07. gestión de fallas críticas con enfoque probabilístico en flota pesada - adolfo huamán diaz

1

Management of critical failures with probabilistic approach

2

1. PAS 55.

2. UPTIME Pyramid of Excellence & PAS - 55.

3. Strategic Management.

4. Tactic Management.

5. Maintenance Cost.

6. Critical Failures (Bad Actor).

7. Probabilistic Approach.

8. Models Development.

9. Benefits and Goals.

Organizational Strategic Goals

Corporate Organization Management

Manage Asset Portfolio

Manage Assets System

Manage

Assets Create / acquire

Utilize Maintain Renew

/Disposee

Optimize Life Cycle Activities

Sustained Performance, Cost and Risk Optimization

CAPEX optimization and sustainability planning

Layered Integration

Life Cycle Optimization

Sustained Value

Value Creation

Business Criticality

Continuous Improvement

Pragmatic

Inclusive Whole Cycle Optimized Risk - Based Data - supported Continuous

Improvement Pragmatic

Human

Assets

Info

Assets

Intangible

Assets

Financial Assets

Physical Assets

Vital Context:

Business, Objectives, Policies, Performance

requirements, Risk Assessment

Important Interface:

Condition, Asset Health, Performance, Activities, Costs & Opportunities


Reputation, Social Responsibility, Constraints,

Social Impact


Life Cycle Cost, Capital Investments, Operating

Costs


Motivation, Communication, Roles & Responsibilities, Knowledge,

Experience , Leadership, Teamwork

Human

Reliability

Maintainability Equipment's

Equipment Reliability

Process

Reliability

Asset Reliability

Vital Context:

Business, Objectives, Policies, Performance

requirements, Risk Management


Internal Reliability, Planning & Scheduling Effectiveness, Tactic

Management, Background.


Maintenance Strategies, Maintenance Optimized; Maximize

MTBF, MTTF, MTTR, UPTIME

Important Interface: Knowledge, Understanding, Lean Process, Performance

measurements & Know How

Important Interface: Active participation of people,

Positive influencer, Continuous Improvement, Do

How

Inclusive Whole Cycle Optimized Risk - Based Data - supported Continuous

Improvement Pragmatic

7 7

Adolfo Hitler Huaman Diaz Physical Asset Optimization

MAINTENANCE ADMINISTRATION, Business Systems, IT HH RR.

FRAME WORK

PRODUCTION Operations FINANCE, Capital Management

Information Technology

Purchasing

Training Management

Capital Effectiveness, RONA

Production Rate

Spare Parts Management

Systems & Operating Improvements

ASSET OPTIMIZATION

Safety, Health, Environment, Risk Management and Control

Production Effectiveness

Availability

Reliability

Quality

Production Planning

Process Control O&M Cost

Optimization

Maintenance Management

DRIVERS BUSINESS EFFECTIVENESS: ROCE, RONA, EBIT

Value = Quality x Service

Cost x Time x Risk

Q $

8 8

OPEX CAPEX

Development

Costs

Investment

Costs Operating Costs

CMC + IOP + EI

Cost of Low Reliability = Risk

Operating Costs + Planned

Maintenance Research

Design

Acquisition

Building, Installation & Commissioning

At Working (Years) Today

Disposal

Risk

f(t)

Cost

9 9 9

9

C-MORE, Canad

STRATEGY - > 60 days

EM - Strategy

Equipment Health

Strategic Planning &

Budgeting

Reliability Engineering

Component Life

Cycle Cost Mgmt

Lean Maintenance

Efficiency

Continuous

Improvement

PLANNING - < 60 days EXECUTION

Functional Failures

Mgmt

Condition Monitoring

Prognostic Analysis

Application & Operation

Mine Planning

Mine Operations

Preventive Maintenance

- PM

Programmed Component

Replacement - PCR

Backlogs Mgmt

Preventive Maintenance

Quality PM

Programmed Component

Replacement - PCR

Backlogs Mgmt

Breakdown

Diagnostics

Execution

Asset Health Mgmt

Daily Tactics

Data Capture

Downtime Top Ten

Maintenance Efficiency

OEM Warranty Mgmt

Work-Order

Administration

Risk Assessment

FACILITIES MGMT Health Inspections

PERFORMANCE ANALYSIS

Facilities

Maintenance

SHER Policies

Facility Projects

COST ACCOUNTING CAPEX OPEX

Risk

Assessment

Financial

Health

Availability Utilization Reliability CPH

Work-Order Mgmt Non

Destructing

Testing

FMEA

SFMEA

Equipment Health

Reliability Engineering

Component Life Cycle

Cost Mgmt

Lean Maintenance

Efficiency

Continuous

Improvement

Functional Failures

Mgmt

Condition Monitoring

Prognostic Analysis

The first job of your PdM Program: Identifying how your equipment can fail Selecting the right PdM strategies and

technologies to apply to the Physical Assets Determining the amount of PdM coverage for your Fleet, Equipment, System, Sub-system, etc.

Ranking the criticality of each item of equipment Building databases for each PdM Tech

improvement Determining PdM staffing levels

Non

Destructing

Testing

Reliability & PdM Process

SFMEA

DTA

AHR

11

2. UPTIME Pyramid of Excellence.-

Asset

Management

Basic Care

Strategy

People

Performance Management

Materials Management

Work Management

Support System Management

Leadership

Essentials

Choosing Excellence

The Uptime Pyramid of Excellence (Campbell & Picknell).

People

Process

Tech

12

3.- Tactic Management (Cont.).-

Coordinate with Operations (Business Focus).

Develop the maintenance strategy for the critical equipment or Bad Actor. Based on RCM analysis & Tactic Management.

Define a "Interim Corrective Action"

Evaluate the feasibility according to the current strategy (spares, people, planning window, downtime impact, risk).

Develop and execute the "Action Plan ASAP.

Align with the Core Business (Cost, Risk & Benefits).

Implement a continuous improvement process.

People

Process Tech

R

13

2.- Inspection Decisions: Optimizing CBM

P F curve customized to mobile equipment (CBM)

Book Ref: DISPATCH

Dispatch to Maintenance. Truck HT110 please check TPS for low power fault

Wireless

Download

Real Time VIMS

Event Monitoring

Real Time

Diagnostics

14

5.- Maintenance Costs.-

" Sobre el 60% del Costo de Mantenimiento durante el tiempo de Vida de un equipo, son causados por Defectos evitables durante el Diseo, Adquisicin,

Instalacin, Operacin y Mantenimiento".

7%

5%

31% 32%

8%

17%

Division of Maintenance Costs by Origin

Management

Construction

Non Preventable

Operations

Maintenance

Design & Engineering

15

5.- Maintenance Costs (real context).-

Cost Opportunities to mobile equipment

16

6.- Critical Failures (Bad Actor).-

1. Hidden Failure. 2. Low Detection Level (Low sensitivity to change). 3. Randomly. 4. Catastrophic Consequences (Cost, Downtime, Productivity).

17

3.- Strategic Management.-

C-MORE, Canada

Component

Replacement

Decisions

Inspection Decisions

Capital Equipment

Replacement

Decisions

Maintenance

Resource Requirements

Maintenance Management System (CMMS, EAM, ERP)

Optimizing Equipment Maintenance and Replacement Decisions Optimization

18

3.- Tactic Management (Cont.).-

C-MORE, Canada

Maintenance Management System (CMMS, EAM, ERP)

Optimizing Equipment Maintenance and Replacement Decisions Optimization

R

ep

lace

me

nt

Co

mp

on

en

t Best Preventive:

DPD.

Replace Only Failure.

Constant Interval.

Age - Based

Spare Parts Provisioning

Repairable Systems. In

spe

ctio

n D

eci

sio

ns

Inspection frecuency.

Profit Maximization

Availability Maximization.

Inspection Intervals.

FFIs.

Condition - Based Maintenance.

Blended Health, Monitoring & Age Replacement.

C

apit

al R

ep

lace

me

nt

Economic Life.

Constant Annual Utilization.

Varying Annual Utilization.

Technological Improvement.

Repair vs Replace

Re

sou

rce

's R

eq

uir

em

en

t Worshops Machines.

Right Sizing Equipment.

Lease / Buy

Probability &

Statistics

Stochastic Processes (CBM Optimization)

Time Value of Money

Queing Theory

Simulation

19

6.- Optimizing Equipment Maintenance: Replacement Equipment.-

Reactive

Fix it after it Breaks: Overtime Heroes

Preventive (PM)

Maintain before it Breaks

Pdm / Condition

Based (CBM)

Identify and correct specific problems, before something Breaks

Proactive (PROACT)

Eliminate problems, eliminate source of Breakage

Reliability Driven

Identify and eliminate causes of failure; minimize the need for Maintenance

$

e

"Para alcanzar una mxima efectividad y un costo ptimo las

Organizaciones deben esforzarse para ir hacia el enfoque Proactivo manejado por confiabilidad;

rpidamente como sea posible"

20

7.- Reliability Modeling, Prediction, Lifetime Analysis Probability Approach.-

PDF

t

Key Variable. Studying variation. Continuous. Measurable. Accuracy. Sensitivity on time.

uom

MTTF

x

f(t)

y(x) y=f(x)

h(t)

Reactive Focus

Proactive Focus

Cost

Risk

Benefits

21

6.- Reliability Analysis (PDF).-

Findings: The MTTF to Con Rod Bearings Fail" is less than the Business

Objective (PCR). High probability of failures.

0.00014

0.00012

0.00010

0.00008

0.00006

0.00004

0.00002

0.00000

X = Bearing Hours

De

nsit

y

= 5,647

36.8%

0

Business Objective: 16,000MTTF = 5,030

Distribution PlotWeibull, Shape=1.7, Scale=5648, Thresh=0

= 1.7 (Wear out)

2010

63.2%

PCR : Programed Component Replacement

Software Ref:

22

6.- Risk Management: Reliability Analysis (four major components of reliability).-

24000180001200060000

0.00010

0.00005

0.00000

Bearings Hours

PD

F

100001000100

90

50

10

1

Bearings Hours

Pe

rce

nt

24000180001200060000

100

50

0

Bearings Hours

Pe

rce

nt

24000180001200060000

0.00045

0.00030

0.00015

0.00000

Bearings Hours

Ra

te

Erosion Cavitation

Layer separation & Fatigue

Failure Mode

1.94045 9848.63 0.964 29 0

1.28012 7686.00 0.975 45 0

Shape Scale Corr F C

Table of Statistics

Probability Density Function

Surv iv al Function Hazard Function

Distribution Overview Plot for Bearings Hours_20110518LSXY Estimates-Complete Data

Weibull

Bearings

Hours

Erosion

Cavitation (r%)

236 99.9

1,557 97.2

2,879 91.2

4,200 82.5

5,521 72.2

6,842 61.0

8,164 49.9

9,485 39.4

10,806 30.1

12,127 22.3

13,449 16.0

14,770 11.1

16,091 7.4

17,412 4.8

18,734 3.0

20,055 1.8

21,376 1.1

22,697 0.6

24,019 0.3

25,340 0.1

Failure Modes: Erosion Cavitation (wear out). Layer Separation & Fatigue (randomly).

Software Ref:

23

6.- Lineal Regression Statistical Model.-

24

6.- Statistic Domain: Matrix Plot to Accum Variable vs. Working Age.-

Statistical Model to variables by exception with low sensitivity to change in mobile equipment

9000800070006000500040003000200010000

40

30

20

10

0

Bearing Hours

Accu

mu

late

d L

ea

d

2500 (Infant Age)

29 Caution

7000 (Mid Life)

WT047

WT051

HT150HT143HT119HT113

HT111

HT080

HT074

HT066HT050

HT151

HT152

HT115

HT135HT105 HT055

HT054

HT046

HT147HT123HT120 HT114HT108

HT107

HT064HT056

HT144HT142HT122HT067HT048

HT128

HT127 HT126

HT124

HT063

HT061

HT044

HT106

HT102HT071

HT065

HT149

HT140HT112HT110

HT103

HT101HT060HT137

HT130HT116HT052HT139HT133

HT134 HT077HT073

HT062HT131

HT104HT145

HT059HT072

HT138

HT132 HT057

HT045HT141

HT125 HT070HT154HT076

HT049

HT117HT078

HT118

HT079

HT058

HT146HT121

HT069

HT153

HT053

HT068HT109

HT148

HT136

HT129

HT075

Matrix Plot of Accumulated Lead vs Bearing Hours_20110511

25

6.- Statistic Domain: Matrix Risk Plot to eliminate Bad Actors.-

Scheduled to 16th May


This con rod bearings was changed by Mid Life & On condition


After of HT068


26

6.- Risk Management: Reliability Analysis by Crystal Ball to Erosion Cavitation [email protected]

There are used for: Uncertainly. Time series prognostic. Probabilistic Optimization

Software Ref:

27

6.- Risk Management: Matrix Risk Plot to eliminate the "Bad Actors" according the Risk (Slope).-.-

1000080006000400020000

40

30

20

10

0

x = Bearing Hours

y =

Accu

mu

late

d L

ea

d

2879 (86.2%)

29 (Fail)

6842 (53.5%)

R-sq = 71.7%

Matrix Risk of Accumulated Lead vs Bearing Hours

y = f(x); Accum Lead accord with Con Rod Bearings Hours (normal condition); not by exception.

= 0.006

= 0.005

= 0.007 = 0.005

= 0.005

= 0.004

Software Ref:

28

6.- Risk Management: Slope Analysis Adjusted to Two Life Cycle for Con Rod Bearings".-

Statistical Model to variables by exception with low sensitivity to change in mobile equipment

29

6.- Risk Management: Slope Analysis Adjusted with the Real Context.-

120001000080006000400020000

60

50

40

30

20

10

0

X-Data

Y-D

ata

Accum Lead HT069 * Con Rod Bearing HT069

Accum Lead HT069_1 * Con Rod Bearing HT069_1


Variable

Scatterplot of Accum Lead vs Con Rod Bearings Accum Hours HT069

Slope: 0.005; R-sq= 97.3%

Slope:0.004; R-sq= 95.6%

Slope:0.006; R-sq= 96.1%

9000800070006000500040003000200010000

30

25

20

15

10

5

0

X-Data

Y-D

ata




Variable


Slope:0.002; R-sq= 95.7%

Slope:0.003; R-sq= 98.6%

Slope:0.001; R-sq= 67.9%

9000800070006000500040003000200010000

50

40

30

20

10

0

X-Data

Y-D

ata




Variable


Slope:0.006; R-sq= 98.7%

Slope:0.007; R-sq= 99.3%

Slope:0.003; R-sq= 99.1% 1. Parameters: 1. R-sq > 65%. 2. Individuals Trends. 3. Risk of Failure

30

6.- Risk Management: Matrix Risk Plot to eliminate Bad Actors.-

120001000080006000400020000

60

50

40

30

20

10

0

Con Rod Bearing Hours

Accu

mu

late

d L

ea

d

7000 (Mid Life)2500 (Infant Age)

29 (fail)

HT075HT146HT129 HT076HT109HT154HT147WT047

HT148HT115HT074

HT056HT119

HT124

HT114HT064HT120HT122HT067HT048

HT142HT072HT123 HT108HT127HT126HT063HT044HT105

HT059HT065

HT057HT132HT128HT101HT060HT071HT140

HT107 HT110HT137HT052HT102HT106HT130HT139 HT138

HT112HT116HT073

HT045HT131HT077

HT133 HT141HT103 HT125

HT070HT049

HT104 HT078HT145 HT058HT079

HT117

WT051HT055HT121HT053

HT061HT068

HT153

HT054

HT144

HT069

R-sq= 75.1%

Scatterplot of Accumulated Lead vs Bearing Hours_20110602TBD (accident)

Has accumulated 9,241 hours scheduled to Jun

11th

Scheduled to Jun 03th

Has accumulated 5,683 hours, scheduled to Jun

26th

Has accumulated 6,937 hours, scheduled to Jun

6th

31

6.- Risk Management: Reliability Analysis_20110623.-

20000150001000050000

0.00012

0.00008

0.00004

0.00000

Bearings Hours

PD

F

100001000100

90

50

10

1

Bearings Hours

Pe

rce

nt

20000150001000050000

100

50

0

Bearings Hours

Pe

rce

nt

20000150001000050000

0.0006

0.0004

0.0002

0.0000

Bearings Hours

Ra

te

Erosion Cavitation


Failure Mode

2.22049 8790.94 0.943 44 0

1.61573 7396.52 0.859 45 0

Shape Scale AD* F C

Table of Statistics



Distribution Overview Plot for Bearings Hours_20110623ML Estimates-Complete Data

Weibull

Software Ref:

32

6.- Risk Management: Survival and Hazard Function Analysis.-.- 20000100000

0.00010

0.00005

0.00000

Bearings Hours

PD

F

100001000100

90

50

10

1

Bearings Hours

Pe

rce

nt

20000100000

100

50

0

Bearings Hours

Pe

rce

nt

20000100000

0.0003

0.0002

0.0001

0.0000

Bearings HoursR

ate

Erosion Cavitation


Failure Mode:

1.66230 9079.24 0.963 33 0

1.28012 7686.00 0.975 45 0

Shape Scale Corr F C

Table of Statistics



Distribution Overview Plot for Bearings Hours per Failure ModeLSXY Estimates-Complete Data

Weibull

h (; , ) =

.1

FM1:Erosion Cavitation

FM2:Layer Separation & Fatigue

Where, = = Shape. = = Scale.

,

h ()

1.1

1.2

1.3 1.4

1.5

1.6

= shape

Statistical Model to variables by exception with low sensitivity to change in

mobile equipment

Desired Performance FunctionalFailure

Time

R (t)

Detectable Deterioration

Total Failure

UnexpectedBreakdown

P

F

PotentialFailure

Warning Interval(P F Net)

PrematureReplacement

Cost Curve

25002000150010005000

30

25

20

15

10

5

0

x=Bearing Hours

Accu

mu

late

d L

ea

d

2500 (Infant Age)

29 (Caution Level)Linear

Quadratic

Fits

HT076

HT118

HT136HT053HT069HT146HT109HT117 HT080

HT149HT129

HT075HT148HT143HT113HT151HT150

HT152

HT135

HT065

HT134

HT068

Scatterplot of Accumulated Lead vs Bearing Hours (Monitoring State)

Updated 20110623

R-sq=70.6%

R-sq=74.6%

34

6.- Risk Management: Matrix Risk Plot adjusted only to Normal Zone.-

The behavior of this equipment is

projecting to the caution zone

6.- Risk Management: Reliability Analysis (PDF) improved.-

37

0.00012

0.00010

0.00008

0.00006

0.00004

0.00002

0.00000

X = Hours

De

nsit

y

=8,573

63.2%

0 =9,356

36.8%

0

MTTF=7,593 MTTF=8,315

PCR:16,000Mid Life:7,0002.1 8573

2.6 9356

Shape Scale

Weibull, Thresh=0

Distribution Plot: Layer Separation, Erosion Cavitation

Updated: 20110804

63.2%

=2.6

=2.1

Findings:

The MTTF to "Erosion Cavitation" is making progress toward the PCR.

The MTTF in addition is covering the Mid Life Strategy.

= Shape = Characteristic Life MTTF. Business Objective ICA.

7.- Statistic Domain: Regression Analysis: "Best Sub sets" & "PLS" by exceptions.-

38

Findings:

The loading plot show us that the main predictors variables are: Iron, Silicon, Soot, Sodium & Cooper.

In another domain, we need to check the Oxidation level (service accuracy)

0.50.40.30.20.10.0-0.1-0.2-0.3

0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

-0.3

-0.4

Component 1

Co

mp

on

en

t 2

Kin Visc

NitrationOxidation

SulfurSoot

Potassium

Sodium

Silicon

Aluminum

Copper

Tin

Chrome

PQ

Iron

PLS Loading Plot: Lead HT134March to July 2011

Iron Copper Silicon Soot Sodium Oxidation

Software Ref:


39

Best Subsets Regression: Lead versus Iron, PQ, ... HT103 Response is Lead

P O N

A o x i K

l S t i t i

C C u i S a S d r n

h o m l o s u a a

I r p i i d s S l t t V

r o T p n c i i o f i i i

Mallows o P m i e u o u u o u o o s

Vars R-Sq R-Sq(adj) Cp S n Q e n r m n m m t r n n c

1 79.9 79.1 47.8 0.56865 X

1 79.3 78.4 50.0 0.57765 X

2 84.5 83.1 33.9 0.51021 X X

2 83.4 81.9 37.8 0.52829 X X

3 86.9 85.1 27.5 0.47961 X X X

3 86.1 84.2 30.3 0.49376 X X X

4 89.2 87.2 21.5 0.44527 X X X X

4 88.6 86.4 23.6 0.45806 X X X X

5 91.8 89.7 14.6 0.39854 X X X X X

5 91.3 89.1 16.3 0.41025 X X X X X

6 93.6 91.6 10.1 0.35964 X X X X X X

6 92.3 89.8 14.9 0.39640 X X X X X X

7 94.4 92.2 9.6 0.34809 X X X X X X X

7 94.0 91.6 11.0 0.35981 X X X X X X X

8 94.9 92.5 9.8 0.34114 X X X X X X X X

8 94.6 92.0 10.9 0.35134 X X X X X X X X

9 95.2 92.6 10.6 0.33918 X X X X X X X X X

9 95.2 92.5 10.6 0.33996 X X X X X X X X X

10 96.2 93.6 9.3 0.31408 X X X X X X X X X X

10 95.8 93.0 10.7 0.32990 X X X X X X X X X X

11 96.4 93.6 10.4 0.31420 X X X X X X X X X X X

11 96.3 93.4 10.9 0.32006 X X X X X X X X X X X

12 96.6 93.5 11.8 0.31707 X X X X X X X X X X X X

12 96.6 93.5 11.8 0.31712 X X X X X X X X X X X X

13 96.8 93.3 13.2 0.32197 X X X X X X X X X X X X X

13 96.7 93.2 13.4 0.32473 X X X X X X X X X X X X X

14 96.8 92.8 15.0 0.33339 X X X X X X X X X X X X X X

Oxidation Level.- This represent the PM

service accuracy

Wear Variables.- This represent main

predictors

R sq (adj). Cp Mallows


40

Findings:

The loading plot show us that the main predictors variables are: Iron, Silicon, Soot, Sodium, Kin Visc & Oxidation

In another domain, we need to check the Oxidation level (service accuracy)

0.40.30.20.10.0

0.3

0.2

0.1

0.0

-0.1

-0.2

-0.3

-0.4

-0.5

-0.6

Component 1

Co

mp

on

en

t 2

Kin Visc

Nitration

Oxidation

Sulfur

Soot

Potassium

Sodium

SiliconAluminum

Copper

Tin

Chrome

PQ

Iron

PLS Loading Plot: HT103

Iron Copper Silicon Soot Sodium Oxidation Kin Visc

Software Ref:

7.- Deterministic Domain: Matrix Risk Plot & Prognostic Determination.-

41

70006000500040003000200010000

30

25

20

15

10

5

0

X-Data

Y-D

ata

7000 (Mid Life)2500 (Infant Age)

29 Accum Lead Generated * Con Rod Bearing Hours SimulatedAccum Lead HT053 * Con Rod Bearing HT053Variable

26262525

252424

23

202020202019

1716

13

876

5

4

2

8

76

5

42

Scatterplot of Accum Lead G vs Con Rod Bear, Accum Lead H vs Con Rod B

HT053, Bronze Bearings

R-sq = 91.5%R-sq = 98.4%

Distribution ID Plot for Lead Descriptive Statistics

N N* Mean StDev Median Minimum Maximum Skewness Kurtosis

6 0 1.66667 0.516398 2 1 2 -0.968246 -1.875

Goodness of Fit Test

Distribution AD P

Normal 1.091

8.1 Summary of all cost in the first year (only 2011)

Summary of Benefit Cost:

Clearly avoided catastrophic failure - HT130: $530,610.30

Avoided possible catastrophic failure in progress (11 cases): $3555,841.56

Total Cost of Benefits: $4086,451.86

8. Total Impact due to B. Problems.-

Summary of Impact Cost:

Control the situation - One time assumed impact: $1022,976.67

Implement the actions - Future impact: $584,193.58

Total Cost of Impact: $1607,170.25

Difference between Benefit and Impact:

Total Cost to Avoid: $2479,281.61 42

43

8.2 Summary of all cost in the following years (2012 to up)

Summary of Benefit Cost:

Annual rate of engine catastrophic faulires avoided (8 cases): $2586,066.59

Total Cost of Benefits: $2586,066.59

8. Total Impact due to B. Problems.-

Summary of Impact Cost:

Implement the actions - Future impact: $584,193.58

Total Cos of Impact: $584,193.58

Difference between Benefit and Impact:

Total Cost to Avoid: $2001,873.01

Adolfo Hitler Huaman Diaz General Manager AMBE

[email protected]

44

07. gestión de fallas críticas con enfoque probabilístico en flota pesada - adolfo huamán diaz

Documents

maintenance cost

strategic management

tactic management

risk operating costs

uptime important interface

disposal risk ft cost

internal reliability

assets system