A hybrid-logic approach towards fault detection in complex cyber-physical systems

Nisheeth Srivastava, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Existing data mining approaches to complex systems anomaly detection use uni-variate and/or multi-variate statistical hypothesis testing to assign anomaly scores to data streams associated with system components. The former approach assumes statistical independence of individual components, while the latter assumes substantial global systemic correlation. As a compromise between these two epistemological extremes, we present a data-mining approach hybridizing existing statistical techniques with theorem-proving methods to create a novel algorithm for anomaly detection, diagnosis and control in complex systems. Our algorithm takes sensor inputs from physical sensors providing system subcomponent performance data and outputs (i) a global systemic risk indicator and (ii) possible diagnosis hypotheses. We present results on three different systems, and in comparison with current state-of-the-art fault detection algorithms to demonstrate the viability of our approach. We find that our algorithm proves robust towards increased data dimensionality in contrast with existing clustering-based fault detection methods and can also detect contextual faults that are undetectable using existing statistical techniques.

Original languageEnglish (US)
Title of host publicationAnnual Conference of the Prognostics and Health Management Society, PHM 2010
PublisherPrognostics and Health Management Society
ISBN (Electronic)9781936263011
StatePublished - Jan 1 2010
EventAnnual Conference of the Prognostics and Health Management Society, PHM 2010 - Portland, United States
Duration: Oct 13 2010Oct 16 2010

Publication series

NameAnnual Conference of the Prognostics and Health Management Society, PHM 2010

Other

OtherAnnual Conference of the Prognostics and Health Management Society, PHM 2010
CountryUnited States
CityPortland
Period10/13/1010/16/10

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