Machine Breakdown: Limit switch faulty (Y32) against

Y71 (Ideal)

5.5 Machine Breakdown: Heater stuck at chute (Y90)

against Y100 (Ideal)

5.6 Neural Network Training for Group 1: Push up cylinder

problem

5.7 Neural Network Training for Group 2: Watt tester

malfunction

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49

51

52

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6 DISCUSSION AND RESULTS

6.1 Network Training using Levenburg-Marquardt

6.2 Validation on Grouped Neural Network Training

6.2.1 Results on Repair Time

6.2.2 Fitting the Function into Machine Schedule

6.3 Validation on Overall Performance of the Combined

Inputs’ Neural Network Training

6.3.1 Results on Repair Time

6.3.2 Fitting the Function into Machine Schedule

6.4 Conclusion

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64

65

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7 CONCLUSION AND FUTURE WORK

7.1 Conclusion

7.2 Future Work

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67

69

REFERENCES 71

APPENDICES 76

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LIST OF TABLES

TABLE NO TITLE PAGE

4.1 Ideal conditions of the phenomena 32

4.2 Selected group for additional validation activity (Push up cylinder)

33

4.3 Selected group for additional validation activity (Watt tester malfunction)

34

5.1 Training, validation and testing's mean squared error (MSE) and regression R values for H=5, 10, 15, 20 with Y1 input against target Y54

44

5.2 Training, validation and testing's mean squared error (MSE) and regression R values for H=5, 10, 15, 20 with Y2 input against target Y54

45

5.3 Mean square error and regression R value for 4 sequence network training (Input=Group 1 and target=Y100)

52

5.4 Mean square error and regression R value for 4 sequence network training (Input=Group 2 and target=Y100)

54

6.1 Selected phenomena which have MSE<1 57

6.2 List of phenomena for repair time data based on Table 6.1

57

6.3 Statistical information for Table 9 phenomena 57

6.4 Combined training results for 65 inputs 58

6.5 Mean repair time for Group 1 (Original and Optimized) 60

6.6 Mean repair time for Group 2 (Original and Optimized) 61

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6.7 Mean repair time for combined inputs (Original and Optimized)

64

A-1 List of machine breakdown phenomena (from 1-52) 77

A-2 List of machine breakdown phenomena (from 53-102) 78

A-3 Machine repair time (in minutes) from year 1992 – 2012 (For Y1 – Y51)

79

A-4 Machine repair time (in minutes) from year 1992 – 2012 (For Y52 – Y102)

80

B-1 Push up cylinder problem for Group 1 (Machine breakdown phenomena)

81

B-2 Watt tester malfunction for Group 2 (Machine breakdown phenomena)

82

C-1 Training results for combined inputs 83

C-2 Training results for combined inputs (Continued) 84

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LI ST OF FIGURES

FIGURE NO

TITLE PAGE

2.1 Neural Network Architecture 19

3.1 Basic Flowchart (General) 25

3.2 Neural Network training flow 26

4.1 Start screen of NN Fitting Tool GUI 29

4.2 Data Input screen 30

4.3 Import Wizard in Neural Network Fitting Tool 30

5.1 Trained network results for Y1 against Y54 (Hidden neurons: 5)

36

5.2 Training progress results for Y1 against Y54 (Hidden neurons: 5)

3.6

5.3 Regression Plot for Y1 against Y54 (Hidden neuron: 5) 37

5.4 Function Fit for Y1 against Y54 (Hidden neuron: 5) 37


38


38




40

5.10 Training progress results for Y1 against Y54 (Hidden 40

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neurons: 15)




42


42



5.17 Training regression plots for Y2 against Y54 for H=5, 10, 15, 20

46


5.19 Regression Plot for Y22 against Y56 (Hidden neuron: 15)

48


50


51

D-1 Results for training sequence #1 (Group 1 against Y100) 85




E-1 Results for training sequence #1 (Group 2 against Y100) 89




CHAPTER 1

INTRODUCTION

1.1 Introduction

Most manufacturing companies produce products from home appliances to

food industry for instance. Before producing actual product, the company practically

study or analyse the incoming sales first. The sales quantity for a particular product is

then sent to the planning department for scheduling process. Upon complete

scheduling, the manufacturing or assembly of product department will follow the

planned scheduled accordingly. In the scheduling process, there are several factors

that make scheduling tasks become more challenging and tougher for production

planners. Though there are many systems to consider these factors and to ease the

task of the planners, there are constraints that indirectly cannot be solved. The most

frequent scheduling factor is the multiple models that a manufacturing line produces

in a day for instance. When there is an existence of multiple model of a product, the

schedule is then planned for different models in a day, adjusted according to sales to

warehouse requirements.

2

Nowadays, with customer oriented businesses, manufacturing companies

especially the sales section tend to fulfil the customer’s needs in means of delivery of

the product to them. This commonly can be seen in automotive industries when a car

ordered by customer is obtained within 2 weeks. Typical companies may take close

to 2 months for order lead time inclusive of manufacturing time from the factory.

From this, we can foresee the importance of scheduling tasks for the benefit of sales

achievements, delivery lead time, model change planning and perhaps helps the new

product line up of certain companies. However, these scheduling processes are not

useful if the operations in the factory face many issues on their facilities. The

facilities for producing the desired products are very important because failure of

operating the facilities in normal condition will affect all the factors mentioned above.

This study solely look into facilities breakdown problems and the need of

breakdown’s repair time to be reduced for obtaining high machine utilization rate.

With high machine utilization rate, production processes run smoothly and will be

able to achieve sales and profit targets as part of the organizations’ desirable goals. In

this study, machine breakdown repair time is being analysed.

1.2 Background and Rationale

The scheduling tasks can be made through various systems that many experts

have designed and developed through the decades. Scheduling in a typical and small

manufacturing factory is performed using simple Excel Worksheet. The rule of

thumb for scheduling is supply meets the demands i.e. supply must be equal to

demand. Demand from sales section is converted to supply as the output of the

product quantity called as finished goods. For bigger manufacturing companies

usually uses the similar formulae but with planned quantity of inventory. However,

for manufacturing companies that make electrical appliances, automotive, food

industries and other industries those indirectly play a dominance role in the

3

consumer’s market uses systems such as CANVAS METAFRAME, Sapphire,

Oracle System and other reliable software to make the scheduling tasks easy and fast.

Most of these systems’ inherent element is actually using conceptual artificial

intelligence. Artificial Intelligence can be simply defined as the study of systems that

act in a way that to any observer would appear to be intelligent (Coppin, 2004).

Customer needs have become the main priority for any manufacturing firms.

The most critical criteria for a customer are to obtain his/her product within the

required or fastest time. This is a major constraints neither the planner nor the shop

floor supervisors. They have to complete their work order within the time frame

given by the demanding sales. Thus, the multiple model of a product will be

automatically factored into the schedule plan. With this, planners need to make many

assumptions during making the schedule. They have to factor in the other

requirements of other models too. Mixing many models into a production line is

rather difficult tasks and requires a lot of brainstorming. The rationale is these

planners will make some assumption such as IF THEN rules manually. For instance,

Rule 1: IF the line produces 500 sets of model A by 3.00pm for delivery, THEN

balance working time of 2 hours can be fitted with another model B which delivery

requirement falls on the same day. The main constraint is the quantity of model B

that can be produced is 200 sets for 2 hours. Rule 2: IF the model B demand for the

day is 150 sets for delivery, THEN rule 1 can be implemented with respect of rule 2

executions. The balance 50 sets of model B becomes an inventory for the day before

next delivery. The above rules are just for an example, but in real time, the

constraints can be as complicated as producing more than 5 models a day.

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1.3 Problem Statement

Production scheduling and it’s diagnose towards re-scheduling is performed

manually especially and particularly for machine breakdown issue. Typical

scheduling is made in Excel worksheet and adjusted manually on the same Excel

worksheet. There is no proper method for making schedule automatically thus

reducing job of re-scheduling.

The machine breakdown occurrences need justification of recovery solution

in term of mean repair time. Apart from that, if machine breakdown occur, the

schedule is adjusted manually. Nevertheless, some recovery solutions in term of

repair time can be not feasible enough to meet due dates and meeting other

requirements in production scheduling.

1.4 Objectives of the Project

1. To train the neurons of repair time samples based on the machine

breakdown list.

2. To compute the mean square error (MSE) and regression, R value of

the trained neural network.

3. To evaluate and validate the regression of the trained network by

creating the regression model and compute the optimized mean repair

time for machine breakdown.

5

1.5 Research Questions

1. How the neurons are being trained and how the samples of machine

repair time are taken?

2. How the mean square error (MSE) and regression R value is obtained

from the trained network?

3. What is the optimized mean repair time for the breakdown phenomena?

1.6 Scope of Study

The scope of this project is restricted to machine breakdown phenomena as

the samples. However, a rather huge sample of data in terms of repair time is taken

from a manufacturing company. The sample of repair time is restricted to only one

machine which has been operating for more than 25 years. The samples are in range

of 21 years where every year, all phenomena that have been recorded with repair

time are used in this project for the neural network training.

The MATLAB's Neural Network Toolbox is used throughout the project. In

particular, the Neural Network Fitting Tool is used which is a ready-made Graphical

User Interface inside the said program. The fitting tool uses the approach of

Levenberg-Marquardt back propagation algorithm as a training tool for the neurons.

The neurons in this study refer to the samples of repair time. Microsoft Excel is used

to assist in sorting out the data based on the required testing, validation and training

of the neurons.

6

The samples are divided into several groups for comparison purposes which

are purely based on the type of machine breakdown and its cause-effect interrelation.

1.7 Significance of study

This research is aimed to ease the task of production planners and

maintenance team. It can reduce the time taken for a planner to analyse the solutions

for recovery time and fit the recovery schedule into the actual dynamic schedule.

Planners no longer need to plan how to fit the recovery schedule since this research

acquires the help of Artificial Neural Network to make the recovery plan.

Some manufacturing companies tend to conduct meeting to solve machine

breakdown problems. Supervisor, maintenance technicians and production planners

gather to conclude the best recovery solutions after taking consideration of machine

downtime and other considerable factors. But, in certain cases, there will be

misunderstanding between these groups of employees. Thus, the new system will be

able to reduce meetings between them and indirectly solve the recovery time.

This research upon completion need to be reviewed again and if possible, a

Graphical User Interface (GUI) is prepared to ease any end user. Hence, this GUI

becomes a product to be targeted to all manufacturing companies. Certain service

companies may benefit from this research depending on their business needs.

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1.8 Structure of Thesis – Chapters Overview

This thesis shall consist of basic elements of project report. Chapter 1

discusses about the introduction, research objectives, scope of project and the

significance of the project. The next Chapter 2 is the literature review of previous

researches and projects that related with this thesis. Chapter 3 discusses the steps or

methodology of the project. The methodology of this project is based on simulation

and testing, thus the final step consists of testing data and its validation. Chapter 4

summarizes the simulation steps upon successful data selection. Chapter 5 consists of

types of testing conducted and selected neural network training. The neurons are

trained in this chapter based on certain manipulated conditions before training

activity. Chapter 6 is the discussion topic on results gained through this project and

includes the validation of selected sample data. Besides, various analyses of the

results are summarized in this chapter. Chapter 7 concludes the project with

recommendations for future work.

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