ML Analytics

ML

Predictive capabilities powered by machine learning

Machine learning models trained on your historical data enable predictive capabilities. Detect anomalies before they cause failures, predict remaining useful life, and classify machine states with high accuracy.

Fault Classification Confusion Matrix
ML
Overall Accuracy: 89.2%
Actual
Normal
Tool Wear
Bearing
Chatter
Thermal
Normal
184
10
0
2
4
Tool Wear
16
170
8
6
0
Bearing
4
6
176
14
0
Chatter
6
8
12
174
0
Thermal
6
4
0
2
188
Predicted
Sources:Feature VectorsLabeled Training Data
Remaining Useful Life (RUL) Prediction
ML
Sources:Sensor FeaturesLSTM ModelMonte Carlo Dropout
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Model Training

Models are trained on 6-12 months of historical data, including normal operation and known failure events. Transfer learning from fleet data accelerates training for new machines.

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Data Requirements

Minimum 1,000 hours of operational data with labeled events. Higher data quality and volume improves prediction accuracy. Controller + retrofit sensor data yields best results.

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Continuous Improvement

Models retrain weekly on new data. Prediction confidence scores help operators decide when to act. False positive rates decrease over time as the model learns your specific machines.