Machine learning based repair forecasting
AssigneeThe Boeing Company
InventorsJames M. Ethington, Liessman Sturlaugson
Life of this patent
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Abstract
A data processing system may include instructions stored in a memory and executed by a processor to categorize a plurality of systems into clusters using an unsupervised machine learning method to analyze repair data parameters of a historical dataset relating to the plurality of systems. The system may assign a repair forecast to each cluster, and may generate a system repair forecasting model using selected predictor variables, the historical data set, and the repair forecasts according to a supervised machine learning method. The selected predictor variables may correspond to a mathematical combination of operational data parameters in the historical dataset.
Claims20
A data processing system for generating repair forecasting models, comprising:
- one or more processors;
- a memory including one or more digital storage devices;
- and
- a plurality of instructions stored in the memory and executable by the one or more processors to:
- receive a historical dataset relating to each system of a plurality of systems, the historical dataset including a plurality of repair data parameters and a plurality of operational data parameters;
- automatically categorize the plurality of systems into at least two clusters by using an unsupervised machine learning method to analyze the historical dataset with respect to the plurality of repair parameters;
- assign a repair forecast label to each cluster of the at least two clusters;
- and
- generate a system repair forecasting model, using one or more selected predictor variables, the repair forecast labels, and the historical dataset to train the system repair forecasting model according to a supervised machine learning method;
- wherein each of the one or more selected predictor variables corresponds to a mathematical combination of one or more of the operational data parameters.
A computer implemented method of generating a repair forecasting model, comprising:
- receiving a historical dataset relating to each system of a plurality of systems, the historical dataset including a plurality of repair data parameters and a plurality of operational data parameters;
- categorizing the plurality of systems into at least two clusters by using an unsupervised machine learning method to analyze the historical dataset with respect to the plurality of repair parameters;
- assigning a repair forecast label to each cluster of the at least two clusters;
- and
- generating a system repair forecasting model, using one or more selected predictor variables, the repair forecast labels, and the historical dataset to train the system repair forecasting model according to a supervised machine learning method;
- wherein each of the one or more selected predictor variables corresponds to a mathematical combination of one or more of the operational data parameters.
A computer program product for generating repair forecasting models, the computer program product comprising:
- a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer readable program code configured to cause a data processing system to generate a repair forecasting model, the computer readable program code comprising:
- at least one instruction to receive a historical dataset relating to each system of a plurality of systems, the historical dataset including a plurality of repair data parameters and a plurality of operational data parameters;
- at least one instruction to automatically categorize the plurality of systems into at least two clusters by using an unsupervised machine learning method to analyze the historical dataset with respect to the plurality of repair parameters;
- at least one instruction to assign a repair forecast label to each cluster of the at least two clusters;
- and
- at least one instruction to generate a system repair forecasting model, using one or more selected predictor variables, the repair forecast labels, and the historical dataset to train the system repair forecasting model according to a supervised machine learning method;
- wherein each of the one or more selected predictor variables corresponds to a mathematical combination of one or more of the operational data parameters.
Dependencies are inferred from the claim text.