Anomaly detection for predictive maintenance and deriving outcomes and workflows based on data quality

Abstract

Systems, methods, and computer readable storage mediums for performing sensor health monitoring are described. The method includes verifying data quality and suppressing alert generation using machine learning techniques to identify whether two anomalies generated by an asset monitoring system are related. The method can include receiving data characterizing measurement data acquired by a sensor coupled to an industrial asset. An anomalous data sample within the received data can be identified and removed from the anomalous data sample. A new sample of the removed data sample can be estimated using interpolation and the new sample can be assessed. Maintenance analysis can be performed based on the assessed, estimated new sample.