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.

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 Life
Engineering
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 System
Interventions /
Histories
Installation
Date
Actual
Component
Life / Hours
OEM
Recomended
Life / Hours
Type of
Events
SH01 Hoist Middle Shaft Hoist Gear 09-may-00 0,0 30.000 B
SH01 Hoist Middle Shaft Hoist Gear 14-abr-02 14.221,6 30.000 EF
SH01 Hoist Middle Shaft Hoist Gear 15-abr-02 0,0 30.000 B
SH01 Hoist Middle Shaft Hoist Gear 04-nov-02 3.390,0 30.000 EF
SH01 Hoist Middle Shaft Hoist Gear 05-nov-02 0,0 30.000 B
SH01 Hoist Middle Shaft Hoist Gear 30-oct-03 6.086,9 30.000 EF
SH01 Hoist Middle Shaft Hoist Gear 31-oct-03 0,0 30.000 B
SH01 Hoist Middle Shaft Hoist Gear 15-jul-07 25.938,2 30.000 EF
SH01 Hoist Middle Shaft Hoist Gear 16-jul-07 0,0 30.000 B
SH01 Hoist Oil Change 15-oct-09 12.691,0 OC
SH01 Hoist Oil Change 22-nov-09 912,0 OC
SH01 Hoist Middle Shaft Hoist Gear 03-dic-09 13.402,0 30.000 EF
SH01 Hoist Middle Shaft Hoist Gear 04-dic-09 0,0 30.000 B
SH01 Hoist Middle Shaft Hoist Gear 01-ene-11 2.639,7 30.000 EF

Equipment System
Interventions /
Histories
Installation
Date
Actual
Component
Life / Hours
EOM
Recomended
Life / Hours
Type of
Events

Equipment System
Interventions /
Histories
Installation
Date
Actual
Component
Life / Hours
OEM
Recomended
Life / Hours
Type of
Events

Equipment System
Interventions /
Histories
Installation
Date
Actual
Component
Life / Hours
OEM
Recomended
Life / Hours
Type of
Events
SH01 Hoist Oil Change 11-sep-09 12.691,0 OC
SH01 Hoist Oil Change 15-oct-09 912,0 OC
SH01 Hoist Oil Change 13-nov-09 7.800,0 OC

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.

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 the
PHM technique to assist maintenance professionals
to interpret condition data by identifying the key risk
factors and their relative influence on the health of
equipment.
Economic considerations were then blended with the
PHM risk model to identify the optimal decision
chart.

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%.

IMEC 2011 Final Clientes

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IMEC 2011 Final Clientes