QUICK NAV: MACHINE LEARNING | PIPELINE SEGMENTS | CUSTOMIZE
Flowstate’s machine learning models use pipeline flow rate data to learn operational behaviors and patterns of each pipeline segment. The machine learning models predict what output flow should be. That prediction is compared to actual output flow data in the deep learning imbalance model, and used to detect leaks.
Machine learning reduces false alarms and detects smaller leaks by:
Additionally, Flowstate’s machine learning uses a variety of leak detection methods, customized for your pipeline segment’s characteristics such as:
Classifying a segment provides a starting point for the types of leak detection methods that will be developed and delivered. We will work with your team to determine both methods that will be used and the performance requirements that will be established including:
leak size, time to detection, and false alarm rate.
| Class | Data Failure | Over/Short | Rupture | SVB 2% | SVB 1% | Leak Signature |
|---|---|---|---|---|---|---|
| Class A | X | X | X | X | X | X |
| Class B | X | X | X | X | ||
| Class C | X | X |
Nominally, a minimum of three weeks of operational data is needed to use machine learning to train and test a model for a particular pipeline.
However, if the line is a Class B or Class C segment with irregular or inconsistent flow, a larger window of data will need to be collected to adequately learn the operations.
Our system is developed to comply with API RP 1130.
Learn more about our new approach to leak detection: