Das Szenario
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.
Was diese Zeichnung zeigt
Die dahinterstehenden Entscheidungen verstehen.
01
Split dataset into training and test sets
02
Accept model performance or tune hyperparameters
03
Detect model drift and retrain on new data
04
Detect significant erosion and issue maintenance alert
When building or deploying machine learning models for asset integrity using continuous sensor data, especially acoustic monitoring in pipelines.