TY - GEN
T1 - Model Assisted Distributed Root Cause Analysis
AU - Mayrhofer, Michael
AU - Mayr-Dorn, Christoph
AU - Guiza, Ouijdane
AU - Egyed, Alexander
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2021/9
Y1 - 2021/9
N2 - Cyber-physical production systems are composed of
a multitude of subsystems from diverse vendors and integrators,
connected in a distributed fashion. An undesirable phenomenon
in one system might cause a misbehavior in another connected
system. Searching for the root cause of this misbehavior quickly
becomes very tedious as many possible search directions exist.
This paper proposes an approach and algorithm to tie together
information available in design-time and runtime models. This
then allows, in conjunction with observed and desired status of
a system, to recommend search options and concrete solution
steps to guide workers along the fixing process without being
overwhelmed by the complexity of the overall system of systems.
We demonstrate the feasibility of our approach using a lab-scal
production cell model.
Index Terms—Automation systems; information models;
worker assistance; debugging; root cause analysis; cyber physical
systems
AB - Cyber-physical production systems are composed of
a multitude of subsystems from diverse vendors and integrators,
connected in a distributed fashion. An undesirable phenomenon
in one system might cause a misbehavior in another connected
system. Searching for the root cause of this misbehavior quickly
becomes very tedious as many possible search directions exist.
This paper proposes an approach and algorithm to tie together
information available in design-time and runtime models. This
then allows, in conjunction with observed and desired status of
a system, to recommend search options and concrete solution
steps to guide workers along the fixing process without being
overwhelmed by the complexity of the overall system of systems.
We demonstrate the feasibility of our approach using a lab-scal
production cell model.
Index Terms—Automation systems; information models;
worker assistance; debugging; root cause analysis; cyber physical
systems
UR - https://www.scopus.com/pages/publications/85122928020
U2 - 10.1109/ETFA45728.2021.9613684
DO - 10.1109/ETFA45728.2021.9613684
M3 - Conference proceedings
SP - 1
EP - 8
BT - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )
ER -