Predictive Maintenance Dashboard

AI-powered equipment failure prediction & maintenance optimization

ML Active Monitoring

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Critical Risk

Equipment units

High Risk

Equipment units

Predicted Failures

Next 30 days

Cost Savings

€47K

This quarter

Failure Probability Trend

30-day rolling prediction confidence

Equipment Health Score

ML-based health assessment

Hydraulic Press #3

Predicted failure in 5-7 days

23%

Health Score

Risk Factors: Vibration anomaly, Temperature spike, Pressure fluctuation

Conveyor Motor #7

Predicted failure in 12-15 days

58%

Health Score

Risk Factors: Bearing wear detected, Current draw increase

CNC Mill #1

Optimal condition

94%

Health Score

Status: All parameters within normal range

Recommended Maintenance Schedule

AI-optimized intervention timeline

URGENT: Hydraulic Press #3

Replace hydraulic seals, inspect pump system

Critical 4h downtime

12d

Conveyor Motor #7

Replace bearings, lubrication service

High Priority 2h downtime

18d

Robotic Arm #2

Preventive calibration, sensor check

Scheduled 1h downtime

25d

CNC Mill #1

Routine inspection, coolant system check

Routine 30min downtime

Real-time Sensor Anomalies

Live monitoring of critical parameters

Vibration

2.4 mm/s

↑ 15% above normal

Temperature

78°C

↑ 8% above normal

Pressure

145 bar

✓ Normal range

Current Draw

12.3 A

↑ 6% above normal

ML Model Performance Metrics

Prediction accuracy and model confidence

94.2%

Prediction Accuracy

87.5%

Model Confidence

156

Failures Prevented

€284K

Total Savings (YTD)

Model Details: Random Forest Classifier trained on 2.4M sensor readings from 47 equipment units. Features include vibration patterns, temperature profiles, current draw, pressure variations, and historical maintenance records. Model updated weekly with new data.