Intermittent Fault Diagnosis as Discrete Signal Estimation: Trackability analysis

14 pages•Published: January 6, 2018

Abstract

We address the problem of intermittent fault diagnosis as an instance of discrete signal estimation, in the context of fault management in autonomous systems and vehicles. We propose an estimation approach based on constrained optimization using conditional preference theories. We show that in some cases, our estimator can fail to find an estimation for the system. We provide a way to detect and eliminate these cases at design time.

Keyphrases: conditional preferences, discrete event systems, intermittent faults, model checking

In: Marina Zanella, Ingo Pill and Alessandro Cimatti (editors). 28th International Workshop on Principles of Diagnosis (DX'17), vol 4, pages 234-247.

Links:	https://easychair.org/publications/paper/ncvC
	https://doi.org/10.29007/tpdv

BibTeX entry

@inproceedings{DX'17:Intermittent_Fault_Diagnosis_as,
  author    = {Xavier Pucel and Stéphanie Roussel},
  title     = {Intermittent Fault Diagnosis as Discrete Signal Estimation: Trackability analysis},
  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/ncvC},
  doi       = {10.29007/tpdv},
  pages     = {234-247},
  year      = {2018}}

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