Modeling multiple risk factors and working age
description
Transcript of Modeling multiple risk factors and working age
1OMDECOptimal Maintenance Decisions Inc.Optimal Maintenance Decisions Inc.
Modeling multiple risk factors and working age
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Proportional Hazards Modeling(“Extended” Weibull analysis)
Now, what would the optimal policy be if we have the benefit of extra data - namely, condition data?
Parameters Condition data values at time t
1
t
th ...2321 rpmaxialVibipsPbppmFeppme
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Work orders
CBM data
VA+OA+Other monitored data
A Watchdog Agent
CMMS
Event information
Insp
ect io
n d a
t a
DecisionModel
Best
Decisio
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Eve
nt d
ata
Statisical models (EXAKT), Expert Systems, trained neural nets, Case Based Reasoning …
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From Data to Decision
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Example 1
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Example 1 (slide 2)
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Example 1 (slide 3)
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Example 1 (slide 4)
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Example 1 (slide 5)
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Example 1 (slide 6)
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Example 2 (slide 1)
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Example 2 (slide 2)
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Building the Model - Graphical Investigation
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Data missing in this region
More data investigations
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Corrected Silicon
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Oil changes
A
C
BFeppm
Working ageDOil change interval
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Missing oil change events
Missing ‘OC’ events?
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Strange history
Strange History?
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Investigating the strangeness
No events to support this jump in values
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A major challenge in CBM optimization
The Definition of Failure
Initially a failure was declared when wear metals were high.
This was like forcing the model to “chase its tail”.
Needed a physical definition of failure based on the observable condition of the wheel motor at overhaul.
Based on the new definition (gear damage), the number of histories ending in failure doubled.
Model “fit” improved dramatically.
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The Wheelmotor Optimal CBM Model
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Failed at WorkingAge = 11660 hr
Inspection atWorkingAge = 11653 hr
Inspection atWorkingAge = 11384 hr
Had we replaced at 11384 hr…or 11653 hr…!!!!
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Profitability Impact of Optimized CBM
CR = 325% $1M
CR = 544% $1.7M
CR = 650% $2M
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Sensitivity analysis
Sensitivity of model to cost
ratio
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Example 3 – Complex items
Inspections_MA
IdentDateWorkingAgeCovariate1NameCovariate2Name…Comment
Events_MA
IdentDateWorkingAgeEventComment
EventsDescription_MA
EventNamePComment
VarDescription_MA
VariableNameMeasureUnitWarnLimit1WarnLimit2…Comment
CovariatesOnEvent_MA
EventStartingDateEndingDateCovariate1NameCovariate2Name…Comment
IdentToModel
ModelNameIdentNameDate
EventToModel
ModelNameInputEventNameOutputEventNameInputPOutputP
VarToModel
ModelNameInputVariableNameOutputVariableNameVariableDataTypeMeasureUnitWarnLimit1WarnLimit2…
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Example 3 (2)
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Example 3
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The onion skins of CBMCBM (least intrusive)
CBM phys. inspection
PF
CBM overhaul
PF
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.FF
$
$Model A
Model A
Model A
Model B
$
$
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Truck Engine
1. Copper appearing in oil analysis
2. Checked filter – copper particles found
3. Drained engine oil – copper particles found on drain plugs
4. Removed the engine sump and discovered that the main oil pump drive gear-retaining bolt had come loose.
5. Failure mode: excessive wear on brass bushings due to improperly torqued retaining bolt
layers of discovery
Reference:Derek Wilcock, Sishen Iron Ore Mine, "Engine Failure Results in Proactive Solutions". Practicing Oil Analysis Magazine. September 2005
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Increment the knowledge base!
Item Function Failure Cause Effects
Engine To lubricate oil whetted components
Pumps insufficient lube
Main oil pump drive-retaining bolt comes loose due to incorrect torqueing of the bolt
Excessive play between the drive shaft and the front housing causes excessive wear on the brass bushing located in the pump housing and the brass thrust washers on either side of the gear. Particles of copper are generated and will appear in the oil, the sump, the filter, and drain plugs. If allowed to continue, the engine will be badly damaged, to the point of requiring a complete rebuild. The cost to Sishen Iron Ore Mine would be $181,388 to replace a K2000 series engine.
Machine (several test points)
Vibration spectral data
Screening MatrixColumns:1. 10 preselected orders2. 2 highest non-synch peaks in hi
and lo ranges3. Noise floorRows:1. from previous inspection2. Deviation from Av + 1
Normalization Cepstrum4 highest peaks1x sidebands
Demodulation
PossibleBearingdefects Signal
processing
Decision making
Diagnostic templates
For a major comp. group
Pass or fail each diagnosis
Faultdiagnosis
Relativeseverity
Component Specific Diagnostic Matrices (CSDM)
Unique component specific frequenciesAdjacent componentsDifferences from previous inspection.Deviations from Av + 1
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4 brg overhung rotor
Close-coupled (no brgs in pump)
4 brg
vertical
TAN
TAN
RAD
RAD
Component specific diagnostic matrix and diagnostic templates
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Peaks in the spectrum with 3.61x spacings
Peaks in the spectrum with 1x spacings
Cepstrum
10 2 3 7654 8 109
3.61
1x 1x 1x 1x
7.22
2x2x
Spectrum
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Inner race defect travels in and out of the load zone
softer impact
harder impact
Repetitive change in impact forces causing amplitude modulation appearing as sidebands
Harmonic energy related to inner race defect
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Expert rule using results of Demodulation
Expert rule using 1x vibrations1. Consider (for simplicity only the
1x vibration levels of) the vertical motor and centrifugal pump (with coupling),
2. Excessive (7-10 Vdb over baseline) 1x vibrations.
3. Could mean motor imbalance, pump imbalance, angular misalignment, foundation horizontal flexibility, a radial or thrust bearing clearance problem, or motor cooling fan blade damage. Which?
4. Axial and radial data at both locations angular misalignment?. 5. Radial is higher than axial motor imbalance or pump imbalance?6. Axial motion is characteristic (due to rocking) of unbalance in a vertical pump.
Which component is unbalanced?7. In a vertical pump one direction, the direction of external structural support, is
always stiffer than the other directions. The radial axis in this case is the direction of structural flexibility.
8. Low 1x levels at the pump in the tangential direction because tangential axis is the direction of high structural stiffness and therefore the tangential component of the vibration due to motor imbalance does not transmit to the pump.
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Hybrid decision agent
EXAKT
RULE
Optimal decision