IMEC 2011 Final Clientes

Optimizing mining hoist

maintenance decisions

through blending oil analysis

and hazard modelling

Miguel Sánchez Cía. Minera Doña Inés de Collahuasi, Chile

Pedro Cancino, Dictuc Tribologia, Chile

Introduction

This presentation discusses work carried out at Minera

Collahuasi to improve the existing oil analysis program

currently undertaken for hoist systems.

Current practice for monitoring the health of equipment

is by examining trends in readings obtained from

condition monitoring.

Such an approach does not guarantee that the full

information-value contained in the readings is captured.

Methodology

This paper reports a new approach that uses a statistical

procedure, proportional-hazards modeling (PHM) to

identify the key measurements that should be used to

assess the true state of health of equipment.

Data Availability

Within the mine’s computer maintenance management

system (CMMS) from MINCOM ELLIPSE there are

histories of hoist lifetimes.

Costs associated with failure and preventive removals

were also available.

Oil analysis results from a fleet of 9 shovels were

analyzed along with their respective failures and repairs

over a six year period.

Life Cycle of a Component

Age Effect

Life Cycle of a Component

Current Practice

Normal

<10

<50

<200

<15

Warning

10 - 20

50 - 100

200 - 300

15 - 25

Alarm

>20

>100

>300

>25

Cr

Cu

Fe

Si

Age Effect

Summary: Principle of CBM Optimization

Age Data

Condition-

Monitoring Data

Supplied by user

Software engine

Intermediate result

Final result

Maintenance

Decision

Hazard Model

Transition Model

Cost and

Availability Model

Remaining

Useful LifeEngineering

Judgment

1 1

1

...( ) n nz t z tt

HAZARD t e

Hazard Model

Failures/op.hour

Failures/km.

Failures/cycle

Failures/tonne

Etc…

Contribution

of age to

hazard

Contribution of condition

information to hazard

Combined Data From All Histories / Data Cleaning

Equipment SystemInterventions /

HistoriesInstallation

Date

Actual ComponentLife / Hours

OEM RecomendedLife / Hours

Type of Events

SH01 Hoist Middle Shaft Hoist Gear 09-may-00 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 14-abr-02 14.221,6 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 15-abr-02 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 04-nov-02 3.390,0 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 05-nov-02 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 30-oct-03 6.086,9 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 30-oct-03 48.578,3 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 31-oct-03 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 31-oct-03 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 15-jul-07 25.938,2 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 16-jul-07 0,0 30.000 BSH01 Hoist Oil Change 15-oct-09 12.691,0 OCSH01 Hoist Oil Change 22-nov-09 912,0 OCSH01 Hoist Oil Change 13-oct-10 7.800,0 OCSH01 Hoist Middle Shaft Hoist Gear 03-dic-09 13.402,0 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 04-dic-09 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 01-ene-11 2.639,7 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 01-ene-11 41.979,9 30.000 EF




Date


EOM RecomendedLife / Hours

Type of Events




Date



Type of Events





Date



Type of Events

SH01 Hoist Middle Shaft Hoist Gear 09-may-00 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 14-abr-02 14.221,6 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 15-abr-02 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 04-nov-02 3.390,0 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 05-nov-02 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 30-oct-03 6.086,9 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 30-oct-03 48.578,3 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 31-oct-03 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 31-oct-03 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 15-jul-07 25.938,2 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 16-jul-07 0,0 30.000 BSH01 Hoist Oil Change 11-sep-09 12.691,0 OCSH01 Hoist Oil Change 15-oct-09 912,0 OCSH01 Hoist Oil Change 13-nov-09 7.800,0 OCSH01 Hoist Middle Shaft Hoist Gear 03-dic-09 13.402,0 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 04-dic-09 0,0 30.000 BSH01 Hoist Middle Shaft Hoist Gear 01-ene-11 2.639,7 30.000 EFSH01 Hoist Middle Shaft Hoist Gear 01-ene-11 41.979,9 30.000 EF





Date



Type of Events


Age Effect

How does data model hazard?

significant covariates.

EQUIPMENT FAILURE

Age Effect

Sample History History consists of:

•Beginning time

•Inspection results

•Ending time and reason

Combined data from all histories.

PHM Model for MSHG

Optimal Hazard Level

Cp = Total cost of preventive replacement

Cf = Total cost of failure replacement

Cp = cost of manpower + cost of a new gear

US$ 0.25 M

Cf = cost of manpower + cost of a new gear + opportunity cost

US$ 2.3 M

Decision Graph for MSHG of PA04

Z__00 = - 0.00997583*Iron + 0.14597734*Magnesium + 0.01593247*Molybdenum

+ 0.59406619*Nickel - 0.04483219*Sodium + 0.01527817*Zinc

at 29582 hours or

at 29788 hours.

Conclusion

The case study demonstrates the value of using thePHM technique to assist maintenance professionalsto interpret condition data by identifying the key riskfactors and their relative influence on the health ofequipment.

Economic considerations were then blended with thePHM risk model to identify the optimal decisionchart.

Potential Savings

The optimal decision model indicates that the

implementation of a Condition Based Maintenance

Strategy in these cases will result in more frequent

preventive replacements in order to avoid costly

failures.

The study then indicates that the implementation of the

decision chart would result in a cost reduction of 38%.

Optimizing mining hoist

maintenance decisions

through blending oil analysis

and hazard modelling

Miguel Sánchez Cía. Minera Doña Inés de Collahuasi, Chile

Pedro Cancino, Dictuc Tribologia, Chile

IMEC 2011 Final Clientes

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