About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ISWC-Posters 2020
Conference paper
An unsupervised framework for semantics driven causal explanations for anomalies?
Abstract
Explainability for anomaly detection from multivariate time series sensor data procured from large assets in Industry 4.0 is a challeng- ing and relatively unexplored problem. Apart from the temporal nature of time series data itself, another challenging aspect of this problem is the necessity of making the explainer context aware by infusing seman- tics which may originate from a different data modality. To address this problem, we present a workflow for the first-of-its-kind semantics-driven causal explainer for time series which uses Bayesian Network Structure learning techniques and tailors them for the anomaly explanation prob- lem by simultaneously leveraging the knowledge of asset semantics from a graph model. We also present explanatory insights obtained from in- vestigating a mechanical vibration anomaly for a steam turbine which was validated in our engagement with a large European energy company.