MACHINE LEARNING
ML Erosion Prediction Workflow
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CASE ANALYSIS
Scenario
An oil and gas operator uses acoustic emission sensors to monitor internal pipeline erosion. This flowchart describes the ML workflow to predict erosion risk and trigger maintenance before failure.
Key decisions
- Split dataset into training and test sets
- Accept model performance or tune hyperparameters
- Detect model drift and retrain on new data
- Detect significant erosion and issue maintenance alert
When to reuse this
When building or deploying machine learning models for asset integrity using continuous sensor data, especially acoustic monitoring in pipelines.
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flowchart-ml-workflow-for-internal-pipeline-erosion-prediction-using-a