=Paper=
{{Paper
|id=Vol-2306/paper12
|storemode=property
|title=Semantic Technologies for the Modeling of Condition Monitoring Knowledge in the Framework of Industry 4.0
|pdfUrl=https://ceur-ws.org/Vol-2306/paper12.pdf
|volume=Vol-2306
|authors=Qiushi Cao
|dblpUrl=https://dblp.org/rec/conf/ekaw/Cao18
}}
==Semantic Technologies for the Modeling of Condition Monitoring Knowledge in the Framework of Industry 4.0==
https://ceur-ws.org/Vol-2306/paper12.pdf
Semantic Technologies for the Modeling of
Condition Monitoring Knowledge in the
Framework of Industry 4.0
Qiushi Cao[0000−0002−5858−0680]
Normandie Université, INSA Rouen, LITIS, 76000 Saint-Étienne-du-Rouvray, France
qiushi.cao@insa-rouen.fr
Abstract. Following the trend of Industry 4.0, the demand for high
productivity and availability of manufacturing processes has triggered
the tendency of automation in various environments. The automation in
different manufacturing processes and activities has given opportunities
to the use of intelligent condition monitoring systems, which have been
applied to many subdomains in manufacturing to improve productivity
and availability of production systems. To develop such an intelligent sys-
tem, semantic interoperability among different system components and
system users is a critical issue. For this reason, semantic technologies are
of paramount importance. In this paper, we present our proposal for the
formal representation of condition monitoring knowledge using semantic
technologies. The proposal is based on an ontological framework which
consists of a core reference ontology for representing generic condition
monitoring concepts and relations, and a domain ontology for formalizing
manufacturing domain-specific knowledge. Based on the proposed onto-
logical framework, we will develop an intelligent condition monitoring
system which will be capable of detecting faulty conditions in machines,
machine tools, and manufacturing processes, and providing appropriate
decision support for tasks such as fault prognostics, diagnosis and pre-
ventive maintenance.
Keywords: Industry 4.0 · Condition monitoring · Manufacturing pro-
cess · Availability · Fault prognostics · Semantic technology · Ontology.
1 Introduction
Industry 4.0 is an inter-disciplinary effort to inter-connect all resource of a fac-
tory and the factory itself with the Internet to build a smart factory. The tools,
machines, workstations, and human operators are all interconnected to facili-
tate the traceability of processes, the adaptive and flexible control of production
machines, and the real-time and decentralized reactions to unexpected events.
Following the vision of Industry 4.0, the manufacturing industry today is ben-
efiting from a trend of automation in data exchange. The automatic exchange
and analysis of data open up opportunities for manufacturers to further op-
timize the production processes. Collecting data from various components of
2 Qiushi Cao
a production line and analyzing them in a scalable Cloud infrastructure can
significantly improve the productivity, reliability, and availability of production
systems in heterogeneous environments [1]. However, the utilization of these ad-
vanced technologies not only offer the aforementioned benefits to manufactures
but also brings them challenges such as the management of a large amount of
data generated by networked machines and sensors.
The management of big data is considered as a challenging task in the context
of condition monitoring (CM) [1]. The objective of CM is to determine the cor-
rectness of the operating states of physical assets and manufacturing processes.
Normally, when a propensity of machinery fault or failure is detected, highly
experienced machine operators are capable of performing appropriate actions to
prevent the outage situation of the production system. However, as the structure
and behavior of production systems are getting more and more complex, the vol-
ume of machine operating data grows significantly. Thus it is possible that the
domain professionals fail to respond to a machinery fault or failure timely and
accurately. For this reason, manufacturing companies are searching for solutions
through which they can manage this big data efficiently and perform prognostics
tasks intelligently. To this end, the utilization of intelligent condition monitoring
systems (ICMSs) is a promising approach [2].
To develop such an ICMS, semantic interoperability among different sys-
tem components and system users is a critical issue. Since the data collected
by the ICMSs come from heterogeneous data sources, the “meaning” of these
data varies according to different contexts and domains, thus making it difficult
to be harmonized. To deal with this challenge, shared, rigorous and machine
understandable vocabularies with robust structures are needed. In this context,
semantic technologies, especially ontologies, appear as good candidates to cope
with the semantic interoperability problem. An ontology is a formal represen-
tation of certain domain knowledge, which computationally captures and struc-
tures domain concepts and relationships [3]. The use of ontologies can ensure
the consistency of semantics, thus providing a shared understanding of knowl-
edge among different participants within a domain. However, in the CM domain,
most of the existing ontologies are only designed to represent a specific portion
of domain knowledge of CM and lack the formal representation of manufacturing
concepts. Thus, their domain coverage and scope are limited. In this context,
there is a need for an ontology which provides a comprehensive representation
of knowledge in both CM and manufacturing domains.
This paper introduces in detail a proposal for the development of an ICMS.
The development work starts with a formal representation of domain knowledge
that is related to CM tasks performed upon manufacturing processes. An on-
tological framework which consists of a core reference ontology for representing
generic CM concepts and relationships and a domain ontology for formalizing
manufacturing domain-specific knowledge is presented. Based on the proposed
ontological framework, ontology reasoning techniques are adopted for facilitating
decision making related to the fault and failure detection in machines, machine
tools, and manufacturing processes. The detection of machine fault and fail-
Semantic Technologies for the Modeling of Condition Monitoring Knowledge 3
ure enables the supervision of optimal preventive maintenance activities, which
aims to guarantee high availability of manufacturing processes. This Ph.D. work
is under the frame of the HALFBACK Project1 , and it is at the early stage.
2 State of the Art
In recent years, several systems and software have been developed to facilitate
the automation of condition monitoring tasks on machines and machine tools.
In [4], an automatic condition monitoring system for crack detection of rotating
machinery is introduced. The authors process the vibration signals of cracks in
a rotating shaft by combining the Wavelet Packets transform energy with Arti-
ficial Neural Networks. The proposed system is able to automate the diagnosis
process without human interventions. However, the developed system is only
capable of detecting crack effects, not other defects such as assembly errors or
temperature anomalies. For the condition monitoring of cutting tools, an auto-
matic detector based on vibratory analysis is demonstrated in [6]. In their work,
the authors obtained vibratory signatures produced by a turning process, and
the mean power of vibratory signatures is identified as the main indicator of the
monitored cutting tool. However, the system is developed merely for the eval-
uation of cutting tool states, and it is not capable of providing prognostic and
preventive maintenance decisions, based on the collected vibration signals. An
automatic system for detecting wheel defects of rail vehicles is presented in [5].
The system performs analysis of wheel surface defects based on high-quality im-
ages of the wheel treads and flanges, and it has been tested for its usability and
functionality by being used in an operational railway site. The main limitation
of this system is the missing functionality of providing warning signals, such as
alert and alarm. This limitation hinders the launching of maintenance activities.
As the manufacturing domain becomes more dynamic and knowledge-intensive,
using ontologies to formally represent the knowledge of CM and manufacturing
turns out to be a notable research topic [7]. The incorporation of ontologies
to support decision making of condition monitoring in many domains has been
a promising approach to improve the availability of manufacturing processes.
During the last decades, several ontologies and ontological models are devel-
oped under different contexts and domains for the goal of facilitating knowledge
formalization, sharing and reuse. In this section, we also review the existing
ontologies and ontological models that are relevant to our work. The review is
carried out according to two aspects: (i) ontologies that model the manufacturing
domain; and (ii) ontologies that model the CM domain.
For the first category of ontologies, the Process Specification Language (PSL)
ontology [8] is one of the early-stage contributions. This ontology explicitly spec-
ifies the terminologies for representing manufacturing activities. These termi-
nologies model the key elements of process scheduling, process modeling, pro-
cess planning, production planning, and project management. The Manufactur-
ing Service Description Language (MSDL) ontology [7] aims to describe a wide
1
http://halfback.in.hs-furtwangen.de
4 Qiushi Cao
range of manufacturing services. This ontology incorporates the formalization of
manufacturing processes, machine components, and machine tool capabilities.
Based on the formalization of manufacturing-related knowledge, various manu-
facturing processes such as cutting, milling, and feed motions are expressively
described. The MAnufacturings Semantics ONtology (MASON) [9] is another ex-
ample which drafts the generic and common manufacturing concepts with their
interrelationships. MASON is built upon three head concepts: Entities, Opera-
tions and Resources. Among them, Entities gives an abstract view of manufac-
turing products, Operations describes a wide range of manufacturing processes,
and Resources covers all the resources that are related to manufacturing.
The second category of ontologies are normally designed for facilitating the
tasks of fault or failure prognostics and machine health monitoring (PHM). The
OntoProg Ontology [10] is a notable contribution which addresses the PHM issue
of machines in smart manufacturing. The ontology is developed based on a set of
international standards, from which the formal terminologies for constructing a
PHM architecture are extracted. The Sensing System Ontology [11] is proposed
to define the embedded sensing systems for industrial Product-Service Systems
(PSSs). The ontology provides a comprehensive description of sensors embedded
on PSSs, with the goal of monitoring machine health.
After reviewing the ontologies that are designed for either CM or manu-
facturing, we discover that none of them provides satisfactory knowledge rep-
resentation of both domains. For example, some ontologies tend to focus on a
narrow field, such as the product domain, and they do not formalize condition
monitoring-related concepts, e.g. Failure, Fault and Error. Also, none of the
existing ontologies provide knowledge representation of the concepts Warning
Signal in maintenance tasks, e.g. Alert and Alarm, and also the relationships
between them. To perform a CM task on a piece of machinery, the knowledge
base of an ICMS should incorporate not only the machine-interpretable knowl-
edge for characterizing the manufacturing entities or processes which are being
monitored but also the knowledge about fault or failure detection and prognos-
tics. To this end, in this Ph.D. project, we present our proposal for the formal
representation of both manufacturing and CM domain knowledge using semantic
technologies. In more detail, we will (i) propose an ontological framework that
encompasses expressive knowledge representations for both CM and manufac-
turing domains; and (ii) develop an ICMS for fault prognostics, diagnosis and
preventive maintenance of production lines.
3 Proposed Approach
The ontological framework is developed through a Middle-Out approach, which
is a combination of the Top-Down and Bottom-Up approaches [12]. For the
Top-Down approach, a set of international standards and textbooks are used
to extract concepts from a general point of view, while for the Bottom-Up
approach, existing domain ontologies are analyzed exhaustively for providing
domain-specific knowledge. The proposed ontological framework consists of a
Semantic Technologies for the Modeling of Condition Monitoring Knowledge 5
core reference ontology for representing generic CM concepts and relationships,
and a domain ontology for formalizing manufacturing domain-specific knowl-
edge. Figure 1 shows the three-layered ontological framework.
Fig. 1. The proposed ontological framework.
The development of the ontological framework starts with the choice of a
foundational ontology which define general and basic notions across a wide range
of domains. The reuse of a foundational ontology enables the integration of other
ontologies that represents more specific domain concepts and relationships. In
our work, we adopted the Unified Foundational Ontology (UFO) [13], as it pro-
vides rigorous and expressive representation of general concepts and relation-
ships. The core ontology we developed is aligned to the UFO ontology, to ensure
a rigorous conceptualization. The UFO ontology is at the top layer of our onto-
logical framework.
For the middle layer, we develope the core reference ontology for condition
monitoring, named CM-core. According to [14], core reference ontologies are
built within the scope of a domain. Normally, they catch central concepts and
relationships of a domain and are considered as an integration of several domain
ontologies. During the development phase, we reuse some existing ontologies
such as the Semantic Sensor Network (SSN) Ontology [15], PSL Ontology and
SWRL Time Ontology [16].
At the bottom layer, a domain ontology called Manufacturing CM Ontology
is developed to formalize domain knowledge that is related to condition monitor-
ing tasks performed upon manufacturing processes. To enhance the reusability
and extensibility of the Manufacturing CM Ontology, we followed the ontology
partitioning and module extraction approaches introduced in [17], and struc-
tured our ontology into three modules: the Manufacturing Module, the Context
6 Qiushi Cao
Module, and the Condition Monitoring Module. A set of domain ontologies are
reused in this step, including the MSDL ontology, MASON ontology, .etc.
To evaluate and validate our domain ontology, which is used as the main
ontology for performing ontological reasoning tasks, we use a web-based ontol-
ogy validation tool named OOPS!2 for detecting potential errors in the ontology.
The proposed ontology is evaluated by OOPS! according to three dimensions: (i)
Structural dimension, (ii) Functional dimension, and (iii) Usability-Profiling di-
mension. To examine the robustness, fidelity, and quality of the ontology, we also
integrate the evaluation conducted by domain experts, for checking its usability
for specific domain tasks.
In the near future, we are going to specialize the Manufacturing CM Ontology
into a more specific domain ontology, named the Production Line CM Ontology,
for representing specific concepts and relationships of manufacturing production
lines.
4 Preliminary Results and Future Work
Based on the three-layered ontological framework, rule-based reasoning tasks is
performed to propose decision makings about machinery fault and failure predic-
tion. We instantiated the Manufacturing CM Ontology with different examples
of machinery in production lines, and then proposed SWRL rules3 to infer about
the correctness of the machinery operating states. Figure 2 shows two example
rules, in which the first rule reasons whether a bearing experiences the state “in-
ner race defect” (Dir). The second rule reasons whether a bearing has a minor
error. The SWRL rules are extracted from real experiments about machinery
defects identification. The results of rule-based reasoning tasks shows that the
ontology could be used for the reasoning of machinery operation conditions. The
reasoning capabilities of SWRL rules allowed the condition monitoring tasks
such as machinery state identification and error detection to be accomplished.
In the near future, we plan to improve the performance of the logical in-
ference tasks by applying a fuzzy semantic approach. The rules we mentioned
in the preceding paragraph are based on crisp logic, which may fail to partition
numeric values when the values are considerably close to the partition threshold.
To deal with this kind of uncertainty situations, a fuzzy approach needs to be
implemented. This approach will use fuzzy rules to enhance the representation
of imprecise severity level of machinery faults, errors, or failures. For example,
an identification of an error will be associated with a fuzzy index, indicating the
grade of its membership to a minor or medium-level error. The fuzzy seman-
tic approach will be applied to tackle the symbol anchoring problems [18], thus
facilitating the condition monitoring tasks of manufacturing processes.
Another direction of future work aims to involve the joint utilization of ma-
chine learning techniques with semantic technologies. To facilitate the prognostic
2
http://oops.linkeddata.es/response-advanced.jsp
3
https://www.w3.org/Submission/SWRL/.
Semantic Technologies for the Modeling of Condition Monitoring Knowledge 7
Fig. 2. Examples of proposed SWRL rules.
results of faults and failures in manufacturing processes, machine learning tech-
niques, especially big data algorithms will be used to mine the collected data in
smart factories. Big data algorithms will use the collected data to understand
the manufacturing processes and to learn from the experience of the operators,
through which a set of predictive rules will be extracted and then used jointly
with the ontology for predicting machine damage, quality loss or maintenance
demands in the future.
Acknowledgements
I would like to thank my supervisors, Dr. Cecilia Zanni-Merk, Dr. Christoph
Reich, and Dr. François de Bertrand de Beuvron for their guidance and super-
vision during the PhD work. My work has received funding from INTERREG
Upper Rhine (European Regional Development Fund) and the Ministries for Re-
search of Baden-Wrttemberg, Rheinland-Pfalz (Germany) and from the Grand
Est French Region in the framework of the Science Offensive Upper Rhine HALF-
BACK project.
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