"Development of a methodology to assess the eligibility of a CBM program"

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D

IPARTIMENTO DI

I

NGEGNERIA DELL

’E

NERGIA DEI

S

ISTEMI

,

DEL

T

ERRITORIO E DELLE

C

OSTRUZIONI

RELAZIONE PER IL CONSEGUIMENTO DELLA LAUREA MAGISTRALE IN INGEGNERIA GESTIONALE

Development of a methodology to assess the

eligibility of a CBM programme

RELATORI IL CANDIDATO

Prof. Ing. Gino Dini Giulia Vassalle

Dipartimento di Ingegneria Civile e Industriale

Vassalle.giulia1991@gmail.com

Dott. John Ahmet Erkoyuncu

Department of Manufacturing (Cranfield University)

Sessione di Laurea del 04/10/2017 Anno Accademico 2016/2017

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Development of a methodology to assess the eligibility of a CBM programme

Giulia Vassalle

SOMMARIO

Questo lavoro di tesi è stato sviluppato presso la struttura estera “Through-life Engineering Services Centre” all’ Università di Cranfield e si focalizza sullo studio della Condition-based maintenance, il cui utilizzo si è rapidamente diffuso negli ultimi decenni grazie ai suoi vantaggi. Infatti, eseguendo attività di manutenzione sulla base delle condizioni in tempo reale della macchina/sistema sotto monitoraggio è possibile massimizzare la sicurezza, la disponibilità, l'affidabilità del sistema stesso e quindi di ottimizzare i costi di manutenzione. Nonostante tutti questi vantaggi, non è sempre possibile od opportuno adottare una strategia di CBM per ogni componente costituente il sistema. Pertanto, lo scopo della ricerca è di indagare, costruendo una metodologia, quali aspetti devono essere considerati affinché il programma di CBM sia considerato ‘eligible’. Più specificamente, la metodologia proposta analizza i seguenti aspetti della CBM: organizzativi, tecnici ed economici. Ogni aspetto corrisponde ad uno specifico strumento / tipo di analisi utilizzato. Inoltre, come test pratico per fornire linee guida successive a personale manutentivo, sono stati condotti diversi test su cuscinetti artificialmente danneggiati per valutare la loro criticità e classificarli in differenti stadi di deterioramento.

ABSTRACT

This thesis work has been developed at Cranfield University in the “Through-life Engineering Services Centre” and it focuses on the use of Condition-based maintenance strategy, which has spread rapidly over the past few decades thanks to its advantages. In fact, by performing maintenance activities based on real-time condition of the system/machine, it is possible to maximize its safety, availability, improve equipment reliability, and optimize maintenance costs. Despite all these advantages, in some cases it is not feasible or appropriate to adopt a CBM strategy for any component in the manufacturing industry. Therefore, the purpose of the research is to investigate, by building a methodology, which aspects need to be considered for a CBM programme to be eligible. More specifically, the proposed methodology analyses the following aspects of CBM: organizational, technical and economical. Each aspect corresponds to a specific tool/analysis used. Finally, to provide further guidelines to maintenance personnel, several tests have also been carried out on artificially-damaged bearings to assess their criticality and to classify them in different deterioration stages. Data were collected through Arduino and an accelerometer and processed using Python language

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Keywords:

Maintenance strategies, Condition monitoring, Sensors, Cost model, Maintenance costs, components in CBM, condition monitoring techniques

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ACKNOWLEDGEMENTS

The study and research work presented in this thesis has been carried out at the Through Life Engineering Service, Department of Manufacturing, Cranfield University (UK).

I would like to express sincere gratitude to my overseas supervisor, Dr John Ahmed Erkoyuncu for his guidance, thought provoking discussions and continuous support during the research. It was a pleasure to work with him and I hope that I managed to help him with his researches as well

I would also like to thank my hometown supervisor, Professor Gino Dini, for providing me the opportunity to develop this work and to gain this valuable experience.

I am also grateful to all my colleagues of the Department of Manufacturing and all the people I met at Cranfield University for their presence and support. But above all I would like to thank Elisa for her continuous help and motivation during this journey, especially when I started.

This journey would have not been possible without the help of my family. I am indebted to them for always supporting and believing in me.

I take this opportunity to express my gratitude to Marco, for his immeasurable patience and for being always close to me during my research work. I will never thank you enough.

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4.3.1 FMECA analysis ... 78 4.3.2 Avalibility of CM techniques ... 79 4.3.3 Availability of CM instrumentation... 79 4.3.4 Accessibility of components ... 79 4.3.5 Setting of CM interval ... 79 4.3.6 Size of P-F interval ... 80 4.4 CBM cost effectiveness ... 81 4.4.1 CBM testing ... 82 4.4.2 CBM monitoring performance ... 82

4.4.3 Maintenance cost model ... 83

4.4.3.1 Preface ... 83

4.4.3.2 Cost model ... 84

4.4.3.2.1 Assumptions and Limitations of the model ... 85

4.4.3.2.2 Assumptions of the best time T to replace a component ... 86

4.4.3.2.3 Total maintenance cost of component (First scenario) ... 87

4.4.3.2.3.1 Time-based maintenance costs of component ... 88

4.4.3.2.3.2 Corrective maintenance costs of component ... 91

4.4.3.2.3.3 Fixed maintenance costs of component ... 93

4.4.3.2.4 Total maintenance cost of component (Second scenario) ... 93

4.4.3.2.5 Conclusions ... 95

4.4.4 Implementation of CBM ... 95

5 ROLLING ELEMENT BEARING ... 97

5.1 Bearings’ eligibility assessment for a CBM program ... 98

5.2 Rolling element bearing FMECA analysis ... 90

5.2.1 Preface ... 90

5.3 Bearings CM ... 108

5.3.1 Preface ... 108

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5.3.2.3 Experimental results ... 118

5.3.2.4 Bearings degradation stages ... 123

5.3.2.1 Discussion and conclusion of the experiment ... 124

6 CONCLUSIONS ... 127 6.1 Conclusions ... 127 6.2 Research contribution ... 129 6.3 Future work ... 130 REFERENCES ... 132 7 APPENDICES ... 140

7.1 Maintenance Cost model ... 140

7.1.1 Binary variables used in the cost model ... 141

7.1.2 Cost model breakdown analysis (First scenario) ... 142

7.1.2.1 Brekdown of formulas that makes up the Total time-based maintenance cost of component ... 142

7.1.2.1.1 Cost of planned shutdown of system/machine ... 142

7.1.2.1.2 Set-up costs... 144

7.1.2.1.3 Replacement costs of component ... 145

7.1.2.1.4 Repair costs of component ... 147

7.1.2.1.5 Diagnosis costs of component ... 149

7.1.2.1.6 Inspections costs of component ... 149

7.1.2.2 Breakdown of formulas that make up the best time to replace T ... 150

7.1.2.2.1 Cost of failure of component ... 150

7.1.2.2.2 Scale and location parameter ... 152

7.1.2.3. Breakdowns of formulas that make up the total corrective maintenance cost of component ... 152

7.1.2.3.1 Cost of unplanned shutdown of system/machine ... 153

7.1.2.3.2 Set-up costs... 154

7.1.2.3.3 Replacement costs of component ... 156

7.1.2.3.4 Repair costs of component ... 158

7.1.2.3.5 Diagnosis costs ... 159

7.1.2.5 Failure probability of component at time t for a two-parameter Weibull distribution and average virtual interval ... 159

7.1.2.6 Breakdown of formulas that constitutes the total fixed maintenance costs of component ... 161

7.1.2.7 Discount rate ... 162

7.1.2.8 Constraints ... 162

7.2 Arduino code ... 163

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LIST OF FIGURES

Figure 2-1 Double diamond method ... 22

Figure 3-1 Areas of Literature Review ... 28

Figure 3-2 Maintenance strategies ... 30

Figure 3-3 Bathtub curves ... 33

Figure 3-4 Weibull plot ... 35

Figure 3-5 Steps of a CBM program ... 41

Figure 3-6 Ball bearing geometry ... 48

Figure 3-7 Fatigue failure ... 55

Figure 3-8 Wear failure ... 56

Figure 3-9 Overheating failure ... 56

Figure 3-10 Misalignement failure ... 57

Figure 3-11 Corrosion failure ... 58

Figure 3-12 False brinelling failure ... 59

Figure 3-13 True brinelling failure ... 59

Figure 3-14 Contamination failure ... 60

Figure 3-15 Inadequate lubrication failure ... 60

Figure 4-1 Key steps of CBM eligibility ... 66

Figure 4-2 Methodology to assess the eligibility of a CBM program ... 67

Figure 4-3 Example of a hard failure ... 72

Figure 4-4 Example of a soft failure ... 72

Figure 4-5 Bathtub curve ... 73

Figure 4-6 Cruciality analysis ... 75

Figure 4-7 CBM technical feasibility study ... 77

Figure 4-8 P-F interval ... 80

Figure 5-1 Photograph of the experimental set-up ... 109

Figure 5-2-5-3 Ball bearing geometry ... 110

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Figure 5-6 Photograph of Arduino Yun board ... 116

Figure 5-7 Photograph of the accelerometer mounted on the housing ... 117

Figure 5-8 Raw vibration signal from healthy bearing ... 119

Figure 5-9 Raw vibration signal from small defect size in outer race ... 119

Figure 5-10 Raw vibration signal from medium defect size in outer race ... 119

Figure 5-11 Raw vibration signal from large defect size in outer race ... 120

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LIST OF TABLES

Table 3-1 Advantages and disadvantages of CM ... 32

Table 3-2 Advantages and disadvantages of TBM ... 39

Table 3-3 Advantages and disadvantages of CBM ... 43

Table 3-4 Bearings failure causes and % of contribution ... 53

Table 4-1 SWOT analysis of CBM strategy ... 69

Table 4-2 Correspondence between crucial category and characteristics of component ... 74

Table 4-3 Key Performance Indicators of CBM ... 82

Table 5-1 Severity scale ... 100

Table 5-2 Occurrence scale ... 101

Table 5-3 Detectability scale ... 102

Table 5-4 Ball bearing FMECA analysis ... 105

Table 5-5 Deep groove ball bearing UOO2 specification ... 111

Table 5-6 Geometry of U002 and outer race defect frequency( BPFO)…... ..115

Table 5-7 Connection between Arduino Yun and accelerometer MPU6050 .. 117

Table 5-8 Amplitude of BPFO ... 122

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LIST OF EQUATIONS

(3-1) ... 35 (3-2) ... 38 (3-3) ... 38 (3-4) ... 54 (4-1) ... 81 (5-1) ... 106 (5-2) ... 114 (5-3) ... 114 (5-4) ... 114 (5-5) ... 114 (5-6) ... 115 (5-7) ... 117 (5-8) ... 120

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LIST OF ACRONYMS ABBREVIATIONS

A list of acronyms abbreviations is included, which will be used at various points in the chapters that follow:

Acronyms Corresponding to: Acronyms

Corresponding to:

CA Criticality analysis CBM Condition-based maintenance

CM Condition monitoring CM Corrective maintenance FMECA Failure mode, effect and

criticality analysis

MTBF Mean time between failure

MTTF Mean time to failure PM Preventive maintenance FFT Fast Fourier Transform RPN Risk Priority Number SWOT Strenghts, Weaknesses,

Opportunities and Threats

TBM Time-based maintenance

KPI Key Performance Indicators WA Weibull analysis AGAN As good as new RTF Run to failure

IDE Integrated development environment

WD Weibull distribution

TFS Technical feasibility study BPFO Ball pass frequency outer race

BPFI Ball pass frequency inner race FTF Fundamental train frequency BSF Ball spin frequency

Abbreviation Corresponding to: Abbreviation Corresponding to:

𝑻, 𝒊 Time to replace the component.

𝑪𝒔𝒄𝒓𝒂𝒑, 𝒊 Cost of scrap

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𝑪𝑷𝑴,𝒊 Preventive maintenance cost 𝑪𝒎𝒂𝒕,𝒊 Material cost

𝑪𝒇,𝒊 Cost of failure 𝑪𝒍𝒐𝒈, 𝒊 Logistic cost

𝑪𝒄𝒎, 𝒊 Corrective maintenance cost 𝑪𝑪, 𝒊 Carrying cost.

𝑪𝑵𝑪𝒊 Cost of internal non-conformity 𝑨𝑰 Average value of

components 𝑪𝑷𝑹 Cost of product rework 𝑰𝒂 Alternative inventory

interest

𝑪𝑷𝑾 Cost of production waste 𝑰𝒓 Additional interest to cover

the cost associated with the risk for carrying inventory 𝑪𝑸𝑳 Cost of quality loss 𝑪𝑯 Holding cost.

𝑻𝑨𝑫 Active downtime 𝑪𝑺𝑹 Cost for storage rooms

𝑷𝑹𝑷 Reduction in product price 𝑪𝒓,𝑺𝑷 Manhours rate for

personnel handling the inventory 𝑹𝑷 Production rate 𝑪𝑬𝑬 Cost of equipment to

handle the component 𝑵𝑷𝑷

𝒉

Number of processed products by a single product process per

hour

𝑿𝒊 Fraction of the total cost

allocated to component i

𝑪𝑺𝑳 Cost of safety loss 𝑪𝑨 Administration cost.

𝑪𝑺𝑭 Cost of fines for breaches of

regulation when a component fail injures an employee

𝑪𝑻 Transportation cost

𝑪𝑪 Cost of compensation for an

injured employee

𝑪𝑻𝑺 Transportation cost from

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𝑪𝑵𝑪𝒆 Cost of external non-conformity 𝑪𝑻𝑭 Transportation cost

between the company’s own facilities 𝑪𝑬𝑳 Cost of environmental loss. 𝑪𝑺𝑨 Cost of spare parts

availability 𝑪𝑪𝑺𝑳 Cost of customer satisfaction

loss

𝑪𝒓𝒆𝒑𝒂 Cost to repair the

component

𝑪𝑭 Cost of fines for late delivery 𝑻𝒓𝒆𝒑𝒂 Time to repair the

component 𝑪𝑳𝑩 Cost of loss business to a

competitor due to late orders

𝑪𝒄𝒐𝒏𝒔𝒖𝒎 Cost of consumable to

carry out a repair action

𝛈 Scale parameter of Weibull distribution

𝑵𝒔𝒑 Number of spare parts

𝛄 Location parameter of Weibull distribution

𝑪𝒔𝒑 Cost of spare parts

t Time of reference 𝑪𝒅𝒊𝒂𝒈 Cost to diagnose the

component 𝑪(𝒕)𝒕𝒐𝒕 _{Total maintenance cost of}

component at time t

𝑪𝒓,𝑫𝑰𝑨𝑮 Manhour rate for a

diagnostic action 𝑪𝑻𝑩𝑴_{, 𝒊} _{Total time-based maintenance}

cost of component at time t

𝑴𝒉𝑫𝑰𝑨𝑮 Manhour for a diagnostic

action

[𝒕 𝑻]

Number of times that preventive maintenance (PM)

has been carried out

𝑪𝒕𝒐𝒐𝒍 Cost of tool to make the

diagnostic action

𝒛 Cumulative probability of failure of component at time t

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𝑪𝑷𝑺 Cost of planned shutdown 𝒄𝒓,𝒊𝒏𝒔𝒑 Manhour rate to carry out

the inspection 𝒊 = 𝟏, … … , 𝒏 Number of components 𝑴𝒉𝒊𝒏𝒔𝒑 Manhour of inspection

𝑪𝑭𝑿 _{Fixed maintenance cost of}

component at time t

𝑻𝑾𝑫 Waiting downtime

𝒔 Discount rate 𝒏 Number of dependent component 𝑪𝑷𝑳𝟏 Cost of production loss 𝑷𝒇,𝒊(𝒕) Failure probability of

component at time t 𝑻𝑨𝑫 Active downtime. 𝐉 = [𝒕

𝑻]

Number of times TBM has been carried out 𝑷𝑷 Mean product price 𝑪𝑭𝑿 Fixed maintenance cost of

component at time t.

𝑪𝑷𝑳𝟐 Less efficiency and penalty

cost

𝒏𝒑𝒇𝒙 Number of personnel for

fixed maintenance 𝐏 Penalty rate 𝑪𝒄𝒍𝒆𝒂𝒏 Cost per hour for cleaning

𝑴𝒉𝑺𝑬𝑻−𝑼𝑷 Set-up time (man hours) for

component at time t

𝑪𝒍𝒖𝒃 Cost per hour for lubricating

𝑿𝒄𝒓𝒆𝒘 𝒕𝒓𝒂𝒗 _{Fraction of the total set up time}

allotted to crew travelling

𝑪𝒑𝒂𝒊𝒏𝒕 Cost per hour for painting

𝑿𝒔𝒄𝒂𝒇𝒇 _{Fraction of the total set up time}

allotted to erecting a scaffolding

𝑴𝒉𝑭𝑿 Fixed maintenance time for

component at time t

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painting

𝑪𝑺𝑬𝑻 𝑼𝑷 Set-up cost 𝑪′(𝒕)𝒕𝒐𝒕 Total maintenance cost

introducing CBM 𝒏𝒑 Number of personnel in the

maintenance team

𝒏𝒑𝒑 Number of physical parameters under

monitoring 𝑪𝑺𝑬𝑻−𝑼𝑷𝒄𝒓𝒆𝒘 𝒕𝒓𝒂𝒗 Cost per hour of crew travelling

for an onsite maintenance action

𝛕𝑱 Number of times that a

predefined threshold has been overcome 𝑪_{𝑺𝑬𝑻−𝑼𝑷}𝒔𝒄𝒂𝒇𝒇 Cost per hour of erecting a

scaffolding

𝒏∗ _{Number of failure occurred}

𝑪_{𝑺𝑬𝑻−𝑼𝑷}𝒐𝒑𝒆𝒏 𝒎𝒂𝒄𝒉 Cost per hour to open the machine

𝑪𝑪𝑩𝑴,𝒎𝒐𝒏𝒊𝒕,𝒊 CBM monitoring cost

allocated to component i 𝑪𝑹𝑬𝑷𝑳 Cost to replace the component 𝑿𝒊 Fraction of the total cost

allotted to component i 𝑪𝒓,𝑨𝑫 Manhour rate of active

downtime

𝑪𝑪𝑩𝑴,𝒎𝒐𝒏𝒊𝒕 Cost of CM devices and

data acquisition systems 𝑻𝑫𝑻 Delay time. 𝑪𝑪𝑩𝑴,𝒇𝒂𝒍𝒔𝒆 𝒂𝒍,𝒊 Cost for CM systems false

alarm on component i 𝒏𝒑 Number of personnel in the

maintenance teal

𝒏𝒇𝒂,𝒊 Number of false alarm of

the CM system

𝒏𝒑𝒉 Number of physical parameter under monitoring

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1 INTRODUCTION

The high level of competition in global markets has resulted in significant changes in the way manufacturing companies operate and in their production processes. This has influenced the role of maintenance function, becoming a key factor for business success (Kutucuoglu, 2006). The definition of maintenance provided by Tabikh & Khattab helps to understand the broad extent of the subject, in fact he defines it as “The combination of all technical and associated administrative actions intended to retain an item or system in, or restore it to, a state in which it can perform its required function” (Tabikh & Khattab, 2011). This means that in order to improve the effectiveness and efficiency of their production process so as to maintain their competitiveness, manufacturing companies need to select and implement the appropriate maintenance strategy(Jonge, Teunter, & Tinga, 2017). Any strategy needs a programme to make it work and even though the automation of product processes and the demand for reliable production equipment is growing, only few manufacturing companies are committed to developing an appropriate strategic maintenance programme(Jonge et al., 2017). As Vasili points out, one of the most complex challenge that must be faced when setting up a maintenance programme is the optimization of maintenance costs and the scheduling of maintenance activities. This is regarded as being one of the most critical issue in the manufacturing industry since the failure of a system/machine during operating hours can be costly and safety critical. (Vasili, Sai Hong, Ismail, & vasili, 2011) As a matter of fact, maintenance in the manufacturing environment deals with highly technical and costly equipment which requires a specific know-how and treatment so as to keep it in a good state(Al-Turki, Ayar, Yilbas, & Sahin, 2014).

It is necessary to understand that when a failure occurs, it does not only delay the completion time of the assigned operations but it also affects the other planned operations in the plant. Consequently, the scheduled production cannot be finished on time and it will induce penalty and eventually leads to a bad

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When speaking about costs, Almgren and Andrèasson reckon that the 20% of the total plant operating budget is, on average, intended for maintenance activities, varying from a few percent in light manufacturing to very high percentages in equipment-intensive industry(Almgren & Andréasson, 2012).The reason for this high incidence is most of the times due a "non-effective scheduling of maintenance activities ”(Tabikh & Khattab, 2011).

These are only few of the reasons that led to develop and implement several maintenance strategies in manufacturing industries over the past decades. Such maintenance strategies include corrective maintenance, time-based maintenance and condition-based maintenance. (Kurfess, Billington, & Liang, 2006)CBM is increasingly regarded as being the most effective and efficient strategy in order to perform maintenance in a wide variety of industries. However, its practical application in the manufacturing industry is relatively limited (Rastegari, 2015a). In fact, CBM is not technically feasible or cost-effective to perform in any situation. Therefore, it is necessary a preliminary analysis on components to determine which ones are suitable.

As an example, rolling element bearing faults are regarded as being one of the foremost causes of breakdown in rotating machinery, which ultimately lead to costly downtime and production losses. Therefore, bearings condition monitoring has become a critical factor in rotating machinery maintenance(Abiot Dagnew, 2012). It has been found that Condition monitoring of rolling element bearings has enabled cost saving of over 50% as compared with the old traditional methods.(Natu, 2012)

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2 RESEARCH METHODOLOGY

2.1 Aim of the project

Since it is not always feasible to adopt a CBM approach for every component in the manufacturing industry, the aim of the research project is to build a methodology to guide the user on how to assess its eligibility from an organizational, technical and economical perspective.

2.2 Objectives of the project

To fulfil the aim, several objectives have been set to:

➢ Identify and investigate the different maintenance practices employed in the manufacturing industry

➢ Develop a methodology to investigate which prerequisites must the components have for CBM to be eligible.

➢ Build a maintenance cost model to give a suggestion on how to evaluate the CBM’s cost effectiveness

➢ Choose a component as a practical case study possessing the

prerequisites to be suitable for CBM and collect degradation data through sensor and Arduino to provide further guidelines by making a link with the methodology built

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2.3 Research methodology

2.3.1 Approach to the research

Figure 2-1 – Double diamond method

The double diamond method has been used to organize the research’s steps. The first quarter of the method, “Understand” has the purpose to gather a broad understanding of the subject studied and identify all the possible facets. In this step, all the major research options need to be identified.

The second quarter of the approach, “Define”, has the purpose to narrow down the research options and identify the topic that will be the main subject of the study. This is achieved establishing aim and objectives by formulating key research questions.

The third quarter of the approach, “Explore” has the purpose to collect valuable information from the literature able to help in fulfilling the identified research objectives. Information from different sources have been collected during this phase. More precisely, data have been collected and analysed from the literature using electronic databases such as Scopus, Google scholar and Research Gate to select relevant paper. Information has also been found on the Internet while

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doing general researches. The generation of ideas is also triggered by a brainstorming activity with Cranfield Through Life Engineering services members. The keywords used to develop the literature review are the following: Maintenance strategies, Statistical reliability models, Condition Monitoring techniques, sensors in maintenance, Maintenance cost model, benefits of condition-based maintenance, maintenance costs, rolling element bearings, bearings failure modes and bearings condition-monitoring.

The fourth quarter of the approach, “Create”, has the purpose to develop the framework/methodology thanks to the relevant information collected in the third phase of the research. More specifically, a methodology for selecting the components suitable for a CBM program in a manufacturing company has been developed and discussed in Chapter 4. Furthermore, a cost model able to assess the economic convenience for a manufacturing company in implementing a CBM program has been build and presented in the same chapter as part of the methodology proposed. For details, refer to Chapter 4.

Finally, a practical case has been carried out in Chapter 5 to provide further guidelines. More specifically, vibrations data from healthy and artificially-damaged bearings were collected through an accelerometer as the sensor and Arduino playing the role of data acquisition system of the accelerometer. An analysis of the signals in the frequency domain has been performed. For such was used the FFT algorithm (Fast Fourier Transform). The implementation of the algorithm has been done in Python language. The purpose was to classify the conditions of the bearings (with different sizes of defects) in different degradation stages establishing a vibration threshold beyond which the component must be replaced to prevent them from damaging not only the dependent components but the whole machine/system. This way, by referring to the standards and threshold values, analysts, while monitoring the real-time conditions of the equipment, are aware when the parameters exceed the threshold and therefore they know the conditions according to which the component has to be immediately replaced otherwise it would cause damage. All the experiment’s results are reported in

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2.3.2 Research questions

To fulfill the aim of the research and achieve the research objectives, the following research questions need to be answered:

First research question

What are the current maintenance practices to strategically optimize maintenance costs and resources in the manufacturing industry?

Manufacturing companies must have a well-defined maintenance strategy to lead companies from a reactive approach towards a proactive approach (e.g. condition based maintenance approach), to ensure a competitive position in the market. Therefore, this research question is formulated to investigate the

different maintenance strategies currently employed within the manufacturing industry.

Second research question

What are the current challenges of installing and implementing a condition monitoring system in the manufacturing industry?

Before implementing a CBM system, a thorough study on the challenges that will be encountered in performing a CBM strategy need to be carried out. This research question attempts to introduce the current challenges of implementing CBM in the manufacturing industry.

Third research question

Does condition based maintenance provide tangible benefits to the manufacturing company?

Before fully implementing a CBM strategy, a manufacturing company should evaluate whether CBM can really bring benefits to a company. Since CBM technologies are costly, it is not cost effective to apply CBM where it is not needed. This research question attempts to investigate the economical convenience in implementing a CBM program in the manufacturing industry

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Fourth research question

What prerequisites should the components have to be suitable for CBM practices in a manufacturing industry?

Since is not possible to perform a CBM strategy for all equipment and, more specifically, for every component in a manufacturing industry, a methodology to select suitable components for a CMB program is needed. This research question attempts to investigate which factors should be preventively investigated to make the components suitable for a CBM program in the manufacturing industry. Fifth research question

What factors need to be considered from an organizational and technical point of view to implement a CBM strategy?

In assessing the suitability of a CBM approach, a company should evaluate the organizational aspects of the new potential technology as well as the technical ones. This research questions attempts to investigate how manufacturing companies can effectively implement a CBM program by considering all the aspect outlined.

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2.4 Thesis outline

The first chapter of the thesis focuses on a short introduction to the area of maintenance, explaining how its role has become a key factor for business success and exploring the current maintenance practices. It continues highlighting the motivations that drive manufacturing companies to adopt a CBM program with a brief discussion of its benefits compared to traditional maintenance policies such as TBM and CM. Although considerations that not all components are suitable for CBM have been made, it ends focusing on how CBM on bearings really brings benefits.

Chapter 2 presents the research methodology that has been followed to plan the various steps of the research, adopting the Double diamond method as a guideline.

In chapter 3 all the topics briefly discussed in the introduction and in the objectives of the research are accurately described through a Literature Review, highlighting at the end the actual gap in the research.

Chapter 4 describes in detail the methodology proposed guide the user on how to assess the eligibility of a CBM programme. Furthermore, a maintenance cost model is also proposed and described in detail at the end of Chapter 4. The cost model presents a cost comparison in two scenarios: the first one considers the total maintenance costs of a component considering only the traditional maintenance policy carried out at pre-determined intervals and the second scenario evaluates the total maintenance costs with CBM and its associated costs. From a difference between the two resulting costs, it is possible to evaluate whether a CBM program can really bring benefits.

Chapter 5 focuses on a practical case with rolling element bearing as the component under study. The aim was to assess and classify the bearings conditions in different stages of deterioration by recording and analysing their vibration signals. This can help in giving some suggestions to maintenance personnel and analysts about the best time to replace the component, by comparing the real-time condition data with the historical ones taken as a

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reference. In this way, bearings will be replaced only when needed exploiting their lifetime or preventing catastrophic failures.

Based on the findings of Chapter 4 and 5, the thesis is concluded in Chapter 6, where the conclusions and some advices for future work are described.

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3 LITERATURE REVIEW

3.1 Preamble

As mentioned in the introduction, the third chapter will describe the theoretical foundations on which this project is based.

The above chart shows the macro-area of the literature review.

Figure 3-1 Areas of literature review

Literature

Review

The role of maintenance Maintenance strategies Maintenance costs and cost model Rolling element bearing CM

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3.2 The role of maintenance

Swedish standard SS-EN 13306 (2001) defines Maintenance as “the combination of all technical, administrative and managerial actions during the life cycle of an item intended to retain it in, or restore it to, a state in which it can perform the required function” (p.7)

Its role has increasingly gained more importance in all manufacturing industries due to the continuous growth of capital inventory, the requirements for the functioning of systems and the outsourcing of maintenance activities.(Vasili et al., 2011)

As far as the manufacturing industry, maintenance is very critical compared to transportation ,construction and service business because of the high pressure in reducing costs, increasing value of the assets and improving the quality of the outputs .(Al-Turki et al., 2014)

It is also related to highly technical equipment which requires particular know-how with a limited range of technology providers. Therefore, it needs to be scheduled with much greater accuracy than any other business environment(Al-Turki et al., 2014). Even if its role is critical to guarantee the reliability and availability of production equipment and optimize maintenance resources, (Komonen, 2002) many industries had a negative success in implementing an effective maintenance strategy due to absent of clear program that should be followed. In fact, companies should not be only interested in keeping the equipment and machinery in a good state further, maintenance activities should be properly scheduled and totally effective so as to avoid unnecessary maintenance action which could affect the equipment availability. (Tabikh & Khattab, 2011)

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3.3 Maintenance strategies

Maintenance strategies can be broadly classified into: • Corrective maintenance (CM)

• Preventive maintenance (PM)

• Condition-based maintenance (CBM) or Predictive maintenance(PM)

Figure 3-2 - Maintenance strategies(Stenholm & Andersson, 2014)

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3.3.1 Corrective maintenance (CM)

Swedish standard defines CM as “The maintenance carried out after fault recognition which is intended to put an item into a state in which it can perform a required function” (SS-EN 13306, 2001, p.15). Also known as “Run to failure (RTF) “, CM is regarded as being the oldest type of maintenance. According to this strategy, maintenance activities are performed after a fault has been recognised(Garg, 2013) and at unpredictable intervals since the equipment’s failure is not determinable a priori .(Al-Turki et al., 2014) Garg divided CM into two categories:

1) Emergency maintenance, performed as fast as possible in order to bring a failed machine or facility to a safe and operationally efficient condition; 2) Breakdown maintenance, performed after the occurrence of an advanced

considered failure for which advanced provision has been made in the form of repair method, spares, materials, labour and equipment.(Garg, 2013)

Even though according to Ahmad and Kamaruddin the strategy can lead to heavy maintenance cost and production losses due to sudden failure (Ahmad & Kamaruddin, 2012), Al Turki sustains that in some contexts CM can be the appropriate strategy to implement. For example CM is recommended for non-critical components where the consequences of failure are slight or do not influence the overall function of the system/machine, no safety risks are present and capital costs are small (Al-Turki et al., 2014)

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1.1.1.1 Advantages and disadvantages of CM

Hansen et Al. summarised the pros and cons of CM in the following table:

Advantages Disadvantages

• Lower start-up cost • Increased cost due to unplanned downtime of equipment

• Limited personnel requirement • Increased labour cost, especially if overtime is needed

• Reduced maintenance costs • Equipment not maximized • Potentially increased margin • Unpredictability

• Possible secondary equipment or process damage from

• Inefficient use of staff resources

Table 3-1 Advantages and disadvantages of CM (Hansenetal.,2015) and (Sullivan(a), Pugh, Melendez, & Hun, 2010)

3.3.2 Preventive maintenance (PM)

Swedish standard SS-EN 13306 (2001) defines PM as follows: “Maintenance carried out in accordance with established intervals of time or number of units of use such as scheduled maintenance but without previous item condition investigation” (p.15). The branch of PM carried out at predetermined intervals of time is Time-based maintenance (TBM)(Garg, 2013).More commonly referred to as periodic maintenance, TBM is defined as “the set of maintenance actions performed according to fixed intervals which are intended to repair or replace the components of a system before the occurrence of a failure to assure that the system can continue operating in the same conditions”.(Murthy & Jack, 2014) TBM is intended to minimize the probability of failure or the degradation of components/systems with the purpose to maintain machines and facilities in such a condition that breakdowns and emergency repairs are minimised.(Garg, 2013) Obviously, costs play an important role in the scheduling this type of maintenance but in many contexts it is economically more convenient to replace parts or

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components that have not failed at predetermined intervals rather than waiting for a system to fail.(Murthy & Jack, 2014)

As for CM, either for TBM there are more appropriate contexts in which the strategy should be applied. In fact, according to Garg this maintenance strategy is appropriate for equipment where the occurrence of failure may cause critical breakdown of the machine and production losses. (Garg, 2013). In TBM, maintenance decision-making (such as preventive replacement/repair times intervals) are performed in accordance with the past failure data analysis assuming that the failure behaviour of a component is predictable. The assumption is based on hazard or failure rate trends, known as Bathtub curves.(Ahmad & Kamaruddin, 2012)

3.3.2.1 Bathtub curves and Weibull distribution

The first step in performing a TBM program is to model and analyse past data on component faults to statistically investigate its failure characteristic. Once failure data has been collected, they will be analysed through statistical/reliability modelling to extrapolate the failure parameters such as mean time to failure (MTBF), mean time between failure (MTBF) and the failure rate trend based on Bathtub curve. (Ahmad & Kamaruddin, 2012). The Bathtub graph represent the relationship between the failure rate and the equipment operating life, as shown below.

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maintainability analysis, the components experience decreasing failure rate early in their life cycle(burn-in), followed by a constant one (useful life) and finally increasing failure rate at the end of their life time (wear-out) (Ahmad & Kamaruddin, 2012). To optimize maintenance costs and to avoid loss of production, the most suitable failure distribution of the component group must be identified before carrying out any maintenance strategy. For example, preventive replacement strategy is effective only if the failure rate a component presents an increasing failure rate which means that a failure is predictable. On the opposite side, if the preventive replacement strategy is undertaken when the failure rate is constant or decreasing, then the replacement and downtime cost will grow significantly(Ahmad, Kamaruddin, Mokthar, & Putra Almanar, 2006).

To guarantee the success of a manufacturing process is critical to analyse and understand the failure risk and the reliability of a critical component, part or subsystem. Evaluating the probability of failure of a component at different stages of his lifecycle can help in optimizing decision making in life cycle management and manufacturing processes. A simple approach to predict components failure consists on statistical reliability models of failure occurred in the past. (Zhai, Lu, Liu, Li, & Vachtsevanos, 2013) Different types of statistical reliability models such as Weibull distribution(WD) model, Normal distribution model, and lognormal distribution model are used to analyse the failure data and predict the failure characteristics of equipment(Abiot Dagnew, 2012).Compared to the other models, Weibull analysis presents two main advantages:

1) The main advantage consists in the capability of providing accurate failure data analysis and failure forecast with small samples of data. Therefore, is possible to have the solutions in an early indication of a problem.

2)The second one consists in the capability of Weibull Analysis to provide a useful graphical plot of failure data which are very important for engineering analysis and maintenance decision making. (H.Bainbridge et al., 2011)

An example of a Weibull plot is provided below. As it will be mentioned later, the plot is particularly useful in maintenance decision-making.

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Figure 3-4 - Weibull plot: Probability of failure ( y axis), Time in hours ( x axis) (H.Bainbridge et al., 2011)

Where the X axis represents the time to failure and may be hours, cycles, starts, or landings. The Y axis represents the Probability of failure or Unreliability. (H.Bainbridge et al., 2011) which, for a three parameter Weibull distribution, it can be expressed according to the following formula:

𝐹(𝑡) = 1 − 𝑒

−(

𝑡−γ

𝜂 )

𝛽

(3-1)

Where β, η, 𝛾 are the shape, scale and location parameters respectively. In case of the 2-parameter Weibull distribution the location parameter is set to 0.(Kumar, 2008). The formula describes the percentage of component that will fail at any age. (H.Bainbridge et al., 2011). The three-parameter are described as follows: β = shape parameter

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➢ 𝛽 < 1 𝑟𝑒𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑎 𝑑𝑒𝑐𝑟𝑒𝑎𝑠𝑖𝑛𝑔 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑟𝑎𝑡𝑒 ➢ 𝛽 = 1𝑟𝑒𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑎 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑟𝑎𝑡𝑒 ➢ 𝛽 > 1 𝑟𝑒𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑎𝑛 𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑖𝑛𝑔 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑟𝑎𝑡𝑒 • η= scale parameter

Is defined as being the characteristic life or, in other words, id the typical time to failure in Weibull analysis. It is defined as the age at which 63.2% of the units will have failed. It is linked to the mean time to failure (MTTF). (H.Bainbridge et al., 2011)

1) 𝛾= location parameter

It provides an estimate of the earliest time at which a failure may be observed. A negative γ may indicate that failures have occurred prior to the beginning of the data collection period for the analysis. For example, failures might have occurred during production, in storage, in transit, during checkout prior to the start of a mission, or prior to actual use. Usually the location parameter is set to zero.

When γ = 0 the Weibull distribution starts at time t=0, or at the origin (Tabikh & Khattab, 2011)

The three Weibull distribution parameters can be estimated by analysing the past failure behaviour of the component group. (H.Bainbridge et al., 2011)

3.3.2.2 Maintenance decision-making

As briefly discussed, the Weibull plot is particularly useful in maintenance planning. More specifically, the different 𝛽 values give to the analysts some suggestions about whether or not scheduled inspections and overhauls are necessary or not (H.Bainbridge et al., 2011)

In particular: • 𝛽 < 1

The shape parameter less than one, or infant failure, shows that there may be a quality problem present among maintenance, operating or spare parts acquisition programs. For this type of failure is not advisable a TBM

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program until the root causes of infant failure are not discovered. TBM would not be suitable would not be suitable because a new component would statistically be more likely to fail than one currently in service.

• 𝛽 = 1

The shape parameter approximately equal to one represents random failure. Most of the times is due to external factors. High failure rate random failures are characterized by a shorter than expected, or shorter than desired characteristic life (η). For this type of failure, a TBM program would not be suitable because a new item would be just likely to fail as one currently in service, given that it has survived up to that point. Since the failure are random TBM is not able to improve the reliability of the system but, on the contrary, would have a negative impact on the availability and the maintenance costs.

• 𝛽 > 1

Components having the shape parameter more than one, or wear-out failure, are recommended for TBM or CBM strategy since the failure is predictable. It would cause a negative impact on the organization if components are not lasting as long as expected or desired.

In this case, a new or restored item would be less likely to fail than one currently in service. Therefore, TBM OR CBM would be able to help in reducing the probability of failure and maintenance costs.(Maintenance Technology) and (H.Bainbridge et al., 2011)

3.3.2.3 Best time to replace a component

In increasing the efficiency and the productivity of industries, maintenance activities play a fundamental role. According to Abouel-Seud and Khalil, the best way to realize a strategic maintenance is to determine the optimal preventive time to replace a component (Abouel-seoud & Khalil, 2013)

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time to replace(T) need to be calculated. The time T will be the compromise between replace too early and replace too late. If the failure time follows the Weibull distribution, the optimum replacement time can be determined by minimizing the cost function C(T):

𝑚𝑖𝑛: 𝐶(𝑇) =𝐶𝑓𝐹(𝑇) + 𝐶𝑝𝑅(𝑇)

∫ 𝑅(𝑇)𝑑𝑡₀𝑇 (3-2)

Where, 𝐶(𝑇) is the cost function at time T; 𝐶_𝑓 is the cost of failure; 𝐹(𝑇) is the cumulative distribution function; 𝐶_𝑝 is the cost of preventive replacement and 𝑅(𝑇) is the reliability function. To be able to find the optimal interval, the degradation behaviour of the component group has to be estimated and analysed through statistical and reliability models.(Abouel-seoud & Khalil, 2013) Tabikh and Khattab proposed a different method to estimate the best time to replace a component: instead of minimizing the previous equation every time by usual numerical routine, they developed an equation that can be easily applied under the following assumptions:

• Time to failure follows Weibull distribution

• Preventive maintenance is performed at time =

𝑇

with cost equal to 𝐶_𝑃𝑀 • If a failure occurs before time =

𝑇

cost of failure is incurred.

• The last and the most important condition is when the component is being maintained it is return into its’ initial state “As good as new”

𝑇 = (𝑚 ∗ η ) + γ

(3-3)

Where, 𝑚 = 𝐶𝑓

𝐶𝑃𝑀 ; 𝐶𝑃𝑀 is the cost of preventive maintenance; 𝐶𝑓 is the cost of

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3.3.2.4 Advantages and disadvantages of TBM

Hansen et al summarized the advantages and disadvantages of TBM in the below table.

• Cost effective in many capital-intensive processes

• Catastrophic failure still a risk

• Increase components life cycle • Risk of damage when conducting unneeded maintenance

• Reduced equipment or process failure

• Saving not readily visible without a base line

• Estimated 12% to 18% cost savings over corrective maintenance program

• Labour intensive

• Saved energy cost resulting from equipment running from pick efficiency

• Flexibility can allow for adjustment of schedule to accommodate other work

Table 3-2 - Advantages and disadvantages of TBM (Hansen et al., 2015) and (Sullivan(a) et al., 2010)

Another point of weakness is the fact that TBM may lead to high labour cost and expensive parts replacements while components are replaced without exploiting their operating useful life.(Hansen et al., 2015)

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3.3.3 Condition-based maintenance(CBM)

With the rapid evolution of modern technology, the complexity of the products has increased in terms of higher levels of quality and reliability required. This has led to TBM costs becoming ever higher(Jardine, Lin, & Banjevic, 2006). Therefore, more effective maintenance strategies such as CBM have being investigated by intellectuals and implemented by practitioners. (Jardine et al., 2006) Swedish standard SS-EN 13306 (2001) defines CBM as “The preventive maintenance based on performance and/or parameter monitoring and the subsequent actions” (p.15). CBM is based on real-time data collected through sensors to monitor the health condition of the equipment. The basic definition of real-time data is that “it is data that is not kept or stored, but is passed along to the end user as quickly as it is gathered” (Techopedia). However, real-time data does not imply sending data within a certain number of microseconds. It just implies that the data is not designed to be kept back from its eventual use after it is gathered. Real time data application has been a huge success in applications like traffic GPS system showing to the drivers what is going on around them (Techopedia). Nowadays, CBM, also known as predictive maintenance(PM), is the most popular maintenance policies reported in literature. It has been introduced in 1975 in order to optimize PM decision making.(Ahmad & Kamaruddin, 2012). Its aim is to predict failures at an earlier stage to allow maintenance activities to be planned and take place only when needed or just before failure so as to minimize unplanned shutdown of systems and maintenance costs.(Hansen et al., 2015). CBM does not follow any predetermined intervals and schedule(Jonge et al., 2017) but it is rather performed according to the real-time condition monitoring’s response from the equipment conditions. Condition monitoring (CM) is defined by the Swedish standard as an “activity performed either manually or automatically, intended to observe the actual state of an item” (SS-EN 13306, 2001, p.16) which can be performed either periodically or continuously (Hansen et al., 2015). Obviously, by choosing the periodic monitoring, the risk of not detecting some failures may increase. On the other hand, the second option is more costly, Therefore, a trade-off should be met.(Reza Golmakani & Pouresmaeeli, 2014).

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Jardine et al. divides a CBM program in three key phases:

1. Data acquisition step (information collecting), to obtain relevant data relevant of the health of the equipment

2. Data processing step (information handling), to handle and analyse the data or signals collected in step 1 for better understand and interpret of the data. 3. Maintenance decision-making step (decision-making), to recommend efficient maintenance policies

Figure 3-5 Steps of a CBM program (Jardine et al., 2006)

The CBM approach has been developed since the first two maintenance strategies (CM and TBM), though still in use, are not considered entirely effective. In fact, TBM technique waste useful operating life when components are replaced even though they can still be exploited and, more importantly, provide no prevention against early failures, as well as CM can lead to machine breakdown and production loss when the components are replaced after the occurrence of a failure(Kurfess et al., 2006) CBM optimizes the trade-off between maintenance costs (weakness of CM) and performance costs (weakness of TBM).(Verma & Subramanian, 2012). Even though CBM has been reported in the literature as the most effective strategy, there are some cases in which is not regarded as being

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instantaneously because of its difficulty in being detected. On the other hand, when a mechanical failure occurs gradually and a there is a CM parameter able to detect the deteriorating process, CBM strategy is effective. (Gopalakrishnan, 2004).

3.3.3.1 Sensors and Condition monitoring (CM)

CM systems and CM techniques have gained a lot of success in many industries, e.g. paper mills, refineries, power stations and recently in the manufacturing industry. One of their main contributions is evident is a less number of failures thanks to the early detection of failure. This may allow components to be replaced before the occurrence of a fault.(Al-Najjar & Wang, 2009) Hansen et al defined CM as “the process of monitoring the performance of a parameter (vibration, temperature, oil debris, etc.) of equipment, in order to identify a significant change, which is indicative of a developing fault (Hansen et al., 2015). Another definition of CM is given by Elwany as “the process of collecting real-time sensor-based data/information in order to evaluate the health of a component during its operation”.(Elwany, 2009) With the advent of sensor technology, the interest in prognostic health management has increased a lot. Firstly, because the development of an optimal maintenance strategy is crucial in order to improve the reliability of the equipment, secondly because there is a need to prevent the occurrence of unexpected failure and consequently to reduce maintenance costs. (Kaiser & Gebraeel, 2009) Many hi-tech manufacturing industries are employing technology-advanced sensors in order to collect and analyse real time data to reason about the health state of the equipment. Historical data together with the real-time condition allow to schedule a more effective maintenance strategy, since the length of the equipment downtime is a crucial factor to minimize(Chen & Wu, 2007) With the recent developments on sensors, in fact, chemical and physical non-destructive testing, and sophisticated measurement technologies have made possible real-time condition monitoring of the equipment’s parameters through many types of on-line data (e.g. vibration, temperature, pressure, voltage, corrosion, fluid, etc.)(Chen & Wu, 2007)

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analysis, thermodynamic, tribology, lubrication oil analysis and visual inspection (Hansen et al., 2015). An exhaustive review of CM techniques as far as rolling element bearing is described in section 3.4.2

3.3.3.2 Advantages and disadvantages of CBM

Hansen et Al summarised the advantages and disadvantages of CBM in the below table.

• Minimize spare parts costs • Monitoring equipment costs • Minimize unscheduled downtime • Operational costs (running the

program)

• Reduce labour cost • Skilled personnel needed

• Maximize machine productivity • Strong management commitment needed

• Safely extend overhaul intervals • Increased machine life

• Improve product quality • Reduce product cost • Enhance product safety

Table 3-3 - Advantages and disadvantages of CBM (Hansen et al., 2015) and (Sullivan(a) et al., 2010)

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3.3.3.3 Challenges of CBM

While introducing a CBM programme, an organization has to face several challenges. Some of these can be summarized as follow:

• CM instrumentation(e.g. sensors) may give “false alarms” due to misunderstandings in interpreting deviations from standard conditions caused by changes in operational/environmental parameters. (Elwany, 2009) Therefore, it is very important to check the sensors whether they are working perfectly or not every time

• Highly skilled personnel is required to implement a CM system and more equipment is needed (Hansen et al., 2015)

• High implementation costs since there is the need of different types instrumentations and technologies for condition monitoring data collection and processing.(“IAEA-TECDOC-1551,” 2007)

• Training is required: a high level of technologies is usually employed when CM is applied to the system. Training need to be given to maintenance personnel to make sure they are confident with the technologies and software required for data collection and processing. (“IAEA-TECDOC-1551,” 2007)

That is not to say that without implementing a CBM system would be more convenient performing corrective maintenance in terms of cost saving. It is always advised to assess the value and cost efficiency before introducing a CBM system.(Hansen et al., 2015)

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3.4 Maintenance costs and cost models

This section introduces a brief discussion on the impact of maintenance costs on companies’ performance as well as the current practices in developing maintenance optimization models.

3.4.1 Maintenance costs

The estimation of maintenance cost has become a crucial factor since it has a direct effect on the enterprise’s economic performance. Overestimation or underestimation of maintenance cost could be reflected negatively on the business performance, i.e., overestimation distorts the organization’ brand in the sector and consequently loses a competitive edge, while underestimation will subject the organization to financial losses. By performing maintenance actions frequently the system reliability and availability increase but also the maintenance cost increase.(Marseguerra & Zio, 2000). According to Elwany, maintenance costs can represent up to 15-60% the cost of manufactured products in manufacturing and production plant. (Elwany, 2009). According to Almgren and Andreasson, the 20% of the total plant operating budget is, on average, intended for maintenance activities, varying from a few percent in light manufacturing to very high percentages in equipment-intensive industry. The reason for this high incidence is most of the times due a non-effective scheduling of maintenance activities. For example, an early replacement of a component does not take full advantage of its use/lifetime and results in lost opportunity costs. On the opposite side, a delayed replacement increases the risk of the occurrence of catastrophic failures and consequently corrective maintenance cost arises. Murthy and Jacks classified maintenance costs into two categories:

• Direct cost: cost of manpower, cost of material and spare parts, cost of tool and equipment needed for carrying out maintenance activities, overhead costs etc.

• Indirect cost: they depend on the nature of the business. In the case of a manufacturing company, some of those costs might be the following:

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➢ Equipment related: excessive spare parts inventory, unnecessary equipment redundancy, excessive energy consumption, accelerated wear because of poor maintenance

➢ Production related: Rework, delays in fulfilling orders, excessive scrap and material losses, idle operator due to breakdowns

➢ Product related: Reliability issues, quality issues

➢ Customer related: Customer dissatisfaction, negative word-of-mouth advertising (Murthy & Jack, 2014)

Companies should not be only interested in keeping the equipment in a good state further, maintenance activities should be properly scheduled and totally effective so as to avoid unnecessary maintenance activities which could affect the equipment availability and result in a costly waste of resources. (Tabikh & Khattab, 2011)

3.4.2 Maintenance cost models

The optimization of maintenance activities is a very critical problem especially in the manufacturing industry since the failure of the system during its function can have several negatives consequences. For example, when a failure occurs in a system/machine, not only may cause a delay in the execution of the scheduled operations but it can also affect the other scheduled operations in a plant. It may also have a negative impact on the organization’s image.(Vasili et al., 2011). Therefore, many maintenance decision models has been developed(Elwany, 2009). Mathematical optimization models are widely used in the maintenance environment in order to:

-optimize maintenance costs

-reduce the cost of failure (Hussain, 2016)

According to Komonen, the variables oftenlly used in a theoretical cost model are: • Internal objective variables: (Downtime costs + maintenance costs)/

production equipment (production equipment=its replacement value) • Exogenous variable: The amount of production equipment (RV), the

downtime costs of production, utilization rate of production equipment, technology factor

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• Action variables: Preventive maintenance Subcontracting (Komonen, 2002)

According to Jonge et Al. the cost parameters included in a maintenance cost model are: the preventive replacement cost (Cp), corrective replacement cost (Cc), unit downtime cost (Cd), and inspection cost (Ci)(Jonge et al., 2017). Kumar developed a model to estimate the optimum inspection frequency required at the minimum maintenance cost based on the technical condition of the component. (Kumar, 2008)

Almgren and Andreasson presented a mathematical model with the purpose to to find the optimal maintenance schedules for systems, in which components are assigned maximum replacement intervals.(Almgren & Andréasson, 2012) Maintenance optimization models lead to various decision-making result. First of all, maintenance policies can be assessed and compared with respect to cost-effectiveness and reliability characteristics. Secondly, models can help in finding the optimum time to inspect or maintain a component. Finally, models can also be of help in determining effective and efficient schedules and plans, taking all kind of constraints into account.(Dekker, 1996)

According to Sinkkonen, the practical application of the cost models should be improved by combining the qualitative and the quantitative aspect in maintenance modelling, possibly in cooperation with case companies.(Sinkkonen, Marttonen, Tynninen, & Kärri, 2013)

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3.5 Rolling element bearing

This paragraph of the literature review has been included in order to prepare the reader to the practical case presented in chapter 5. In fact, it explains all the reasons why bearings are an ideal component for CBM, investigating the failure modes that affect rolling element bearing so as to further identify the most appropriate CM techniques.

3.5.1 Preface

Bearings are highly engineered, precision-made components that enable machinery to move at extremely high speeds and carry remarkable loads with ease and efficiency” http://www.americanbearings.org. They can be classified into two main categories:

• Ball bearing

• Roller bearing (Cylindrical, needle-shaped, or tapered) In the below figure a representation of a ball bearing is presented.

Figure 3-6- Ball bearing geometry (Ben Ali, Chebel-Morello, Saidi, Malinowski, & Fnaiech, 2015)

Depending on the type of bearing, each one has his specific field application. Bearings differ for their geometry, design and dimensions. These characteristics determine the load direction, the amount of load that the bearing can withstand, the maximum rotational speed and the maximum tolerable temperature.

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(Gebraeel, 2003). The life of a rolling bearing is defined as “the number of revolutions (or the number of operating hours at a given constant speed) which the bearing is capable of enduring before the first sign of fatigue occurs on one of its rings or rolling elements (flaking, spalling) (Upadhyay, Kumaraswamidhas, & Azam, 2013)

3.5.2 Bearings CM and CM techniques

Rolling element bearing are regarded as being one of the most critical components in rotating machinery. The failure of a bearing is one of the principal factor that lead to high machine downtime and production losses. As a matter of fact, approximately 45% of failures in rotating machinery are caused by bearings faults.(Gebraeel, 2003) Traditional maintenance strategies such as preventive maintenance based on predetermined time intervals have been employed to schedule bearings maintenance. These methods, though still in practise, are not effective all the times since they can lead to wastage of bearing operating useful life when they are replaced prematurely and, more importantly, are not effective against early failures.(Kurfess et al., 2006) Bearing CM plays an important role to assess the health of machinery and can provide prognostic knowledge and information about preventing failures from occur. Even small defect in rolling element bearing might result in dangerous failure of machinery and, if a failure occurs, not only the machine, but also the assembly line stops and the deriving costs may be extremely high. Therefore, it is extremely important detect to defects of the bearing before they cause upcoming damage and expensive downtime (Kumbhar & Chhapkhane, 2014) .Many bearings CM techniques exist, such as bearing temperature monitoring, testing of oil debris (used in bearing lubrication), monitoring overall vibration severity and/or vibration signatures, and monitoring acoustic emissions. The bearings CM techniques are described below:

Vibration analysis

"Development of a methodology to assess the eligibility of a CBM program"

D

I

’E

S

,

T

C

Development of a methodology to assess the

eligibility of a CBM programme

SOMMARIO

ABSTRACT

.

Keywords:

ACKNOWLEDGEMENTS

TABLE OF CONTENTS

LIST OF FIGURES

LIST OF TABLES

LIST OF EQUATIONS

LIST OF ACRONYMS ABBREVIATIONS

1

INTRODUCTION

2

RESEARCH METHODOLOGY

2.1 Aim of the project

2.2 Objectives of the project

2.3 Research methodology

2.4 Thesis outline

3

LITERATURE REVIEW

3.1 Preamble

Literature

Review

3.2 The role of maintenance

3.3 Maintenance strategies

𝐹(𝑡) = 1 − 𝑒

𝑇

𝑇

𝑇 = (𝑚 ∗ η ) + γ

3.4 Maintenance costs and cost models

3.5 Rolling element bearing