Measurement result analysis by anomaly detection and identification of anomalous variables
AssigneeElisa Oyj
InventorRasmus Heikkilä
Life of this patent
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Abstract
A computer implemented method of analyzing measurement results of a target system, such as an industrial process or communication network. The method includes receiving a data sample with a plurality of variables representing the measurement results, detecting that the data sample is an anomalous sample using an anomaly detection model, processing the data sample by applying an imputation model to selected subsets of variables of the data sample to obtain imputed samples, and applying the anomaly detection model to the imputed samples, determining anomalous variables of the data sample based on results from the processing of the data sample, and outputting the anomalous variables of the data sample for management operations in the target system.
Claims10
A computer implemented method of analyzing measurement results of a target system, the method comprising:
- receiving a data sample comprising a plurality of variables representing the measurement results,
- detecting that the data sample is an anomalous sample using an anomaly detection model,
- processing the data sample by separately applying an imputation model to a plurality of selected subsets of variables of the data sample to obtain a plurality of imputed samples, and applying the anomaly detection model to the imputed samples to obtain respective anomaly scores,
- determining anomalous variables of the data sample based on results from the processing of the data sample, and
- outputting the anomalous variables of the data sample for management operations in the target system.
An apparatus comprising
- a processor, and
- a memory including computer program code;
- the memory and the computer program code configured to, with the processor, cause the apparatus to perform:
- receiving a data sample comprising a plurality of variables representing the measurement results,
- detecting that the data sample is an anomalous sample using an anomaly detection model,
- processing the data sample by separately applying an imputation model to a plurality of selected subsets of variables of the data sample to obtain a plurality of imputed samples, and applying the anomaly detection model to the imputed samples to obtain respective anomaly scores,
- determining anomalous variables of the data sample based on results from the processing of the data sample, and
- outputting the anomalous variables of the data sample for management operations in the target system.
A non-transitory memory medium comprising computer executable program code which when executed by a processor causes an apparatus to perform:
- receiving a data sample comprising a plurality of variables representing the measurement results,
- detecting that the data sample is an anomalous sample using an anomaly detection model,
- processing the data sample by separately applying an imputation model to a plurality of selected subsets of variables of the data sample to obtain a plurality of imputed samples, and applying the anomaly detection model to the imputed samples to obtain respective anomaly scores,
- determining anomalous variables of the data sample based on results from the processing of the data sample, and
- outputting the anomalous variables of the data sample for management operations in the target system.
Dependencies are inferred from the claim text.