=Paper=
{{Paper
|id=Vol-3828/ISWC2024_paper_41
|storemode=property
|title=Knowledge Representation and Engineering for Smart Diagnosis of Cyber Physical Systems
|pdfUrl=https://ceur-ws.org/Vol-3828/paper41.pdf
|volume=Vol-3828
|authors=Ameneh Naghdipour,Benno Kruit,Jieying Chen,Peter Kruizinga,Godfried Webers,Stefan Schlobach
|dblpUrl=https://dblp.org/rec/conf/semweb/NaghdipourKCKWS24
}}
==Knowledge Representation and Engineering for Smart Diagnosis of Cyber Physical Systems==
https://ceur-ws.org/Vol-3828/paper41.pdf
Knowledge Representation and Engineering for Smart
Diagnosis of Cyber Physical Systems
Ameneh Naghdi Pour1 , Benno Kruit1 , Jieying Chen1 , Peter Kruizinga2 ,
Godfried Webers3 and Stefan Schlobach1
1
Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, NL
2
Canon Production Printing Netherlands, Van der Grintenstraat 1, 5914 HH Venlo, NL
3
Philips Medical Systems International, Veenpluis 6, 5684 PC Best, NL
1. Motivation
Machine breakdowns pose a substantial expenses for equipment manufactures, such as Canon
and Philips, and their customers. A considerable portion of the expenses comprises salaries
for service engineers, costs for providing spare parts, and training service engineers for fault
diagnosis. Furthermore, breakdowns and subsequent downtime have extensive implications
on the plant capacity as customers are unable to utilize the machine during these periods.
Therefore, manufacturers must prioritize effective fault diagnosis to minimize costs and mitigate
the adverse impacts on the operation of their customers. The current maintenance approach
of the manufacturers involved in this project includes training their own service engineers
to diagnose the fault, by providing them with valuable documentation and sometimes videos.
However, this documentation cannot encompass all the necessary support for service engineers
because of the complexities involved in navigating intricate documentation and the ever-
increasing complexity and size of machinery, particularly with Cyber–Physical Systems (CPS)
like Canon printers and Philips magnetic resonance imaging scanners. Additionally, providing
training video to support service engineer is costly in terms of time and resources.
2. Proposal
To overcome this challenge and enhance support for service engineers, several methods for
fault diagnosis of CPS have been introduced including model-based [9], signal-based [10], and
quantitative-knowledge-based [2]. However, these methods have limitations, such as the need
for precise physical models and reliance on extensive historical (sensor) data, both of which
can be prohibitively expensive to develop. To mitigate these limitations, a promising approach
Posters, Demos, and Industry Tracks at ISWC 2024, November 13–15, 2024, Baltimore, USA
$ a.naghdipour@vu.nl (A. Naghdi Pour); b.b.kruit@vu.nl (B. Kruit); j.y.chen@vu.nl (J. Chen);
peter.kruizinga@cpp.canon (P. Kruizinga); godfried.webers@philips.com (G. Webers); k.s.schlobach@vu.nl
(S. Schlobach)
https://https://github.com/Ameneh71 (A. Naghdi Pour); http://bennokruit.nl/ (B. Kruit);
https://jieyingchenchen.github.io/ (J. Chen); https://www.few.vu.nl/~schlobac/ (S. Schlobach)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Input Phase 1: Phase 2: Output
Knowledge Graph Diagnosis
Documented
Logbook Data Construction
Knowledge
FMEA
Root Cause
Ontology Symptom
Engineering Observation
Bill of
Materials
Tacit Knowledge
Repair
Troubleshooting
Querying & Procedure
Manual Information
Reasoning
Extraction
Figure 1: Construction and application framework of the domain fault knowledge graph
is qualitative-knowledge-based fault diagnosis [3] [1]. One key aspect of this method is the
need for a reasonable model that accurately describes knowledge related to faults. Classical
models like fault trees [6], petri nets [8], and rule systems [7], have been used in the past, but
they typically require prior analysis of potential equipment fault modes and involve manual
editing, which makes them inflexible and challenging to update dynamically. Therefore, we
proposes to use knowledge graph (KG) technology to mine fault knowledge from vast and
diverse documents and then construct a structured and interconnected fault knowledge base.
3. Framework
Figure 1, presents our framework for the construction and application of a fault knowledge
graph, which we have created in close collaboration with our industrial partners. Two main
phases including knowledge graph construction and diagnosis are depicted along with input
sources and output results. The input sources are knowledge and data that have been used in
diagnosing faults. We identified and categorized them by regularly interviewing authorities
in the two aforementioned manufacturers. It is worth mentioning that these various sources
are complementary, we utilize all of them to leverage their strengths while compensating for
their limitations. For example, the Bill of Materials provides the physical structure and location
of each part but lacks information on potential issues. In contrast, sources like Failure Mode
Effects Analysis, troubleshooting manuals, and logbook data offer insights into these problems.
Integrating these sources enables more accurate and effective fault diagnosis.
In the first phase, we manually developed an upper-level ontology that serves as the founda-
tion for structuring the schema of the knowledge graph. This involved a comprehensive analysis
of various input sources to identify valuable knowledge, relevant entities, and relationships
for fault diagnosis. We also formulated competency questions to highlight key queries for the
knowledge graph and conducted interviews with industrial partners to align their expectations
with the ontology. Our fault diagnosis ontology is further inspired by the Industrial Domain
Ontology [5] and the Industrial Ontology Foundry-Maintenance Reference Ontology [4] by
considering and comparing the entities and relations. Currently, we are using this ontology to
create a cohesive KG that allows for the analysis of fault frequencies, locations, interactions,
and solutions. To this end, we apply different information extraction techniques such as Regular
Expressions, Named Entity Recognition and Large language Models to populate data based
on the ontology. Ongoing development indicate that the upper-level ontology allows us to
model a diverse set of qualitative features related to the functioning and repair of complex
cyber-physical systems.
4. Future Work
The next phase, diagnosis, shows the application of our proposed method in which service
engineers observe symptoms of the failure, which should be converted to a query for KG-based
reasoning. As a result, the root cause of the issue along with a procedure should be suggested
to solve the issue. To this end, we are planning develop querying and reasoning systems for
diagnosis, with the aim of supporting different fault diagnosis reasoning techniques.
Acknowledgements
This publication is part of the project ZORRO with project number KICH1.ST02.21.003 of the
research programme Key Enabling Technologies (KIC) which is (partly) financed by the Dutch
Research Council (NWO).
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