=Paper=
{{Paper
|id=Vol-3647/SemIIM2023_paper_13
|storemode=property
|title=Towards Standardised Product Data Exchange with Information Modelling Framework at Siemens Energy
|pdfUrl=https://ceur-ws.org/Vol-3647/SemIIM2023_paper_13.pdf
|volume=Vol-3647
|authors=Maja Miličić Brandt,Foivos Psarommatis,Christophe Simon,Arild Waaler,Baifan Zhou
|dblpUrl=https://dblp.org/rec/conf/semiim/BrandtPSWZ23
}}
==Towards Standardised Product Data Exchange with Information Modelling Framework at Siemens Energy==
https://ceur-ws.org/Vol-3647/SemIIM2023_paper_13.pdf
Towards Standardised Product Data Exchange with
Information Modelling Framework at Siemens Energy
Maja Miličić Brandt1,* , Foivos Psarommatis2 , Christophe Simon3 , Arild Waaler2 and
Baifan Zhou4,2
1
Siemens AG, Germany
2
Department of Informatics, University of Oslo, Norway
3
Siemens Energy, Germany
4
Department of Computer Science, Oslo Metropolitan University, Norway
Abstract
Business processes in a broad range of industries along the value-chain and product life-cycle suffer
from significant challenges pertaining to data quality. These challenges include unknown data validity
and tolerance ranges, inconsistency across datasets, data incompleteness, and inaccessibility due to
data silos. To address these challenges, we need standardised product data exchange between different
business units along the value-chain and product life-cycle. Existing modelling languages such as OWL
are expressive, but not sufficiently usable for non-semantic experts, while typically these experts (e.g.,
engineers) possess the essential knowledge for creating the data models. To this end, we develop an
approach named as Information Modelling Framework (IMF) that aims at user-friendly modelling for
engineers. This paper presents our ongoing research of the IMF approach and exemplify its usage with a
real business case at Siemens Energy.
Keywords
information modelling, standardised data exchange, system engineering
1. Introduction
Motivation. In a broad range of industries, including manufacturing, process, etc. the busi-
ness processes along the value-chain and product life-cycle suffer from significant challenges
pertaining to data quality [1, 2]. These challenges include pervasive issues such as unknown
data validity and tolerance ranges, inconsistency across datasets, data incompleteness, and
inaccessibility due to data silos [3]. The data quality issues have far-reaching implications,
affecting critical aspects of operations, decision-making, and efficiency [4]. There is a pressing
need to address these data quality concerns by establishing standardised protocols for prod-
uct data exchange [3]. At Siemens and Siemens Energy, we endeavour to contribute to the
resolution of these challenges by exploring and proposing innovative solutions for semantic
SemIIM’23: 2nd International Workshop on Semantic Industrial Information Modelling, 7th November 2023, Athens,
Greece, co-located with 22nd International Semantic Web Conference (ISWC 2023)
*
The authors are ordered alphabetically by the last names.
$ maja.milicicbrandt@siemens.com (M. Miličić Brandt); foivosp@ifi.uio.no (F. Psarommatis);
christophe.jt.simon@siemens-energy.com (C. Simon); arild@ifi.uio.no (A. Waaler); baifanz@ifi.uio.no (B. Zhou)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
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Workshop ISSN 1613-0073
Proceedings
Requirements Proposal Order-Execution Operation Decom-
missioning
Data: unstructured data, Data: company-
Production
e.g., extensive and specific formats: Data: A fraction of proposals gets an order.
documents,
Mode
heterogenous customer with a higher amount and richer documentation. Re-
requirements in data datasheets, pin-
sheets, documents, ids/comments, cycling
and standards, etc. Service
laws/regulations
Procurement
Mode
supply chain/ contractor/
data from suppliers Com-
Challenges: collisions Detailed Logis-
missioni Dis-
interpretations Design tics Main-
Manufacturing ng posing
uncertain specification Basic Design
tolerance classes/quality assurance tenance
Mode
Figure 1: Sketch of activities carried out by different business units along the value-chain and product
life-cycle. Note that the figure is a simplified overview.
industrial information modelling, ultimately aiming to enhance data quality, accessibility, and
interoperability within Siemens Energy and similar industrial contexts [5].
Challenges. In particular, we consider the following challenges:
• C1, Consolidation challenge to address semantic ambiguity and data transformation. The
exchange of technical information frequently occurs without standardised semantics and
consistent data structures. This process often relies on unstructured documents or datasheets,
both in inter-company transactions involving clients and suppliers and within internal teams.
The result is a multitude of different data formats that require unification for effective com-
munication.
• C2, Provenance challenge that results from loss of information along the extended value-chain.
Across the extended value-chain (Fig. 1), valuable technical information can become frag-
mented and scattered. Multiple copies of the same data fields proliferate, and numerous
intermediate steps introduce complexity and potential for data loss. This lack of transparency
and data quality hinders operational efficiency and effectiveness.
• C3, Automation challenge in ensuring data integrity. Current practices often necessitate
manual verification of requirements, a process prone to errors. This manual approach not
only consumes valuable resources but also raises concerns about data integrity and accuracy.
An automated solution is needed to enhance the reliability and consistency of requirement
verification processes.
Requirements. Our goal is to develop an approach to achieve standardised product data
exchange between the different units in the business process and value-chain. Based on the
industrial/business needs, the requirements include the following points:
• R1, Standardised/shared: the approach should have standardised vocabulary and information
structure with a good coverage and a process of extending it;
• R2, Machine-processable: the approach should have automatable information verification
mechanism to ensure that the info satisfies a set of specifications;
• R3, Precise: the approach should use unambiguous/precise languages, with minimal redun-
dancy, be clear cross references and well-organised;
• R4, User-friendly: the approach should be easy to use for engineers (non-semantic experts);
• R5, Good interface/modularised: to use the information modules, users only need to understand
the modules, and do not need to understand the whole content;
Cooling
system
Pumping Interaction Heat
system exchanger
Pump
Electric
Pipe
drive
Figure 2: System thinking: breakdown, granularity, and interaction.
• R6, Flexible/versatile: the approach should accommodate a broad range of needs/interests;
• R7, Provenance traceable: the approach should be table to track where the information comes
from, to have clear provenance along the information propagation chain.
Existing Approaches. The existing approaches can be categorised largely into two groups.
One group is intended for users who are experts in information modelling, such as OWL [6],
SHACL [7], etc. These approaches are standardised, machine-processable, precise, and flexible,
but they require extensive training before the users can start to be productive (not meeting
R4). The other group includes tools intended for engineers, such as COMOS [8], but more work
needs to be done in reaching agreement between organisations and standards (R1), and they
are by large not machine-processable (R2) and sufficiently precise (R3).
This paper presents our ongoing research on standardisation of product data exchange
between different business units along the value-chain and life-cycle. We rely on the approach
of Information Modelling Framework (IMF), which is being developed as an engineering friendly
method to model engineered systems and assets. We aim at using IMF models as the standardised
data exchange format, and present a gearbox use case at Siemens Energy as preliminary results
to exemplify our approach. This new approach has the rigour of logic-based formal languages
while being user-friendly enough for engineers, thus bridging the gap between the existing
approaches.
2. Approach: Information Modelling Framework
Approach Guidelines. We rely on the approach of Information Modelling Framework (IMF)
[3]. IMF is being developed as an engineering friendly method to model engineered systems
and assets. IMF aims to bridge the gap between ontologies and industrial system data by:
• Implementing ISO/IEC 81346-1 in RDF [9];
• Explicitly representing Aspects (function, product, location) of the asset along its life-cycle [9];
a b (Product)
Artefacts:
Location: Cooling pumps,
position System pipes,
of system chillers
System Block:
elements
the boundary of a system
element
+ +
Terminal (points that indicate input Three aspects
to/output from system boundary) Activity: cooling
(Function)
Figure 3: (a) The two graphical elements in IMF: System Block (or Block for short) and Terminal. (b)
Three Aspects in IMF modelling.
• Using a limited vocabulary to express engineered systems;
• Alignment with Industrial Data Ontology (former ISO 15926-14) [10].
System Thinking. To model engineered systems and assets, we first need some basic concepts
of system thinking. For simplicity, we brief these concepts in informal descriptions and take the
example of a cooling system (Fig. 2).
• System and system elements. A system is a group of system elements to achieve a purpose,
where a system element is a system by itself. For example, a cooling system achieves the
purpose of the cooling function, that is to reduce the temperature of e.g., an physical object.
• Breakdown and granularity. A system can be broken down to system elements, and this
breakdown can be continued to a granularity that is needed. The cooling system can be broken
down input a pumping system and a heat exchanger, where the pumping system can be
further broken down into pumps, which can be broken down into pipes and electric drives.
This breakdown goes to a granularity that is needed.
• Interaction. The system elements can interact with other system elements. For example, the
pumping system interact with the heat exchanger via pipes and liquid in the pipes.
IMF Modelling Basics. With the system thinking as the background knowledge, we shortly
introduce some IMF modelling principles and refer the readers to the complete manual for
details [11]. To model an engineered system in IMF, is to describe the system with connected
graphical elements in three Aspects. The syntax is introduced informally as below: 1
• Graphical elements. Aiming a simplistic modelling language for non-semantic experts, espe-
cially engineers, IMF provides two language elements, system block and terminal (there is
other language element interface point, which is not in focus of in the paper). These language
elements are also called graphical elements, because they have graphical representation as
shown in Fig. 3a.
1
This paper aims at a minimal description of the IMF modelling principles that is needed for understanding the
gearbox case. We thus significantly simplified the IMF modelling principles. The simplified principles still align
with the full-extent principles.
imf:fulfilledBy
Gas Compressor Train Gas Compressing
imf:ProductBlock
imf:ProductTerminal
GasCompressor GearboxAssembly lis:hasPart
imf:connectedTo
Casing Gearbox Shaft Driven Gear Turning
Gearbox LowNoiseHood
Winterisation Soleplate Oil Pump Device
Input Shaft Output Shaft Input Shaft Junction
Coupling
System System System Box
Figure 4: The Product Aspect (excerpt) of the IMF model for the gearbox case
• Three Aspects. As in ISO/IEC 81346-1 in RDF [9], a system can be viewed from three Aspects:
function, product, and location (Fig. 3b). These are three perspectives of viewing a system:
– the Function Aspect of a system is viewing a system from the perspective of activity, because
the function of a system is the potential of the system to realise an activity (the cooling
activity in example);
– the Product Aspect of a system is viewing a system from the perspective of artefact, because
a system consists of a set of physical artefacts (such as pumps, pipes, chillers);
– the Location Aspect of a system is viewing a system from the perspective of location, which
is the (geographical) positions and spatial extension of system and system elements.
• Relations. The graphical elements are connected via a set of relations. Here we brief common
relations and their domains and ranges that are needed for this paper:
– lis:hasPart. This relation connects (has domain and range of) Blocks of the same Aspect,
or Terminals of the same Aspect. The relation is used to represent the breakdown of a system.
The relation is from Industrial Data Ontology (IDO for short, former ISO 15926-14) [10] (lis
is the namespace of IDO).
– imf:connectedTo. This relation has both domain and range of Terminals of the same
Aspect. The relation is used to represent the interaction of a system.
– imf:fulfilledBy. This relation has domain of Function Block and range of Product Block,
or domain of Function Terminal and range of Product Terminal.
Shared Semantics. IMF aims at semantics shared among the stakeholders. These include three
components:
• Semantics of information models: Semantics of objects, relations, and attributes is defined in
shared IMF models and ontologies, between the stakeholders, such as clients, Siemens Energy,
suppliers, etc.
• Semantics of attributes: These also include standardised specifications, such as attribute
qualifiers, description of a product life-cycle and milestones, solution documentation
• Semantics of data validation: Reasoning and data validation are done using OWL reasoners
and SHACL engines.
imf:FunctionBlock
Gas Compressing lis:hasPart
imf:connectedTo
Centrifugal Mechanical Mechanical Housing/ Electrical
Compressing Driving Transmitting Protection Connection
Mechanical System
Process Gas
Speed Changing Lubrication Noise Reducing Winterisation
Cooling
/Cooling
Moment Moment
MomentInput OilPumping OilDistribution Lubricating OilCooling Insulation Casing
Changing Transferring
Figure 5: The Function Aspect (excerpt) of the IMF model for the gearbox case
3. Gearbox Use Case
A gearbox is a common component of a gas compressor train that is interconnected with many
other components. The gearboxes are normally supplied by external suppliers and incorporated
into solutions provided by Siemens Energy. This section presents the modelling of a gearbox
in the gas compressor with IMF, aiming at standardised data exchange with the IMF models.
We discuss the gearbox as a system element in a gas compressor train. The IMF models of a
gearbox includes Function Aspect, Product Aspect, and Location Aspect.
Product Aspect. To ease the understanding, we start with the Product Aspect, which describes
the breakdown and interaction of the physical artefacts of the system (Fig. 4).
• Gas Compressor Train Block. It is the root node of the IMF model in the Product Aspect. A gas
compressor train is often referred to as a compressor package or gas compression package. It
is a complex mechanical system used in various industries to compress natural gas or other
gases. The gas compressor train is the overall artefact that fulfils the function of compressing
the incoming gas. The Gas Compressor Train Block has many parts, such as the Gas Compressor
Block and the Gear Box Assembly Block.
• Gas Compressor Block. The gas compressor is the central component of the gas compressor
train that fulfils the primary function of compressing the incoming gas.
• Gearbox Assembly Block. The gearbox assembly is a critical subsystem that efficiently trans-
mits power from the driver, typically an electric motor or a gas turbine, to the compressor
and other auxiliary components. This assembly ensures that the compressor operates at the
correct speed and torque, optimising the compression process. Gearbox Assembly Block has
further six parts. For conciseness, we omit these details.
• Coupling Block and connections between the terminals. In Fig. 4, we show the Terminals and
the imf:connectedTo between the Terminals partially. These connections indicate that the
input shaft system of the gas compressor is connected to the coupling via some media (here
force), and the coupling is connected to the output shaft system of the gearbox via media.
Function Aspect. The Gas Compression Function Block is fulfilled by the Gas Compressor Train
Site
Area
Building
imf:LocationBlock
Gas Compressor lis:hasPart
Train imf:connectedTo
Gas Compressor
Gearbox Assembly Gas Compressor
House
Figure 6: The Location Aspect (excerpt) of the IMF model for the gearbox case.
Product Block. We illustrate the IMF model for the system from the function aspect in Fig. 5.
• Gas Compression Block. It is the root node of the IMF model in the Function Aspect, which
describes the primary and overarching function of a gas compressor, that is to compress gas.
This compression process reduces the volume of the gas while increasing its pressure. This
function can be achieved if all of its sub-functions are achieved.
• Centrifugal Compressing Block is one of the sub-functions, which describes the (potential of)
the activity that the compressor uses the mechanical energy to increase the gas pressure.
• Mechanical Driving Block describes the (potential of) the activity to provide the mechanical
energy.
• Housing/Protection Block describes the (potential of) the activity to provide a sturdy and
protective casing or enclosure for the compressor’s internal components. It is an activity
because it spans over time.
• Electrical Connection Block describes the (potential of) the activity to establish the necessary
wiring and connections for the control system to interact with the compressor.
• These sub-functions can be achieved if their sub-functions are achieved. For conciseness, we
omit further details.
Location Aspect. The Location Aspect is the description of installation positions (intended or
actual) at the customer site and the product spatial extension.
• The first three blocks always exist for all engineered systems. Site Block describes the
geographical position and spacial extension of the overall site where the gas compressor
operates. Area Block describes a part of the site, and Building Block describes the building,
which is a part of the area.
• The following blocks describe the gas compress train from the location aspect. The Gas
Compressor Train Location Block has three parts, which is the Gearbox Assembly Block, Gas
Compressor House Block, and the Gas Compressor Block. These blocks contain attributes of
the position and spacial extension of each physical artefacts.
Summary. The three figures (Fig. 4 - Fig. 6) are to exemplify how to describe engineered systems
with the IMF models. We could see that despite the very limited allowed vocabulary (graphical
elements, aspects, and relations), very complex engineering systems from real business case can
be described. In addition, because of the limited vocabulary, representations of such systems
in IMF models are easy and intuitive for engineers to understand, and close to the way they
think. After having the blocks and terminals, the attributes with specified values and ranges are
attached to the blocks and terminals, organising most of the essential information of engineering
systems.
4. Conclusion and Outlook
Conclusion. With good data quality and standardised data models, we could reduce non-
conformance cost, improve productivity (less time spent on search and confirmation) in projects,
increase application and data re-use in IT and administration, shorten cycle time of projects,
and improve reaction times to third parties. IMF aims at such standardised data models, and
the “lingua franca” between organisations, business units along the product life-cycle and
value-chain. This work is a step towards a grand goal, and provides preliminary guidelines and
experience for future investigation and industrial practice.
Limitations. Looking at the challenges C1-C3, we can see that C1 is partially addressed by the
presented IMF approach, while C2-C3 are not touched. Regarding the requirements, we can see
that R1, R3, R4, R5, R6 are partially fulfilled by the IMF approach, while R2 and R7 require future
research. C3 and R2 require translating the IMF models into some well-defined languages, such
as OWL and SHACL. C2 and R7 require more research on tracking the information drift along
the product life-cycle and value-chain, especially tracking the attributes changing, because
preserving information across the attribute life-cycle is essential in quality assurance.
Outlook. As future work, we see the following directions:
• semantic interpretation that translate the IMF models to some well-defined language such as
OWL and SHCAL;
• semantic verification that automatically validates the data integrity;
• software engineering that provides different pieces of tooling and end-to-end tooling for IMF
modelling, including Mimir, Tyle (by Equinor) as starting point as user interface, and Domain
Ontology Editor (by Siemens Energy) for ontology administration;
• alignment with various standards, we need shared ontologies mapped to various standards,
such as CFIHOS, AAS, etc.
• An ecosystem of industrial information modelling is being built across many organisations.
Contributions by Authors
Storyline: Maja. Motivation, challenges and requirements: Christophe, Maja, Arild, Baifan and
Foivos. IMF approach: Arild, Baifan, Foivos, Maja. Gearbox case: Christophe. Figures: Baifan,
Maja, Christophe, Foivos. Writing: Baifan, Maja, Christophe, Foivos, Arild.
Acknowledgments
This publication is supported by the European Commission H2020 with the project number
958371 (OntoCommons) and by the Norwegian Research Council with project number 237898
(SFI SIRIUS), 308817 (DigiWell).
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