=Paper=
{{Paper
|id=Vol-3830/paper1sim
|storemode=property
|title=Knowledge Representation and Engineering for Smart Diagnosis of Cyber Physical Systems
|pdfUrl=https://ceur-ws.org/Vol-3830/paper1sim.pdf
|volume=Vol-3830
|authors=Ameneh Naghdi Pour,Benno Kruit,Jieying Chen,Peter Kruizinga,Godfried Webers,Stefan Schlobach
|dblpUrl=https://dblp.org/rec/conf/semiim/NaghdipourKCKWS24
}}
==Knowledge Representation and Engineering for Smart Diagnosis of Cyber Physical Systems==
https://ceur-ws.org/Vol-3830/paper1sim.pdf
Knowledge Representation and Engineering for Smart
Diagnosis of Cyber Physical Systems
Ameneh Naghdi Pour1 , Benno Kruit1 , Jieying Chen1 , Godfried Webers2 , Peter Kruizinga3
and Stefan Schlobach1
1
Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, NL
3
Canon Production Printing Netherlands, Van der Grintenstraat 1, 5914 HH Venlo, NL
2
Philips Medical Systems International, Veenpluis 6, 5684 PC Best, NL
Abstract
The traditional maintenance approach used by manufactures such as Canon and Philips, which relies on service
engineers’ expertise to diagnose the failure causes, poses significant challenges and costs. To overcome challenges
and minimize expenses, we aim to explore the construction and application of a fault knowledge graph that
integrate different maintenance data and knowledge sources. This involves developing an upper-level ontology
based on the requirements for the fault diagnosis of Cyber Physical Systems. This ontology draws inspiration
from the Industrial Domain Ontology (IDO) and Industrial Ontology Foundry-Maintenance Reference ontology
(IOF-MRO). By leveraging these two ontologies as foundation, we aim to construct a comprehensive framework
that captures and represents fault-related knowledge in a structured manner. Additionally, this paper envisions
the integration of different fault diagnosis methods and knowledge modeling techniques. Combining these
approaches is expected to enhance the accuracy and effectiveness of fault diagnosis, leading to more efficient and
reliable solutions.
Keywords
Fault diagnosis, cyber-physical systems, knowledge representation, knowledge graph
1. Introduction
Machine breakdowns result in a substantial financial burden for manufacturers and their customers,
primarily due to expenses related to training service engineers for fault diagnosis, their salaries, and
the provision of spare parts [1]. Additionally, the capacity of machines is adversely affected, as they
remain out of service during downtime, further increasing costs and impacting productivity. Therefore,
efficient fault diagnosis is of utmost importance for minimizing both downtime and costs. At Canon
and Philips manufactures, service engineers are trained using valuable documentation and occasionally
videos. However, significant challenges raised with this approach. These challenges include heavy
reliance on the engineers’ expertise, the complexity of navigating extensive documentation, and the
high costs associated with producing and providing instructional videos in terms of time and resources.
To address these challenges, the Zorro project has been initiated, focusing on achieving zero downtime
in Cyber-Physical Systems (CPS). Our goal in this project is to support service engineers in diagnosis
the failure cause. Figure 1 shows the proposed framework designed to achieve this goal. The input
comprises various sources that have been identified through interviews conducted with authorities in
the manufacturing field. In the first phase, a Knowledge Graph (KG) is constructed based on the input.
This involves manually developing an upper-level ontology, inspired by IDO and IOF-MRO. In the next
phase, service engineers observe a symptom that need to be converted into a query for the knowledge
SemIIM’24: Third International Workshop on Semantic Industrial Information Modelling, co-located with ISWC’24, 12th November
2024, Baltimore, USA
Envelope-Open a.naghdipour@vu.nl (A. Naghdi Pour); b.b.kruit@vu.nl (B. Kruit); j.y.chen@vu.nl (J. Chen); godfried.webers@philips.com
(G. Webers); peter.kruizinga@cpp.canon (P. Kruizinga); k.s.schlobach@vu.nl (S. Schlobach)
GLOBE https://https://github.com/Ameneh71 (A. Naghdi Pour); https://github.com/bennokr/ (B. Kruit);
https://github.com/JieyingChenChen (J. Chen)
Orcid 0000-0002-7357-7906 (A. Naghdi Pour); 0000-0002-3282-1597 (B. Kruit); 0000-0002-3282-1597 (J. Chen);
0000-0002-3282-1597 (P. Kruizinga); 0000-0002-3282-1597 (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
graph reasoning. The KG then provides a root cause analysis and the corresponding repair procedure
as the output.
In addition to the proposed framework, we have envisioned several methods for fault diagnosis
that can offer significant advantages. Firstly, the automatic generation of Bayesian Networks (BNs)
from KG. The KG excels in data integration, providing a unified view of system faults, while BNs
enable probabilistic reasoning for more accurate diagnosis. This integration allows for a more precise
understanding of fault scenarios and their likelihood, leading to improved diagnostic outcomes. Secondly,
integrating KG with fault tree provides a means to capture complex causal mechanisms. This integration
also facilitates the generation of BNs. Moreover, we envision that KG can be constructed with minimal
effort using Language Models (LLMs). These LLMs can facilitate the integration of different fault
diagnosis representations, such as BNs and Fault Trees, thereby enhancing the robustness of CPS by
minimizing downtime. By leveraging the power of this method, we can work towards achieving zero
downtime and ensuring the continuous operation of CPS in the future.
2. Related work
There are various sources of knowledge and data that can be utilized to support service engineer in
diagnosing failure. These sources include machine log data from sensors, documented knowledge
provided by authorities, logbook data written by service engineers, and also tacit knowledge in the brain
of experts. Researchers have extensively explored different categories of these sources and proposed
various methods for supporting maintenance tasks. These methods can be broadly categorized into
four groups: (1) Model-based approach, which is based on constructing an accurate physical model
that captures the dynamic changes within the system. It diagnoses faults by comparing the actual
measurement output of the system with the predicted output of the model, relying on consistency
monitoring. While this method offers high accuracy, it necessitates the establishment of precise and
quantitative physical models [2]. (2) Signal-based approach, which diagnoses faults by analyzing and
processing monitored signals. However, it can only be used to identify fault types in key components
that are equipped with sensors [3]. (3) Quantitative-knowledge-based method utilize historical data to
train a model and treat fault diagnosis as a pattern recognition problem. However, it does rely on a
significant amount of historical fault data to train the model effectively[4, 5]. (4) Qualitative knowledge-
based method which adopts a different approach by constructing a qualitative model that describes
prior knowledge related to faults. It then utilizes techniques such as searching, matching, and reasoning
to diagnose faults within the system. Unlike the model-based and quantitative-based approaches, this
method does not require the construction of accurate physical models or rely heavily on extensive
historical fault data. Instead, it needs to build a fault knowledge base [6]. Therefore, considering the
requirements of system-level analysis and the need for searching, matching, and reasoning capabilities,
the qualitative-knowledge-based fault diagnosis approach appears to be a suitable choice for this project.
A crucial aspect of qualitative methods is the requirement for a well-defined and reasonable model
that accurately represents knowledge related to faults. The most popular models in the literature
include Rule-Based [7], Causal Models [8], Fault Trees [9], and Petri Nets [10]. Rule-based models use
if-then rules to represent expert knowledge about system behavior and fault conditions, allowing for
automated diagnosis and decision-making under predefined conditions. Causal models diagnose faults
in CPS by mapping cause-and-effect relationships between system components and events. Fault trees
represent different events that can lead to a specific system failure, helping to identify potential causes
and their relationships. Nevertheless, these models have certain limitations. They typically require
prior analysis of potential equipment fault modes and involve manual editing, which makes them
inflexible and challenging to update dynamically. Consequently, the systematic and timely sharing of
maintenance engineers’ experience and expertise in fault diagnosis becomes difficult [6]. Therefore,
this paper proposes to use knowledge graph technology to mine fault knowledge from vast and diverse
fault documents and then construct a structured and interconnected fault knowledge base.
Input Phase 1: Phase 2: Output
Knowledge Graph Diagnosis
Documented
Logbook Data Construction
Knowledge
FMEA
Root Cause
Upper-level Ontology Symptom
Construction Observation
Bill of
Materials
Tacit Knowledge
Repair
Troubleshooting
Querying & Procedure
Manual Information
Reasoning
Extraction
Figure 1: Construction and application framework of domain fault knowledge graph
3. Methodology
Figure 1 shows our proposed framework for fault diagnosis of CPS. The following subsections provide
a detailed illustration of the framework.
3.1. Input Source
Following regular interviews with authorities at Canon and Philips, we have identified various mainte-
nance sources and categorized them into three groups including data, documented knowledge, and tacit
knowledge. Each of these sources offers unique insights and information about the system, but they also
have limitations. Data, which is logbook written by service engineers, contains real-life information
regarding specific problems and actions taken to resolve them. However, the information provided
in the logbook may sometimes be incomplete. For instance, engineers may only write “done” as the
action taken without providing detailed explanations. Documented knowledge encompasses three
resources including (1) Bill Of Material, which provides information about the physical structure of
the system, including the location of each part within assemblies or components, but lacks details on
problems that may happen for each part of the system. (2) Failure Mode Effect Analysis, offers insights
into challenging failure modes and their potential effects, but may not guarantee an accurate and
comprehensive solution for failures. (3) Troubleshooting Manuals, provide insights into the causes of
failures and offer remedies to resolve them. Lastly, tacit knowledge refers to undocumented knowledge
residing in the minds of experts. These different sources are complementary, and relying on just one
of them would not result in an efficient fault diagnosis. By combining them, we can leverage their
strengths and compensate for their limitations.
3.2. Knowledge Graph Construction
The first phase involves two steps to construct the knowledge graph: Upper-level ontology construction
and Information Extraction. The upper-level ontology has been manually created to serve as the
foundational structure for organizing the schema of the knowledge graph. This involved extensive
analysis of various input sources to identify the most valuable knowledge. Additionally, the concepts
and terminology have been carefully selected to ensure clarity and consistency throughout the ontology.
We also drew inspiration from the IDO and IOF-MRO in developing our fault diagnosis ontology. IDO is
a recent work item, approved by the ISO TC 184/SC4 Industrial Data committee in July 2023, and holds
significance in the realm of industrial standardization. IOF-MRO is to support semantic interoperability
through the use of modular ontologies in the maintenance domain. Due to limited space, more detailed
information on the IDO and IOF-MRO models can be found in [11] and [12] respectively. Figure 2
shows the design of the Zorro ontology, where there are classes (nodes) and relationships (edges)
that represent the domain of interest. There is a class named “Component ” that can represent a part,
depensOn subFunction
Function
subComponent hasFunction define hasCause
failVia resultIn
Component Problem Effect
solve address
involve
Solution Workaround
subClassOf
consistOf
Step Procedure
Figure 2: Construction and application framework of domain fault knowledge graph
assembly, or subsystem, each serving a specific “Function ”. When a part encounter malfunction,
it leads to the occurrence of a “Problem ”. The problem has cause(s), which can be considered as
problem, result in a certain “Effect ”. To address these problems, there are two types of “Procedure ”
that can be implemented. The first is a “Solution ”, which directly solves the problem at hand. The
second is a “Workaround ”, which aims to address the effects caused by the problem. Both procedures
consist of multiple “Step ”, with each step involving a specific part of the system. For a thorough
understanding, Table 1 provides a depiction of the classes, their definitions and the sources containing
relevant information about each class, along with an example of the classes in real field. We also
compared our defined classes with the IDO and IOF-MRO. On the other hand, Table 2 illustrates the
object properties, including their definitions and comparison. For the information extraction step,
different techniques such as Regular Expression, Named Entity Recognition, and LLMs, have been
utilized to extracted required knowledge and populate it based on the ontology.
3.3. Diagnosis
The next phase, diagnosis, shows the application of the 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, a diagnosis which find the root cause of the issue along with a procedure should be suggested to
solve the problem. To this end, we are planning to develop querying and reasoning systems for diagnosis,
with the aim of integrating or supporting different fault diagnosis reasoning techniques, such as BNs
and Fault Trees. Crucially, such systems will need to take into account the maintenance engineers’
expert knowledge in order to augment their ability. However, integrating their tacit knowledge into the
diagnosis process is an open research question. We urge the community to engage in this challenge,
leveraging semantic technologies to enhance fault diagnosis and ensure operational resilience.
4. Discussion
The related work section has extensively analyzed the advantages and disadvantages associated with
various fault diagnosis methods and knowledge modeling techniques. Additionally, the rationale for
constructing a knowledge graph as the underlying model to capture and represent prior knowledge has
been explained. However, we believe that the combination of fault diagnosis methods and knowledge
modeling techniques holds great potential for enhancing overall capabilities by leveraging the strengths
Table 1
Classes in the Zorro Ontology
Zorro Definition and Source Close Match Example
Class
Compo- A ’physical object’ of industrial IDO: InAnimatePhysicalObject DC servo mo-
nent equipment that could be a part, a IOF-MRO: MaintainableMateri- tor
component or assembly alItem
Source: BOM, Logbook, FMEA, TM
Function A capability that meets a design IDO: Function Feeding paper
requirement IOF-MRO: MaintainableMateri- to printer
Source: FMEA, TM, Logbook alItemRole
Problem An issue that happened for the IDO: - Overheating
system IOF-MRO: FailedState
Source: Logbook, FMEA, TM
Effect An observable result of the problem IDO: - Wear and tear
on the system IOF-MRO: FailureEffect
Source:FMEA
Procedure Sequence of steps that can be taken IDO: - Check and
to solve the problem IOF-MRO: PlannedProcess clean the
Source: Logbook, FMEA, TM cooling fan
Solution Procedure to directly solve the root IDO: - Check and
cause of the failure IOF-MRO: MaintenanceActivity clean the
Source: Logbook, FMEA, TM cooling fan
Workaround Procedure that addresses the effect IDO: - Replace
of the failure, not the root cause IOF-MRO: SupportingMainte- damaged
Source: Logbook, FMEA, TM nanceActivity components
closer to
motor
Step Single action that need to be in the IDO: - Replace
procedure IOF-MRO: MaintenanceWorkO-
Source: Logbook, FMEA, TM rderRecord
of each approach. We have identified three specific combinations that hold promise in enhancing fault
diagnosis:
1. Automatically generating a Bayesian Network from our knowledge graph: data integration from
various sources is a crucial task for efficient fault diagnosis. While BNs face challenges in data
integration [13], KG excel in this aspect, allowing for a unified and comprehensive view of the
system’s faults. On the other hand, BNs offer a robust framework for probabilistic reasoning,
enabling more accurate diagnosis in the presence of uncertainty. By leveraging the probabilistic
capabilities of BNs, we can enhance the fault diagnosis process, considering the likelihood of
different fault scenarios and their potential impact on the system. This combination harnesses
the strengths of both approaches, enabling a more comprehensive and accurate fault diagnosis
methodology.
2. Integrating our knowledge graph representation with fault tree: The ontology now contains
only simple causal links, but often there is a more complex causal mechanism. Sometimes, faults
only occur when multiple events occur simultaneously, or under specific conditions (such as
humidity). Other times, faults have separate, orthogonal causes. These structures are often
modeled in fault trees. We envision that these structures can also be modeled in a KG, at various
levels of granularity, and that Fault Trees can be generated from them. Additionally, if fault trees
exist that model certain mechanisms, they could be useful sources of information to be linked
and integrated into the KG. Then, their content can be used to generate Bayesian Networks as
described above.
3. Combining model-based with quantitative fault diagnosis methods: In section 2, we have
Table 2
Properties in the Zorro Ontology
Property Definition IDO IOF-MRO
hasFunc- Every part, component, and subsystem of the ma- hasFunc- hasRole
tion chine has a function tion
define If the system does not function properly it defines a create describe
problem
failsVia System fails and initiate a problem - -
hasCause A problem has a cause which the cause itself is a - -
problem
resultIn Problem results in an effect - precedes
address Workaround just addresses the effect of the problem, - -
not the root cause
solve the Solution directly solves the root cause of the prob- - -
lem
subPart Each part may has some subparts contain hasRole
involve Each step of the procedure involves a part of the - -
system
consistsOf Each procedure consist of some steps - Step ”isInputOf”
Procedure
described the drawbacks of these two methods. Both exist on either ends of knowledge-to-data
spectrum: on one end, the model-based diagnosis requires detailed (physical) knowledge of the
system, and on the other end, quantitative fault diagnosis requires comprehensive observation
data to learn from. However, in most cases only a little of either is available, and it is difficult
to integrate the physical models with the observed data. We envision that the extraction of
fault diagnosis knowledge from diverse sources will aid the construction of models that are
both physically informed and data-driven. To this end, the Knowledge Graph facilitates the
integration of various views of historically collected (sensor) data within unified physical, logical
and functional views of the CPS. We envision that this will significantly reduce the efforts needed
to construct accurate models, by re-using modular model components, simplifying the data
analysis pipeline, and allowing efficient validation on historical maintenance data.
5. Conclusion and Future work
In this study, various fault diagnosis approaches were reviewed, and a framework consisting of two
primary phases, Knowledge Graph Construction and Diagnosis, was developed. By constructing a
knowledge graph using diverse input sources, accurate diagnosis and efficient repair procedures can
be achieved. In future work, the focus will be on refining and automating the construction of the
knowledge graph using advanced techniques like LLMs. Additionally, the transformation of KG into
Bayesian Network will be explored to have a probabilistic reasoning. Furthermore, the construction of
model-based and quantitative-based fault diagnosis methods using the knowledge graph will also be
investigated.
Acknowledgments
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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