The 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.
What is in this drawing
Read the decisions behind it.
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.
More like this