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Back-pressure Steam Turbine TB-01

Asset 360 · Turbine · Mill A
TB-01MAINTENANCECriticality ATurbine
Manufacturer
Andalas Power
Model
BPT-2400
Location
Mill A · Utilities
Commissioned
2011-03-05
Age
15 years
Operating hours
83,876 h
Utilization
86%
Availability (90 d)
91.5%
MTBF / MTTR
4,913 h / 6.5 h
Replacement cost
Rp 13.11 bn
Sensors
vibration, rpm, temperature, oilCondition
Region
Sumatra
84/100
Health score

Predictive maintenance

Asset Failure Prediction v3.2 · gradient-boosted survival model on IoT, SCADA and CMMS history · refreshed 06:52

Confidence83%
Health score
84/100
Failure probability
8%7 days
Remaining useful life
120days
Anomaly score
0.150–1
Likely cause
Planned overhaul — governor calibration
Recommendation
Return to service after calibration test
What the model sees
Confidence83%
All monitored signals are within normal bands. Failure probability 8% over 7 days; continue condition monitoring and scheduled PM.
Why this prediction — top drivers (pp of failure probability)
Baseline failure rate for class ≈ 12% · bars push probability up or down
Age / operating hours
+3.8 pp
Vibration RMS trend
+1.7 pp
Speed deviation
+1.0 pp
Temperature deviation
+0.7 pp
Oil condition index
+0.4 pp
Recent preventive maintenance
−2.0 pp
◀ lowers predictionraises ▶

Sensor trends & failure forecast

60-day history (daily) · dashed = model forecast

Vibration · mm/s RMS
Warning 4.5 · Alarm 7.1 mm/s RMS
No degradation trend — forecast shown only when the model detects a trajectory toward the failure threshold.

Maintenance Decision Intelligence

Weighs failure probability, criticality, downtime impact, repair vs replacement cost, spare availability and production schedule

Decision inputs
Failure probability (7 d)8%
CriticalityClass A · production-critical
Downtime impactRp 165 m / h
Planned repair costRp 393 m
Replacement costRp 13.11 bn
Spare availabilityBR-TB-7318: not in stock — procure
Production schedulePeak crop · next shutdown Sun
Option suitability
Monitor82
Failure risk low; condition monitoring sufficient
Schedule maintenance41
Fits next planned shutdown; balances risk and production
Replace asset31
Health 84/100 and age 15 yrs do not justify capex
Repair now2
Stops the line during peak crop — restan and emergency premium
Replace component2
No single failing component identified
Scenario comparison
OPTION A
Repair immediately
Stop line now, replace component using emergency transfer
Failure risk2%
Expected costRp 809 m
Planned downtime4 h

Lowest risk, but interrupts peak-intake processing and creates restan.

OPTION B
Repair in low-production window
Replace at next planned shutdown
Failure risk3%
Expected costRp 472 m
Planned downtime4 h

Balances failure risk against production loss at the next planned shutdown.

Recommended
OPTION C
Continue monitoring
Run to next scheduled shutdown (14 days)
Failure risk8%
Expected costRp 204 m
Planned downtime0 h

Recommended — risk is low; keep condition monitoring and standard PM.

Recommendation: Monitor
Confidence80%
Return to service after calibration test. Option C expected cost Rp 204 m vs Rp 204 m if left to run; residual risk 8%.
Estimated impact based on synthetic scenario assumptions. Recommendation requires validation by the Maintenance Manager.

Spare parts availability

Key parts for TB-01 across the warehouse network · home store WH-01

Cross-module: Inventory AI
BR-TB-7318 · BR-TB-7318
Lead time 14 d · Rp 0/pcs
0 in home store
Not stocked — procured on demand.
GOV-TB-1 · GOV-TB-1
Lead time 14 d · Rp 0/pcs
0 in home store
Not stocked — procured on demand.
Open Spare Parts Intelligence

Maintenance records

CMMS history, open work orders, failure events and spares

Work orderTypeDescriptionCreatedDueTechnicianCostStatus
WO-4181PreventivePlanned overhaul — governor calibration2026-09-272026-09-29Joko T.Rp 22.0 mIn Progress
Synthetic asset data and simulated model output. Maintenance recommendations are decision support requiring validation by qualified personnel.
V-TEKI — IT & Business ConsultingDeveloped by V-TEKI · IT & Business ConsultingPrime Agri AI Intelligence Platform — prototype. All operational records are synthetic; model outputs are simulated; financial impacts are scenario estimates; recommendations require human validation. No actual company confidential data is used.