Patent ID: 8700550

Claim:
A computer-implemented adaptive model training method, said method comprising the steps of: providing a previously trained model having a learned scope of normal operation of an asset obtained from an initial set of training data values; acquiring a set of asset operating data values from the asset for defining operation of the asset; assigning a measure of data quality to the asset operating data values in the acquired set of asset operating data values based on at least one predefined criterion for comparing the asset operating data values in the acquired set of asset operating data values with the previously trained model having the learned scope of normal operation of the asset; filtering the acquired set of asset operating data values for selecting an additional set of training data values from the acquired set of asset operating data values based on at least one predefined criterion of good data quality utilizing the measure of data quality assigned to the asset operating data values in the acquired set of asset operating data values; creating an adapted set of training data values for defining an adapted scope of normal operation of the asset by combining at least one of the data values from the initial set of training data values with at least one of the data values from the selected additional set of training data values based on at least one predefined criterion for selectively choosing the data values included in the adapted set of training data values; and recalibrating the previously trained model having the learned scope of normal operation of the asset by utilizing the created adapted set of training data values for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.