Using Image Processing And Quantitative Technique To Determine The Onset Of Gear Failure.
i
USING IMAGE PROCESSING
AND QUANTITATIVE TECHNIQUE
TO DETERMINE THE ONSET OF GEAR FAILURE
TENGKU AHMAD HAFIZ BIN TENGKU DIN
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
i
TG AHMAD HAFIZ BIN TG DIN
BACHELOR OF MECHANICAL ENGINEERING
(PLANT & MAINTENANCE )(HONS.)
2015
UTeM
ii
SUPERVISOR DECLARATION
“I hereby declare that I have read this thesis and in my opinion, this thesis is
sufficient in terms of scope and quality for the award of the degree of Bachelor
Mechanical Engineering (Plant and Maintenance)”
Signature:
...................................
Supervisor:
DR. REDUAN MAT DAN
Date:
29 JUNE 2015
i
USING IMAGE PROCESSING AND QUANTITATIVE
TECHNIQUE TO DETERMINE
THE ONSET OF GEAR FAILURE
TENGKU AHMAD HAFIZ BIN TENGKU DIN
A thesis submitted to the Universiti Teknikal Malaysia Melaka
for the Bachelor of Mechanical Engineering in the
Faculty of Mechanical Engineering (Plant and Maintenance)
Faculty of Mechanical Engineering
Universiti Teknikal Malaysia Melaka
JUNE 2015
ii
DECLARATION
“I hereby declare that the work in this thesis is my own except for summaries and
quotations which have been duly acknowledged.”
Signature:
...................................
Author:
TENGKU AHMAD HAFIZ BIN TENGKU
DIN
Date:
26 JUNE 2015
iii
Dedicated to my beloved family
iv
ACKNOWLEDGMENT
All Praise to Almighty Allah S.W.T for granting me confidence and
determination in completing this work to its conclusion. Without His blessing and
help, this thesis would not be completed successfully.
I am especially grateful to my beloved parents and family for their support in
my studies throughout the three years.
I would like to highly praise my supervisor Dr. Reduan Mat Dan for his
comprehensive guidance and constructive comments thorough advice, constant
support and encouragement until completion of this thesis.
I would like to thank the staff of Universiti Teknikal Malaysia Melaka
(UTeM), friends, colleagues and others for their assistance and support during the
experiment.
Last but not least, I would like to thank everyone that's involved directly or indirectly
in making and completing this project successfully. May all your cooperation and
contribution will be rewarded from Allah s.w.t.
v
ABSTRACT
The aim of this study is to predict the onset of gear failure based on operating
hours. Wear Debris Analysis (WDA) has been proven useful in providing valuable
information as it also provides supporting indication of the nature and severity of
gear failure. It is important to analyse the basic features of wear debris for prediction
of component machine condition. In this research, a back to back test rig was used to
study the effect of deterioration stage of gear against abnormal loading time. This
thesis proposes an offline sampling technique which uses image processing and
particle counts to diagnose the condition of machinery. It emphasizes on using size,
shape and surface texture of the particles generated over time to predict onset of gear
failure. Therefore, maintenance action can be taken before gears reach catastrophic
failure. By the end of the experiment, result shows an increasing of operating time,
there will be an increasing of size and increasing the number of particle count of
wear particles that generated from friction between two surfaces of gears.
vi
ABSTRAK
Tujuan kajian ini adalah untuk meramal berlakunya kegagalan gear
berdasarkan waktu operasi. Analisis Serpihan Haus (ASH) telah terbukti berguna
dalam menyediakan maklumat yang berharga kerana ia juga menyediakan sokongan
petunjuk kepada jenis dan tahap kegagalan gear. Ia adalah penting untuk
menganalisis ciri-ciri asas serpihan haus untuk ramalan keadaan mesin komponen.
Dalam kajian ini, „back to back oil test rig‟ telah digunapakai untuk mengkaji kesan
peningkatan kemerosotan gear terhadap waktu bebanan tidak normal. Tesis ini
mencadangkan teknik persampelan luar talian dengan menggunakan pemprosesan
imej dan bilangan zarah untuk menganalisa keadaan jentera dan mesin. Ia memberi
penekanan kepada saiz, bentuk dan tekstur permukaan zarah yang dijana dari masa
ke semasa operasi untuk meramalkan masa berlakunya kegagalan gear. Oleh itu
tindakan penyelenggaraan boleh diambil sebelum gear mencapai kegagalan yang
amat teruk. Pada akhir eksperimen, keputusan menunjukkan peningkatan masa
operasi gear, akan ada peningkatan saiz zarah dan peningkatan jumlah kiraan zarah
serpihan haus yang dihasilkan oleh geseran daripada permukaan daripada dua gear.
vii
TABLE OF CONTENT
CHAPTER
CHAPTER 1
TITLE
PAGES
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENT
vii
LIST OF TABLE
xi
LIST OF FIGURE
xii
NOMENCLATURE
xiv
INTRODUCTION
1.1
Background
1.2
Problem Statement
1.3
Objectives
1.4
Scope
1
2
4
4
viii
CHAPTER 2
CHAPTER 3
LITERATURE REVIEW
2.1
Introduction
5
2.2
Wear debris analysis
5
2.3
Wear debris generation
6
2.4
How gear generated wear debris
8
2.5
Gear failure modes
9
2.6
Gear wear phenomena
10
2.7
Classification of wear debris
13
2.7.1
13
2.7.1.1 Particle shape
14
2.7.1.2 Quantitative basic shape analysis
17
2.7.1.3 Colour
18
2.7.1.4 Surface textures
18
2.8
Types of wear debris particles
19
2.9
General detecting particles techniques
21
METHODOLOGY
3.1
Introduction
23
3.2
Experimental method
23
3.3
Analysis method
25
3.1.1 Qualitative method
25
3.1.2 Quantitative method
27
Flow chart
31
3.4
CHAPTER 4
Particle morphology
RESULT AND ANALYSIS
4.1
4.2
Introduction
32
4.1.1 Analysis of data
32
4.1.2 Assumptions in analysis
33
Quantitative measurement
34
4.2.1 Experimental result
36
ix
4.3
Qualitative measurement
42
4.3.1 Qualitative measurement of wear debris
45
particles
CHAPTER 5
CHAPTER 6
DISCUSSION
5.1
Introduction
47
5.2
Discussion on methodology
47
5.3
Discussion on experimental work
48
5.4
Discussion on result normalization
50
CONCLUSION AND FUTURE WORK
6.1
Introduction
52
6.2
Achievement of objectives
52
6.3
Future work
54
REFERENCES
55
APPENDIX I
A simple MATLAB program for capturing images and
58
establishing coordinates
APPENDIX II
60
APPENDIX III
Table App III-1: Experiment data wear rate for CHS
63
Table App III-2: Experiment data wear rate for DLC
64
Table App III-3: Experiment data wear rate for LCA
65
x
APPENDIX IV
GANTT CHART for PSM I
66
GANTT CHART for PSM II
67
xi
LIST OF TABLE
TABLE
TITLE
PAGES
2-1
Wear particle shape and size descriptors
17
2-2
Shape parameters
17
2-3
Effect colour with material compositions
18
2-4
Relationship between type of particles
with size and thickness ratio
20
4-1
Experiment data particles counts for CHS
36
4-2
Experiment data particles counts for DLC
37
4-3
Experiment data particles counts for LCA
38
4-4
Images of gear damage and particle
image for CHS
42
4-5
Images of gear damage and particle
image for DLC
43
4-6
Images of gear damage and particle
image for LCA
44
4-7
Measurement area, perimeter, roundness
and size of the particle
45
xii
LIST OF FIGURES
FIGURES
TITLE
PAGES
2-1
The bathtub curve
6
2-2
The six types of failure patterns
7
2-3
Gear mesh contact points
8
2-4
Part of the mesh cycle
9
2-5
Types of gear wears failure
11
2-6
Particle Morphology
13
2-7
Chunk and slabs
16
2-8
Sphere
16
2-9
Distorted smooth ovoid (pebbles)
16
2-10
Platelets and flakes
16
2-11
Curls, spirals and slivers
16
2-12
Rolls
16
2-13
Strand and fibres
16
2-14
Smooth surface textures
19
2-15
Pitted surface textures
19
2-16
Rough surface textures
19
2-17
Striation surface textures
19
2-18
Sampling techniques
22
3-1
Back to back oil test rig front view from front view
24
3-2
Main component of test rig in schematic drawing
24
3-3
NASA Dextron III Lubrication Oil
25
xiii
3-4
A peristaltic pump
25
3-5
Setup of image capturing equipment
26
3-6
52ZM Stereoscopic Zoom Microscope
26
3-7
Filter patches
26
3-8
Debris image captured using Stereo Zoom Microscope
27
3-9
Drag cursor to measure the longest length
27
3-10
Determination of the area of particle using AMS
27
machinery Manager software
3-11
5200 Trivector Analyzer
28
3-12
52DV digital viscometer
28
4-1
Misalignment device
35
4-2
Particle counts for carbon hardened steel versus time,
39
hours by normalization method
4-3
Particle counts for diamond like coating versus time,
40
hours by normalization method
4-4
Particle counts for low carbon alloy versus time, hours
41
by normalization method
5-1
Gear setup
49
5-2
Oil sump cover
49
5-3
Looseness of torsional load
49
5-4
Leakage occur
49
xiv
NOMENCLATURE
WDA
Wear Debris Analysis
LCA
Low Carbon Alloy
DLC
Diamond-like Coating Carbon
CHS
Carbon Hardened Steel
CBM
Condition Based Maintenance
CCD
Charge Couple Device
REB
Rolling Element Bearing
ATF
Automatic Transmission Fluid
DC
Direct Current
DV
Digital Viscometer
LED
Light Emitting Diode
RPM
Rotation Per Minutes
1
CHAPTER 1
INTRODUCTION
1.1
BACKGROUND
Modern high speed and power machinery components will reach the end of their
useful operating life due to a gradual or sudden failure like gears, bearings, pumps,
hydraulics systems, turbines, compressors and motor [1]. A sudden failure is not
desirable as it may involve in human life and interruption of machine operation [2].
To avoid and minimize sudden failure and possibility consequence, it's very
important to determine the failure root cause from the component machine in near
real time [3]. Wear is one of the failures that could occur in component and it is a
normal characteristic of machine operation. Wear is something that's not required,
but it happened nevertheless and also unwanted removal of surface particle [4]. Wear
can reduce mechanical efficiency and a permanent loss of material. From the wear
processes, debris was generated from rubbing surface and produce friction of load
carrying machine component. Wear debris generate in lubricating oil carry important
information related to the condition and health of the component machinery
Condition based maintenance (CBM) also known as predictive maintenance aims to
monitor life or health of a machine‟s condition really its reliability. CBM may go on
a whole host of other connected benefit and valuable addition to comprehensive such
as reduced maintenance cost, minimize damage of equipment, good financial return
and limit production loss. Various technologies are employed as part of a
2
comprehensive predictive maintenance program to monitor the signs of faults such as
vibration analysis, thermography, wear debris analysis, oil analysis, ultrasonic testing
and machine performance. Different condition monitoring techniques have their own
advantages and disadvantages. Unfortunately, no single technique has been able to
resolve all CBM requirements. Different of requirement in the plant is different
based on selecting CBM techniques. The WDA technique has been developed in
terms of wear debris size, shape, material composition, features of boundary and size
distribution [5; 6]. The particles collected from lubricant oil are useful in machinery
maintenance decisions for predicting and accessing machine condition. WDA is the
most effective CBM strategy to detect and diagnosing the deterioration stage of
machine component through the analysis of oil properties and wear particles.
Furthermore, many good benefits can result from CBM practices when gear fault
detection is preferred, although have not been applied widely in the industry due to
lack of automation and time consuming. WDA can provide the detail of wear mode,
wear severity, surface degradation rate and component source [7]. This motivates
researcher to study the onset of gear failure related by using WDA.
1.2
PROBLEM STATEMENT
Gear is widely used in industrial application such as power generation in oil and gas
industry. Gear is a component within a transmission device where it transmits
rotational force to another gear or other component. Every gear has its own operating
lifetime. Therefore, it only can be used in certain period based on its lifetime. Based
on bathtub curve, gear will be operated based on its lifetime from infant mortality to
normal life where it is at a low constant failure rate. After that, it will fail or end of
life caused by worn –out due to fatigue of materials. A gear is failed when it no
longer performs efficiently and safely for which it is designed. There are many
causes of failure that may occur in certain system operation that involved in gear
such as rubbing wear, cutting wear, rolling fatigue, severe sliding wear and combined
rolling and sliding wear action between it solid surfaces.
3
When one of the gears is found to be defective while it is operating, it could affect
the system and may cause serious damages. It can interrupt the production or can
cause injury. Gear defect will further produce debris particles that are generated by
wear processes. When the wear occurs, they can be classified as a type of failure
occur by their shapes, sizes, edge details, colours, thickness ratios and surface
textures. Wear debris is a characteristic of the condition under which it was formed
and study of these wear particles can yield significant information about the state of
the surface from which it is produced [5]. Wear is not only caused by two surfaces
rubbing together, although it is a major cause. Wear condition is also due to vibration
or multiple influence known as fatigue wear because of some other motion or
external impact. Materials are lost from the surface of a component gear. Thus when
wear occurs, it may not be able to be stopped, but it can be anticipated to a certain
extent and action taken before wear-out [4].
Therefore, WDA is one of the ways to monitor the health of the gear. This case study
will be focusing on three morphologies of wear debris analysis, which are shapes,
size and surface textures. Three types material of helical gear will be used as tested
gear, which is low carbon alloy (LCA), diamond-like carbon coating (DLC) and
carbon hardened steel (CHS). Then, debris particles will be analysed using software
AMS Suite Machinery Health Manager 5.5 to investigate the failure of machine
component. By making wear debris analysis, it can help to recognize a type of failure
based on information gain from qualitative and quantitative techniques.
4
1.3
OBJECTIVES
This research tries to accomplish the following objectives:
i.
To use image processing to detect failure signs on wear debris particles
generated from the gearbox.
ii.
1.4
i.
To use particle count for determining the onset failures of the gear.
SCOPE
Set up experimental work on back to back the gear test rig for collect wear
debris particles.
ii.
Use three types material of helical gear as tested gear, which is low carbon
alloy (LCA), diamond-like carbon coating (DLC) and carbon hardened steel
(CHS).
iii.
Perform qualitative and quantitative techniques to determine the deterioration
stage of gear failure.
5
CHAPTER 2
LITERATURE REVIEW
2.1
INTRODUCTION
The research served in this thesis is principally based on theoretical and
experimental. This chapter will review and discuss about generation and process of
wear debris, wear debris analysis, wear mechanism, wear morphology and types of
failure of gear. Furthermore, recognize how knowledge of wear debris features will
help to monitor the health, condition and deterioration stage of component machine
such as size, shape, material composition, features of the boundary and size
distribution.
2.2
WEAR DEBRIS ANALYSIS
Wear debris analysis is a great technique for non-instructive test of the oily part
of machinery because provide valuable information and supporting indication of the
nature and severity. WDA is widely applied technology in machine health and
condition monitoring. Every debris particle contained in the lubricating oil has its
own important in providing extremely valuable information on wear mechanism on
6
the condition and the health of the machine component by analysis shape, size,
quantity and composition of particles. The particle characteristics are appropriately
specific so that the operating wear modes within the machine may be determined and
allowing prediction of the imminent behaviour of the machine [8]. WDA technique is
even capable to determine wear from many processes namely sliding, rolling,
rubbing and cutting wear. Every mechanism is producing a different morphology.
Wear debris comes in a wide variety of sizes and shapes. Size wear debris may vary
anywhere from a few microns to thousand microns.
2.3
WEAR DEBRIS GENERATION
Wear debris is a particle which have been shed or loss from the mechanical
surface component [20]. Wear debris particle produced from the machine component
like gears or bearing result of two surfaces rubbing, sliding or rolling together.
Meanwhile the plastic flow or breakage occurs due excessive loading. It may also
have some influences in developing a kind of wear. Wear debris generation is
frequently illustrated based on a bathtub curve as shown in Figure 2.1. It is a
statistical method used to demonstrate the general pattern of a machine system based
on its operating time.
Figure 2-1: The bathtub curve [9]
7
„Bathtub curve‟ is basically has three regions of wear in life period, which is
running-in, useful working life and end of life wear out. The curve describes the
relative failure rate of an entire wear particle generation versus time. This bathtub
curve is illustrated that a new machine has a relatively high probability of failure
[10]. In the first period, also known as infant mortality it is decreasing rapidly of
failure wear rate. This period is typically quite short interval. Wear is usually
beneficial and provides an improvement for later running and particularly true for
gears. Following the initial period the probability of failure is low and constant
useful working life of the component for the normal life stage. At this period debris
is produced quite small and minor in size and quantity. Lubrication is doing well in
controlling the effect of wear of gears. The final stage is the disaster period because
of the increasing failure rate. At this stage, fatigue starts to show with the result of
large chunks of debris and increasing quantity of debris exponentially. The machine
is so badly worn that there is serious misalignment and an overload of the
component.
Figure 2-2: The six types of failure patterns
Figure 2-2, shows six patterns of failure [11]. Pattern A shows a constant increasing
conditional probability of failure from beginning of the machine life and going with
ending in the wear out region and about 2 % of failure rates. The longer operation
time the high failure rate. This pattern is tough to differentiate between running-in
and useful working life. Pattern B is the well-known bathtub curve as explained
before based on Figure 2-1 and about 14 % of failure rates. Pattern C shows a
consistently increasing conditional probability of failure rate from running-in to the
USING IMAGE PROCESSING
AND QUANTITATIVE TECHNIQUE
TO DETERMINE THE ONSET OF GEAR FAILURE
TENGKU AHMAD HAFIZ BIN TENGKU DIN
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
i
TG AHMAD HAFIZ BIN TG DIN
BACHELOR OF MECHANICAL ENGINEERING
(PLANT & MAINTENANCE )(HONS.)
2015
UTeM
ii
SUPERVISOR DECLARATION
“I hereby declare that I have read this thesis and in my opinion, this thesis is
sufficient in terms of scope and quality for the award of the degree of Bachelor
Mechanical Engineering (Plant and Maintenance)”
Signature:
...................................
Supervisor:
DR. REDUAN MAT DAN
Date:
29 JUNE 2015
i
USING IMAGE PROCESSING AND QUANTITATIVE
TECHNIQUE TO DETERMINE
THE ONSET OF GEAR FAILURE
TENGKU AHMAD HAFIZ BIN TENGKU DIN
A thesis submitted to the Universiti Teknikal Malaysia Melaka
for the Bachelor of Mechanical Engineering in the
Faculty of Mechanical Engineering (Plant and Maintenance)
Faculty of Mechanical Engineering
Universiti Teknikal Malaysia Melaka
JUNE 2015
ii
DECLARATION
“I hereby declare that the work in this thesis is my own except for summaries and
quotations which have been duly acknowledged.”
Signature:
...................................
Author:
TENGKU AHMAD HAFIZ BIN TENGKU
DIN
Date:
26 JUNE 2015
iii
Dedicated to my beloved family
iv
ACKNOWLEDGMENT
All Praise to Almighty Allah S.W.T for granting me confidence and
determination in completing this work to its conclusion. Without His blessing and
help, this thesis would not be completed successfully.
I am especially grateful to my beloved parents and family for their support in
my studies throughout the three years.
I would like to highly praise my supervisor Dr. Reduan Mat Dan for his
comprehensive guidance and constructive comments thorough advice, constant
support and encouragement until completion of this thesis.
I would like to thank the staff of Universiti Teknikal Malaysia Melaka
(UTeM), friends, colleagues and others for their assistance and support during the
experiment.
Last but not least, I would like to thank everyone that's involved directly or indirectly
in making and completing this project successfully. May all your cooperation and
contribution will be rewarded from Allah s.w.t.
v
ABSTRACT
The aim of this study is to predict the onset of gear failure based on operating
hours. Wear Debris Analysis (WDA) has been proven useful in providing valuable
information as it also provides supporting indication of the nature and severity of
gear failure. It is important to analyse the basic features of wear debris for prediction
of component machine condition. In this research, a back to back test rig was used to
study the effect of deterioration stage of gear against abnormal loading time. This
thesis proposes an offline sampling technique which uses image processing and
particle counts to diagnose the condition of machinery. It emphasizes on using size,
shape and surface texture of the particles generated over time to predict onset of gear
failure. Therefore, maintenance action can be taken before gears reach catastrophic
failure. By the end of the experiment, result shows an increasing of operating time,
there will be an increasing of size and increasing the number of particle count of
wear particles that generated from friction between two surfaces of gears.
vi
ABSTRAK
Tujuan kajian ini adalah untuk meramal berlakunya kegagalan gear
berdasarkan waktu operasi. Analisis Serpihan Haus (ASH) telah terbukti berguna
dalam menyediakan maklumat yang berharga kerana ia juga menyediakan sokongan
petunjuk kepada jenis dan tahap kegagalan gear. Ia adalah penting untuk
menganalisis ciri-ciri asas serpihan haus untuk ramalan keadaan mesin komponen.
Dalam kajian ini, „back to back oil test rig‟ telah digunapakai untuk mengkaji kesan
peningkatan kemerosotan gear terhadap waktu bebanan tidak normal. Tesis ini
mencadangkan teknik persampelan luar talian dengan menggunakan pemprosesan
imej dan bilangan zarah untuk menganalisa keadaan jentera dan mesin. Ia memberi
penekanan kepada saiz, bentuk dan tekstur permukaan zarah yang dijana dari masa
ke semasa operasi untuk meramalkan masa berlakunya kegagalan gear. Oleh itu
tindakan penyelenggaraan boleh diambil sebelum gear mencapai kegagalan yang
amat teruk. Pada akhir eksperimen, keputusan menunjukkan peningkatan masa
operasi gear, akan ada peningkatan saiz zarah dan peningkatan jumlah kiraan zarah
serpihan haus yang dihasilkan oleh geseran daripada permukaan daripada dua gear.
vii
TABLE OF CONTENT
CHAPTER
CHAPTER 1
TITLE
PAGES
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENT
vii
LIST OF TABLE
xi
LIST OF FIGURE
xii
NOMENCLATURE
xiv
INTRODUCTION
1.1
Background
1.2
Problem Statement
1.3
Objectives
1.4
Scope
1
2
4
4
viii
CHAPTER 2
CHAPTER 3
LITERATURE REVIEW
2.1
Introduction
5
2.2
Wear debris analysis
5
2.3
Wear debris generation
6
2.4
How gear generated wear debris
8
2.5
Gear failure modes
9
2.6
Gear wear phenomena
10
2.7
Classification of wear debris
13
2.7.1
13
2.7.1.1 Particle shape
14
2.7.1.2 Quantitative basic shape analysis
17
2.7.1.3 Colour
18
2.7.1.4 Surface textures
18
2.8
Types of wear debris particles
19
2.9
General detecting particles techniques
21
METHODOLOGY
3.1
Introduction
23
3.2
Experimental method
23
3.3
Analysis method
25
3.1.1 Qualitative method
25
3.1.2 Quantitative method
27
Flow chart
31
3.4
CHAPTER 4
Particle morphology
RESULT AND ANALYSIS
4.1
4.2
Introduction
32
4.1.1 Analysis of data
32
4.1.2 Assumptions in analysis
33
Quantitative measurement
34
4.2.1 Experimental result
36
ix
4.3
Qualitative measurement
42
4.3.1 Qualitative measurement of wear debris
45
particles
CHAPTER 5
CHAPTER 6
DISCUSSION
5.1
Introduction
47
5.2
Discussion on methodology
47
5.3
Discussion on experimental work
48
5.4
Discussion on result normalization
50
CONCLUSION AND FUTURE WORK
6.1
Introduction
52
6.2
Achievement of objectives
52
6.3
Future work
54
REFERENCES
55
APPENDIX I
A simple MATLAB program for capturing images and
58
establishing coordinates
APPENDIX II
60
APPENDIX III
Table App III-1: Experiment data wear rate for CHS
63
Table App III-2: Experiment data wear rate for DLC
64
Table App III-3: Experiment data wear rate for LCA
65
x
APPENDIX IV
GANTT CHART for PSM I
66
GANTT CHART for PSM II
67
xi
LIST OF TABLE
TABLE
TITLE
PAGES
2-1
Wear particle shape and size descriptors
17
2-2
Shape parameters
17
2-3
Effect colour with material compositions
18
2-4
Relationship between type of particles
with size and thickness ratio
20
4-1
Experiment data particles counts for CHS
36
4-2
Experiment data particles counts for DLC
37
4-3
Experiment data particles counts for LCA
38
4-4
Images of gear damage and particle
image for CHS
42
4-5
Images of gear damage and particle
image for DLC
43
4-6
Images of gear damage and particle
image for LCA
44
4-7
Measurement area, perimeter, roundness
and size of the particle
45
xii
LIST OF FIGURES
FIGURES
TITLE
PAGES
2-1
The bathtub curve
6
2-2
The six types of failure patterns
7
2-3
Gear mesh contact points
8
2-4
Part of the mesh cycle
9
2-5
Types of gear wears failure
11
2-6
Particle Morphology
13
2-7
Chunk and slabs
16
2-8
Sphere
16
2-9
Distorted smooth ovoid (pebbles)
16
2-10
Platelets and flakes
16
2-11
Curls, spirals and slivers
16
2-12
Rolls
16
2-13
Strand and fibres
16
2-14
Smooth surface textures
19
2-15
Pitted surface textures
19
2-16
Rough surface textures
19
2-17
Striation surface textures
19
2-18
Sampling techniques
22
3-1
Back to back oil test rig front view from front view
24
3-2
Main component of test rig in schematic drawing
24
3-3
NASA Dextron III Lubrication Oil
25
xiii
3-4
A peristaltic pump
25
3-5
Setup of image capturing equipment
26
3-6
52ZM Stereoscopic Zoom Microscope
26
3-7
Filter patches
26
3-8
Debris image captured using Stereo Zoom Microscope
27
3-9
Drag cursor to measure the longest length
27
3-10
Determination of the area of particle using AMS
27
machinery Manager software
3-11
5200 Trivector Analyzer
28
3-12
52DV digital viscometer
28
4-1
Misalignment device
35
4-2
Particle counts for carbon hardened steel versus time,
39
hours by normalization method
4-3
Particle counts for diamond like coating versus time,
40
hours by normalization method
4-4
Particle counts for low carbon alloy versus time, hours
41
by normalization method
5-1
Gear setup
49
5-2
Oil sump cover
49
5-3
Looseness of torsional load
49
5-4
Leakage occur
49
xiv
NOMENCLATURE
WDA
Wear Debris Analysis
LCA
Low Carbon Alloy
DLC
Diamond-like Coating Carbon
CHS
Carbon Hardened Steel
CBM
Condition Based Maintenance
CCD
Charge Couple Device
REB
Rolling Element Bearing
ATF
Automatic Transmission Fluid
DC
Direct Current
DV
Digital Viscometer
LED
Light Emitting Diode
RPM
Rotation Per Minutes
1
CHAPTER 1
INTRODUCTION
1.1
BACKGROUND
Modern high speed and power machinery components will reach the end of their
useful operating life due to a gradual or sudden failure like gears, bearings, pumps,
hydraulics systems, turbines, compressors and motor [1]. A sudden failure is not
desirable as it may involve in human life and interruption of machine operation [2].
To avoid and minimize sudden failure and possibility consequence, it's very
important to determine the failure root cause from the component machine in near
real time [3]. Wear is one of the failures that could occur in component and it is a
normal characteristic of machine operation. Wear is something that's not required,
but it happened nevertheless and also unwanted removal of surface particle [4]. Wear
can reduce mechanical efficiency and a permanent loss of material. From the wear
processes, debris was generated from rubbing surface and produce friction of load
carrying machine component. Wear debris generate in lubricating oil carry important
information related to the condition and health of the component machinery
Condition based maintenance (CBM) also known as predictive maintenance aims to
monitor life or health of a machine‟s condition really its reliability. CBM may go on
a whole host of other connected benefit and valuable addition to comprehensive such
as reduced maintenance cost, minimize damage of equipment, good financial return
and limit production loss. Various technologies are employed as part of a
2
comprehensive predictive maintenance program to monitor the signs of faults such as
vibration analysis, thermography, wear debris analysis, oil analysis, ultrasonic testing
and machine performance. Different condition monitoring techniques have their own
advantages and disadvantages. Unfortunately, no single technique has been able to
resolve all CBM requirements. Different of requirement in the plant is different
based on selecting CBM techniques. The WDA technique has been developed in
terms of wear debris size, shape, material composition, features of boundary and size
distribution [5; 6]. The particles collected from lubricant oil are useful in machinery
maintenance decisions for predicting and accessing machine condition. WDA is the
most effective CBM strategy to detect and diagnosing the deterioration stage of
machine component through the analysis of oil properties and wear particles.
Furthermore, many good benefits can result from CBM practices when gear fault
detection is preferred, although have not been applied widely in the industry due to
lack of automation and time consuming. WDA can provide the detail of wear mode,
wear severity, surface degradation rate and component source [7]. This motivates
researcher to study the onset of gear failure related by using WDA.
1.2
PROBLEM STATEMENT
Gear is widely used in industrial application such as power generation in oil and gas
industry. Gear is a component within a transmission device where it transmits
rotational force to another gear or other component. Every gear has its own operating
lifetime. Therefore, it only can be used in certain period based on its lifetime. Based
on bathtub curve, gear will be operated based on its lifetime from infant mortality to
normal life where it is at a low constant failure rate. After that, it will fail or end of
life caused by worn –out due to fatigue of materials. A gear is failed when it no
longer performs efficiently and safely for which it is designed. There are many
causes of failure that may occur in certain system operation that involved in gear
such as rubbing wear, cutting wear, rolling fatigue, severe sliding wear and combined
rolling and sliding wear action between it solid surfaces.
3
When one of the gears is found to be defective while it is operating, it could affect
the system and may cause serious damages. It can interrupt the production or can
cause injury. Gear defect will further produce debris particles that are generated by
wear processes. When the wear occurs, they can be classified as a type of failure
occur by their shapes, sizes, edge details, colours, thickness ratios and surface
textures. Wear debris is a characteristic of the condition under which it was formed
and study of these wear particles can yield significant information about the state of
the surface from which it is produced [5]. Wear is not only caused by two surfaces
rubbing together, although it is a major cause. Wear condition is also due to vibration
or multiple influence known as fatigue wear because of some other motion or
external impact. Materials are lost from the surface of a component gear. Thus when
wear occurs, it may not be able to be stopped, but it can be anticipated to a certain
extent and action taken before wear-out [4].
Therefore, WDA is one of the ways to monitor the health of the gear. This case study
will be focusing on three morphologies of wear debris analysis, which are shapes,
size and surface textures. Three types material of helical gear will be used as tested
gear, which is low carbon alloy (LCA), diamond-like carbon coating (DLC) and
carbon hardened steel (CHS). Then, debris particles will be analysed using software
AMS Suite Machinery Health Manager 5.5 to investigate the failure of machine
component. By making wear debris analysis, it can help to recognize a type of failure
based on information gain from qualitative and quantitative techniques.
4
1.3
OBJECTIVES
This research tries to accomplish the following objectives:
i.
To use image processing to detect failure signs on wear debris particles
generated from the gearbox.
ii.
1.4
i.
To use particle count for determining the onset failures of the gear.
SCOPE
Set up experimental work on back to back the gear test rig for collect wear
debris particles.
ii.
Use three types material of helical gear as tested gear, which is low carbon
alloy (LCA), diamond-like carbon coating (DLC) and carbon hardened steel
(CHS).
iii.
Perform qualitative and quantitative techniques to determine the deterioration
stage of gear failure.
5
CHAPTER 2
LITERATURE REVIEW
2.1
INTRODUCTION
The research served in this thesis is principally based on theoretical and
experimental. This chapter will review and discuss about generation and process of
wear debris, wear debris analysis, wear mechanism, wear morphology and types of
failure of gear. Furthermore, recognize how knowledge of wear debris features will
help to monitor the health, condition and deterioration stage of component machine
such as size, shape, material composition, features of the boundary and size
distribution.
2.2
WEAR DEBRIS ANALYSIS
Wear debris analysis is a great technique for non-instructive test of the oily part
of machinery because provide valuable information and supporting indication of the
nature and severity. WDA is widely applied technology in machine health and
condition monitoring. Every debris particle contained in the lubricating oil has its
own important in providing extremely valuable information on wear mechanism on
6
the condition and the health of the machine component by analysis shape, size,
quantity and composition of particles. The particle characteristics are appropriately
specific so that the operating wear modes within the machine may be determined and
allowing prediction of the imminent behaviour of the machine [8]. WDA technique is
even capable to determine wear from many processes namely sliding, rolling,
rubbing and cutting wear. Every mechanism is producing a different morphology.
Wear debris comes in a wide variety of sizes and shapes. Size wear debris may vary
anywhere from a few microns to thousand microns.
2.3
WEAR DEBRIS GENERATION
Wear debris is a particle which have been shed or loss from the mechanical
surface component [20]. Wear debris particle produced from the machine component
like gears or bearing result of two surfaces rubbing, sliding or rolling together.
Meanwhile the plastic flow or breakage occurs due excessive loading. It may also
have some influences in developing a kind of wear. Wear debris generation is
frequently illustrated based on a bathtub curve as shown in Figure 2.1. It is a
statistical method used to demonstrate the general pattern of a machine system based
on its operating time.
Figure 2-1: The bathtub curve [9]
7
„Bathtub curve‟ is basically has three regions of wear in life period, which is
running-in, useful working life and end of life wear out. The curve describes the
relative failure rate of an entire wear particle generation versus time. This bathtub
curve is illustrated that a new machine has a relatively high probability of failure
[10]. In the first period, also known as infant mortality it is decreasing rapidly of
failure wear rate. This period is typically quite short interval. Wear is usually
beneficial and provides an improvement for later running and particularly true for
gears. Following the initial period the probability of failure is low and constant
useful working life of the component for the normal life stage. At this period debris
is produced quite small and minor in size and quantity. Lubrication is doing well in
controlling the effect of wear of gears. The final stage is the disaster period because
of the increasing failure rate. At this stage, fatigue starts to show with the result of
large chunks of debris and increasing quantity of debris exponentially. The machine
is so badly worn that there is serious misalignment and an overload of the
component.
Figure 2-2: The six types of failure patterns
Figure 2-2, shows six patterns of failure [11]. Pattern A shows a constant increasing
conditional probability of failure from beginning of the machine life and going with
ending in the wear out region and about 2 % of failure rates. The longer operation
time the high failure rate. This pattern is tough to differentiate between running-in
and useful working life. Pattern B is the well-known bathtub curve as explained
before based on Figure 2-1 and about 14 % of failure rates. Pattern C shows a
consistently increasing conditional probability of failure rate from running-in to the