Machine learning server-side flow measurement
AssigneeFlowCommand Inc.
InventorsJeffrey Garoon, David Jed Somers
Life of this patent
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Abstract
Systems and methods enable predicting flow rate in a pipeline by using machine learning. Data is collected from a pipeline with an acoustic device and transmitted from the acoustic device to a server. The data is processed and, based on the processing, a reference model is selected that is most appropriate for the processed data. Features extracted from the data are input to the reference model. The reference model outputs a predicted flow rate.
Claims20
A computer-implemented method for measuring the flow rate of a fluid or gas or multi-phase flow flowing through a pipeline, the method comprising:
- transmitting, by an acoustic device, signals into an outer shell of a pipeline;
- receiving, by the acoustic device, received signals reflected from the contents of the pipeline;
- processing, by the acoustic device, the received signals to generate a data stream;
- transmitting, by the acoustic device, the data stream to a server;
- computing a flow rate based on the data stream by analyzing a Doppler frequency of the received signals;
- performing dimensionality reduction, by the server, on the data stream and one or more acoustic device attributes to generate a device signature;
- identifying, by the server, one of a plurality of reference models having a device signature that is most similar to the device signature of the acoustic device to select a most similar reference model;
- applying, by the server, an attribute identification machine learning model to identify a plurality of important features from the data stream and one or more device attributes;
- and
- applying a multivariate machine learning model associated with the most similar reference model, using the identified important features in the multivariate machine learning model, to predict a flow rate.
A non-transitory computer-readable medium comprising instructions for:
- transmitting, by an acoustic device, signals into an outer shell of a pipeline;
- receiving, by the acoustic device, received signals reflected from the contents of the pipeline;
- processing, by the acoustic device, the received signals to generate a data stream;
- transmitting, by the acoustic device, the data stream to a server;
- computing a flow rate based on the data stream by analyzing a Doppler frequency of the received signals;
- performing dimensionality reduction, by the server, on the data stream and one or more acoustic device attributes to generate a device signature;
- identifying, by the server, one of a plurality of reference models having a device signature that is most similar to the device signature of the acoustic device to select a most similar reference model;
- applying, by the server, an attribute identification machine learning model to identify a plurality of important features from the data stream and one or more device attributes;
- and
- applying a multivariate machine learning model associated with the most similar reference model, using the identified important features in the multivariate machine learning model, to predict a flow rate.
Dependencies are inferred from the claim text.