System: Active
Assets: 6
Phase:Ready
ML Load:
0%
Alerts: 0
Fleet Health
63%
Assets Monitored
6
3 healthy•1 warning
Critical
1
Require immediate attention
Predictions
2
Failures predicted
Active Alerts
0
0 total today
Equipment Status
CNC Mill #1
healthyCNC-001•Haas VF-4SS•Bay A - Station 1
Health Score
94%
Temperature
42°C
Thresh: 65°C
Vibration
2.3 mm/s
Thresh: 6
Current
28 A
Nominal
Runtime
4.3k hrs
Total
Next Service
18 days
Scheduled
CNC Mill #2
warningCNC-002•Haas VF-4SS•Bay A - Station 2
Health Score
67%
Temperature
58°C
Thresh: 65°C
Vibration
4.8 mm/s
Thresh: 6
Current
34 A
Nominal
Runtime
6.1k hrs
Total
Next Service
3 days
Scheduled
Failure Prediction
Spindle Bearing Wear predicted in 168 hours
Confidence: 94%
Hydraulic Press A
healthyPRESS-001•Komatsu H2F-300•Bay B - Station 1
Health Score
88%
Temperature
38°C
Thresh: 55°C
Vibration
1.9 mm/s
Thresh: 5
Current
45 A
Nominal
Runtime
3.2k hrs
Total
Next Service
12 days
Scheduled
Main Conveyor
criticalCONV-001•Dorner 3200•Main Floor - Line 1
Health Score
34%
Temperature
65°C
Thresh: 55°C
Vibration
8.2 mm/s
Thresh: 6
Current
22 A
Nominal
Runtime
8.9k hrs
Total
Next Service
Overdue
Scheduled
Failure Prediction
Motor Bearing Failure predicted in 24 hours
Confidence: 94%
Coolant Pump #1
healthyPUMP-001•Grundfos CR-45•Utility Room
Health Score
78%
Temperature
44°C
Thresh: 60°C
Vibration
3.1 mm/s
Thresh: 5.5
Current
18 A
Nominal
Runtime
5.6k hrs
Total
Next Service
8 days
Scheduled
Welding Robot
maintenanceROBOT-001•FANUC R-2000iC•Bay C - Cell 1
Health Score
15%
Temperature
25°C
Thresh: 50°C
Vibration
0.2 mm/s
Thresh: 4
Current
0 A
Nominal
Runtime
0.0k hrs
Total
Next Service
In Progress
Scheduled
ML Model
ActiveNeural Activity
ML Load
0%
Confidence
0.0%
Data Points Analyzed0.00M
Model TypeLSTM + Transformer
Training Data2.4M data points
Accuracy94.2%
False Positive3.1%
Alerts
How We Predict Failures
Our ML models learn your equipment's normal behavior and detect subtle anomalies that indicate impending failures.
01
12+ sensor types
Sensor Integration
Connect existing sensors or deploy new ones for vibration, temperature, current, and pressure monitoring.
02
2-4 weeks setup
Baseline Learning
ML models learn each machine's unique operational signature over 2-4 weeks.
03
< 1 min detection
Anomaly Detection
Real-time analysis identifies deviations from normal patterns before failures occur.
04
94% accuracy
Failure Prediction
Advanced models predict specific failure modes with 2-4 week warning windows.
Proven ROI Across Industries
Our predictive maintenance systems are deployed across manufacturing, food & beverage, and industrial facilities.
43%
Less unplanned downtime
Average across deployments
25%
Lower maintenance costs
Parts + labor savings
2-4 wks
Prediction window
Time to plan repairs
<6 mo
ROI payback
Typical return period
"The system predicted a spindle failure 3 weeks out. We scheduled the repair during planned downtime and avoided what would have been 2 days of lost production."
Maintenance Manager
Precision Machining Facility
Stop Unplanned Downtime
Let us analyze your equipment and show you which machines would benefit most from predictive maintenance.