Inference of fault signatures of discrete-event systems from event logs

15 pages•Published: January 6, 2018

Cody Christopher, Yannick Pencolé and Alban Grastien

Abstract

In this paper, we propose a method to diagnose faults in a discrete event system that only relies on past observed logs and not on any behavioural model of the system. Given a set of tagged logs produced by the system, the first objective is to extract from them a set of fault signatures. These fault signatures are represented with a set of critical observations that are the support of the diagnosis method. We first propose a method to compute the fault signatures from an initial log journal and follow with detail on how the signatures can then be updated when new logs are available.

Keyphrases: data driven diagnosis, diagnosis, discrete event systems, fault signatures, online diagnosis

In: Marina Zanella, Ingo Pill and Alessandro Cimatti (editors). 28th International Workshop on Principles of Diagnosis (DX'17), vol 4, pages 219-233.

Links:	https://easychair.org/publications/paper/wj4W
	https://doi.org/10.29007/qmpw

BibTeX entry

@inproceedings{DX'17:Inference_fault_signatures_discrete,
  author    = {Cody Christopher and Yannick Pencolé and Alban Grastien},
  title     = {Inference of fault signatures of discrete-event systems from event logs},
  booktitle = {28th International Workshop on Principles of Diagnosis (DX'17)},
  editor    = {Marina Zanella and Ingo Pill and Alessandro Cimatti},
  series    = {Kalpa Publications in Computing},
  volume    = {4},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/wj4W},
  doi       = {10.29007/qmpw},
  pages     = {219-233},
  year      = {2018}}

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