Foundations of Real Time Predictive Maintenance with Root Cause Analysis

David Kaufmann, Florian Steffen Klück, Franz Wotawa, Iulia-Dana Nica, Hermann Felbinger, Adil Mukhtar, Petr Blaha, Matus Kozovsky, Zdenek Havranek, Martin Dosedel

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Research on cyber-physical systems comes to the fore with the increasing progress of applications in the field of autonomous systems. Therefore, there is a growing interest in methods for enhancing reliability, availability, and self-adaptation of such systems in safety critical situations. Hence, it is essential that autonomous systems are equipped with a detection system to observe faulty behaviour in real time or to predict failing operations to avoid safety critical scenarios, which may harm people. To bring or hold a system within healthy conditions, not only detecting a faulty behaviour is important, but also to find the corresponding root cause.
In this article, we introduce different methods which make use of detecting unexpected behaviour in cyber-physical systems, for the localization of faults. The first approach, model-based diagnosis uses logic to represent a cyber-physical system to perform reasoning for computing diagnosis candidates. A second promising approach deals with simulation- based diagnosis systems, using digital twin models to produce faulty behaviour data in advance, and to find correlations with the original cyber- physical system’s behaviour, for diagnosis. For the third method the focus is set on artificial intelligence (machine learning and neural networks), where the goal is to utilize a huge amount of health and safety critical observations of the system for training to approximate the behaviour associated with faulty and safety critical states.
Original languageEnglish
Title of host publicationArtificial Intelligence for Digitising Industry
EditorsOvidiu Vermessen, Reiner John, Cristina De Luca, Marcello Coppola
PublisherRiver Publishers
Chapter1.4
Pages47-61
ISBN (Electronic)9788770226639
ISBN (Print)9788770226646
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Artificial Intelligence (AI)
  • Industrial Internet of Things (IIoT)
  • Machine learning
  • Deep learning
  • Neural networks
  • Machine vision
  • Smart robots
  • AI at the edge
  • Silicon-born AI Industrial sectors
  • automotive
  • Semiconductor
  • industrial machinery
  • food and beverage
  • transportation

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