=Paper=
{{Paper
|id=Vol-3647/SemIIM2023_paper_7
|storemode=property
|title=SHM: A Light-weight, Mid-level Ontology for Reliable System Health Monitoring
|pdfUrl=https://ceur-ws.org/Vol-3647/SemIIM2023_paper_7.pdf
|volume=Vol-3647
|authors=Eleni Tsalapati,Manolis Koubarakis
|dblpUrl=https://dblp.org/rec/conf/semiim/TsalapatiK23
}}
==SHM: A Light-weight, Mid-level Ontology for Reliable System Health Monitoring==
https://ceur-ws.org/Vol-3647/SemIIM2023_paper_7.pdf
SHM: A Light-weight, Mid-level Ontology for Reliable
System Health Monitoring
Eleni Tsalapati1,* , Manolis Koubarakis1
1
Dept. of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece
Abstract
Early fault diagnosis plays a crucial role in maintaining the health and safe operation of equipment.
However, only a select few of the current diagnostic tools are of high genericity, reliability and can
perform efficiently on-line at the same time. Furthermore, there is only limited research identifying
both system faults and sensor faults simultaneously. Semantic-based technologies can offer a holistic
view of monitored systems and their operation, providing in this way better insights and enhancing
decision support. Towards this goal, in this research work, we present the light-weight mid-level System
Health Monitoring (SHM) ontology. The SHM ontology models the health state of a system at each time
instance based on sensor outputs and calculated values from these outputs, along with their reliability
rates. The requirements specification for the development of the SHM ontology has been based on
domain experts’ competency questions (from two different application areas), and the ontology has been
evaluated against these questions.
Keywords
Ontology, System Health Monitoring, Fault Detection, Reliability
1. Introduction
With the rapid development of industry, early fault diagnosis plays a crucial role in maintaining
the health and safe operation of equipment. Semantic-based fault diagnosis approaches can offer
a holistic view of monitored systems and their operation, enhancing their capability to maximise
their lifetime potential. Semantic technologies allow seamless integration of the structural
(static) and operating (streaming) data of a system and its ancillary systems (e.g., sensors) along
with the formal representation of expert’s knowledge in a human and machine processible form.
This way, neuro-symbolic technologies combining neural networks and symbolic AI approaches
(e.g., ontologies and logics) can be applied to extract useful insights with regard to the health
state of the system enabling robust early diagnosis and decision support.
In the last few years numerous studies have appeared in the literature about semantic-based
health monitoring; prominent examples include [1, 2, 3, 4, 5]. To the best of our knowledge, only
[5] proposes, and has openly available, a mid-level ontology for system health monitoring, the
Context Ontology for Industry 4.0 (COInd4). COInd4 represents the elements of a real factory,
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)
*
Corresponding author.
$ etsalapati@di.uoa.gr (E. Tsalapati); koubarak@di.uoa.gr (M. Koubarakis)
0000-0001-9464-404X (E. Tsalapati); 0000-0002-1954-8338 (M. Koubarakis)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Proceedings
such as machines, processes and sensors (reusing the well-known SOSA/SSN ontology [6]),
but by mostly focusing on modeling the context (i.e., the situation) of operation of these
elements. Sensor failure, which happens very often in system monitoring (e.g., [3]), is not
captured by COInd4. Additionally, COInd4 considers only sensor results, while in the diagnostic
process, calculated values from simultaneous outputs from multiple sensors may also be required
(e.g., [1, 3]).
In this short paper, we present the System Health Monitoring (SHM) ontology. SHM is a
lightweight, mid-level ontology for the representation of the health condition of an operating
system at each time instance of its operation. For the reliable diagnosis of the monitored system
we also represent formally the reliability of the monitoring sensors at each time instance, and,
therefore, the reliability of the calculated results from the sensor outputs and the reliability
of the resulting diagnosis. To offer a 360∘ view of the monitored system, we also model static
knowledge about the monitored system (e.g., manufacturing company, production date, location
of system components etc.) and the sensors used for its health monitoring. In this way, utilizing
a query/question answering system, the end user can, for instance, ask about the health state
of a system at each time instance and the reliability of this result, about mitigation actions
to prevent forthcoming failure, or about the reliability of the monitoring sensors at some
time instance. In this way, the end user can obtain a deeper understanding of the monitored
system and its operation. The OWL expressivity of SHM is OWL 2 EL, hence sound and
complete reasoning can be performed in PTIME with respect to the size of the data. Also, it
can be supported by well-known reasoners (e.g., HermiT [7]). SHM is publicly available1 with
namespace http://www.semanticweb.org/SHM# but for short in the following we use the prefix
shm instead.
In the next section, we present the SHM ontology which we evaluate against a set of compe-
tency questions collected from domain experts.
2. Ontology Development Process
For the development of the ontology we followed standard approaches suggested in the literature
(e.g., [8]), which start with the specification of requirements to define the purpose and the scope
of the ontology. For this purpose, domain experts from two different application areas of system
health monitoring (structural health monitoring and fuel cell system monitoring) provided us
with a set of competency questions. After identifying and reusing relevant ontologies from the
literature, we proceeded with formalising the required knowledge. Finally, after populating
the ontology with sample data from a real-world use case scenario (fuel cell monitoring),
we performed consistency checking and we queried the resulting knowledge base using the
provided competency questions.
Specification of Requirements. Thirty-one competency questions were collected from
domain experts in system health monitoring. The collected questions were roughly about: i)
sensor outputs, which can be supported solely by SOSA/SSN (e.g., “What are the mean, min and
max values of strain measured by sensor 𝑥 during a certain period?”), ii) the health condition of
1
https://github.com/eleniTsalapati/System-Health-Monitoring/blob/main/SHM.owl
the system in relation to its static (e.g., production date, location of components) and non-static
(e.g., the value of a parameter) properties, iii) sensor reliability, and iv) correlations among
parameters (e.g., “What is the correlation between parameters 𝑋 and 𝑌 ?”, “When has the
correlation between 𝑋 and 𝑌 changed?”). From this set of questions, we kept only the ones
that an ontological knowledge base could be utilized for their answering, i.e., questions of type
iv) were filtered out as they cannot be addressed by standard logic-based query answering tools.
Due to space limitations, we present in Table 1 only eight representative questions for cases ii)
and iii).
Ontology Reuse. In line with the Linked Data principles [9], SHM reuses classes and prop-
erties from well known external ontologies. The excerpts of the external ontologies that we
reused in SHM are illustrated in Figure 1i). However, if needed, SHM can be extended with
more knowledge from these ontologies. For the representation of knowledge regarding sensors
and their outputs, SOSA/SSN constructs2 are reused with prefixes sosa, ssn, respectively. For
units and values, we reused the Ontology of units of Measure3 , with prefix om. For events and
actors (sentients or objects) participating actively or passively in the events we use The Simple
Event Model4 , with prefix sem. For company data, the GoodRelations ontology5 is reused with
prefix gr. Finally, for the geometries of the systems and system components, the GeoSPARQL
ontology6 is reused with prefix geo.
The System Health Monitoring Ontology. The SHM ontology is illustrated graphically in
Figure 1ii). Two are the core classes of SHM: shm:System and shm:State. The shm:System
class is subclass of ssn:System of the SSN ontology. According to SSN, an ssn:System
“is a unit of abstraction for pieces of infrastructure that implement sosa:Procedures”.
shm:System is subclass of gr:ProductOrService, hence it inherits its properties, and in
particular in SHM, it is further specified with the object property gr:hasManufacturer
(with range gr:BusinessEntity) and the data property gr:hasName. Additionally, an
shm:System can have a shm:productionYear. Information about the manufacturing com-
pany and production date allows end-users to identify potential reliable or unreliable man-
ufacturing companies, or correlate faulty systems with specific production years. Finally,
shm:System is a subclass of geo:Feature, hence it inherits the property geo:hasGeometry
(with range geo:Geometry). The geometry data of the system/system components can be used
to answer questions of the form “What is located here?”, or “Where is the sensor monitoring the
temperature located?”.
Subclasses of the shm:System class are the shm:MonitoredSystem class and the
shm:HealthMonitoringSensor class. At each time instance of its operation, an
shm:MonitoredSystem is in some shm:State. An shm:MonitoredSystem is a system
that is being monitored by at least one shm:HealthMonitoringSensor and it is composed of
2
http://www.w3.org/ns/sosa/, http://www.w3.org/ns/ssn/
3
http://www.ontology-of-units-of-measure.org/resource/om-2
4
https://semanticweb.cs.vu.nl/2009/11/sem/
5
http://purl.org/goodrelations/v1#
6
http://www.opengis.net/ont/geosparql#
Figure 1: i) Excerpts of the GeoSPARQL ontology, GoodRelations ontology, Ontology of units of Measure
and SOSA/SSN ontology reused by SHM. ii) The System Health Monitoring Ontology. The arrows with
solid lines represent the rdfs:SubClassOf relationships and the dashed arrows the object or data
properties.
at least one shm:SystemComponent, which can act as a platform to host a sensor (i.e., it is a sub-
class of the class sosa:Platform). The shm:HealthMonitoringSensor class is also a sub-
class of sosa:Sensor. Additionally, to capture the potential failure of the sensors during their
operation, each shm:HealthMonitoringSensor is related to an shm:SensorReliability,
which is defined by an shm:reliabilityMeasure (with range xsd:decimal) and a time
instance (through the data property shm:atTime with range xsd:dateTime).
The definition of the term shm:State is borrowed from AI Planning: an shm:State
is a representation of the state of the world. As in AI Planning, each shm:State may
have a previous (shm:previous) or a next (shm:next) shm:State, while the transition
from one shm:State to a next may involve an shm:Event or an shm:Action. Each
shm:State is described by a single time instance and by a set of observation results (i.e.,
instances of the class shm:ObservationResult) describing the state of the sensors and
of the monitored system at this time instance. An shm:ObservationResult can be ei-
ther an shm:SensorOutput (which is a subclass of sosa:Result) or a result calculated
from the sensor outputs, i.e., an shm:CalculatedResult, that aids the diagnosing pro-
cess. Also, the class shm:ObservationResult is subclass of om:Measurement, hence,
it inherits the property om:hasNumericalValue and it is domain of the object prop-
erty om:hasUnit. As the Ontology of units of Measure does not define the range of
om:hasNumericalValue, in SHM is defined as xsd:decimal, to keep the nice computa-
tional properties of OWL 2 EL. Also, the potential unreliability of a sosa:Sensor will affect
the reliability of the relevant shm:ObservationResult. This is expressed with the data
property shm:resultTruthValue with rdfs:range xsd:decimal.
The shm:HealthMode describes the health condition of a shm:System at each shm:State
and it is determined by the observation results. Hence, the potential unreliability of the obser-
vation results is propagated to the shm:HealthMode. This is expressed with the datatype prop-
erty shm:modeTruthValue with rdfs:range xsd:decimal. An shm:MitigatingAction
(subclass of shm:Action) may prevent the shm:System from failure.
For the reliability of the diagnostic process it is important to know which sensor out-
puts are involved in the calculation of each parameter. This is modelled with the ob-
ject property shm:isCalculatedFrom which correlates each shm:CalculatedResult
with all relevant shm:SensorOutputs (notice that, differently from SOSA/SSN, a
calculated result may involve outputs of multiple sensors). Also, the parameter
(e.g., relative humidity) that an shm:CalculatedResult expresses is modelled with
the property shm:calculatedProperty with range shm:CalculatedProperty (sub-
class of sosa:Property) and the entity whose property is being calculated is the
shm:FeatureOfInterest (subclass of sosa:FeatureOfInterest).
Evaluation In line with the literature (e.g., [10, 11], throughout the ontology development
process we performed satisfiability checking using Hermit. We, also, populated the ontology
with real monitoring sample data and, then, tested the consistency of the resulting knowledge
base. To check the ontology with respect to completeness, we translated the competency
questions into SPARQL queries and we verified the results manually. Due to space limitations,
next, we provide the SPARQL queries for only eight of the competency questions. The full
set of questions with the corresponding queries and answers is publicly available. 7 Finally,
SHM passed the OOPS! [12] test, i.e. no structural (i.e., syntax, formal semantics), functional, or
usability pitfalls were detected.
7
https://github.com/eleniTsalapati/System-Health-Monitoring
Table 1
Competency questions from domain experts and the respective SPARQL queries
Question SPARQL Query
SELECT DISTINCT ?s ?y WHERE {
When was the outer anode of stack 1 manufactured? ?s rdfs:label "anode of stack1"^^xsd:string.
?s shm:productionYear ?y }
SELECT DISTINCT ?se ?cn WHERE {
?se rdf:type/rdfs:subClassOf* ?s.
?se gr:hasManufacturer/gr:hasName ?cn.
From which company are the faulty sensors and {SELECT DISTINCT ?cn WHERE {
which other sensors are from this company? ?si rdf:type/rdfs:subClassOf* sosa:Sensor.
?si gr:hasManufacturer/gr:hasName ?cn.
?si shm:hasReliability/shm:reliabilityMeasure ?rm.
FILTER(?rm<0.7)}}}
SELECT ?mode WHERE {
?s shm:stateTime ?t.
?s shm:indicatesMode ?mode.
{SELECT ?t WHERE {
What was the health state of the system when the
?o sosa:observedProperty/rdfs:label "relative humidity".
relative humidity of the cathode was maximum?
?o sosa:hasFeatureOfInterest/skos:altLabel "outer cathode".
?o sosa:hasResult/om:hasNumericalValue ?v.
?o sosa:resultTime ?t.}
ORDER BY DESC(?v) LIMIT 1}}
SELECT ?v WHERE {
?o sosa:observedProperty/rdfs:label "relative humidity".
?o sosa:hasFeatureOfInterest/skos:altLabel "outer cathode".
?o sosa:hasResult/om:hasNumericalValue ?v.
What was the relative humidity of the outer cathode
?o sosa:resultTime ?t.{
when the system started to fail?
SELECT ?t WHERE {
?s shm:indicatesMode/rdf:type shm:FailureMode.
?s shm:stateTime ?t }
ORDER BY ASC(?v) LIMIT 1 }}
ASK {
Was 𝑥 system subjected to a load event ?x rdf:type cv:LoadEvent.
at time 0.474 that caused it to enter the ?x sem:hasActor shm:x_system.
plastic deformation region of its shm:x_system shm:isInState ?st.
stress-strain relationship? ?st shm:stateTime "2022-05-30T00:00:00.474"^^xsd:dateTime.
?st shm:indicatesMode cv:plastic_deformation.}
SELECT ?t WHERE {
?s gr:hasName "fcsystem"^^ xsd:string.
?s shm:isInState ?st.
When did the "fcsystem" system start to degrade?
?st shm:stateTime ?t.
?st shm:indicatesMode/rdf:type shm:FailureMode}
ORDER BY ASC(?t) LIMIT 1
SELECT ?t WHERE {
?st shm:stateTime ?t.
When did the system return to normal state? ?st shm:indicatesMode shm:normalMode.
?st shm:previous/shm:indicatesMode ?m.
?m rdf:type shm:FailureMode}
SELECT DISTINCT ?t WHERE {
?s gr:hasName "temp_stack1"^^xsd:string.
?s shm:hasReliability ?r.
When did sensor temp_stack1 become unreliable? ?r shm:reliabilityMeasure ?m.
?r shm:atTime ?t.
FILTER(?m<0.7)}
ORDER BY ASC(?t) LIMIT 1
3. Conclusion and Future Work
In this research work we presented the lightweight mid-level System Health Monitoring On-
tology. Based on requirements from domain experts, the literature and existing ontologies,
SHM formalises knowledge about health system monitoring taking into account the potential
unreliability of the operating sensors. For future work, we plan to check the applicability of
SHM in more use case scenaria from different application areas and its practical scalability to
handle large-scale systems and streaming data.
Acknowledgments
This project has received funding from the European Union’s Horizon 2020 research and
innovation programme under the Marie Sklodowska-Curie grant agreement No 101032307.
References
[1] E. Tsalapati, T. Jackson, W. Johnson, L. M. Jackson, A. Vasilyev, A. A. West, L. Mao, B. Davies,
The role of semantic technologies in diagnostic and decision support for service systems,
in: T. Bui (Ed.), 51st Hawaii International Conference on System Sciences, HICSS 2018,
Hilton Waikoloa Village, Hawaii, USA, January 3-6, 2018, ScholarSpace / AIS Electronic
Library (AISeL), 2018, pp. 1–10. URL: https://hdl.handle.net/10125/50083.
[2] E. Kharlamov, G. Mehdi, O. Savković, G. Xiao, E. G. Kalaycı, M. Roshchin, Semantically-
enhanced rule-based diagnostics for industrial internet of things: The sdrl language and
case study for siemens trains and turbines, Journal of Web Semantics 56 (2019) 11–29.
doi:https://doi.org/10.1016/j.websem.2018.10.004.
[3] E. Tsalapati, W. Johnson, T. W. Jackson, L. M. Jackson, D. Low, B. Davies, L. Mao, A. A. West,
Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic
modelling, Expert Syst. Appl. 163 (2021) 113550. doi:10.1016/j.eswa.2020.113550.
[4] B. Zhou, Z. Zheng, D. Zhou, G. Cheng, E. Jiménez-Ruiz, T. Tran, D. Stepanova, M. H.
Gad-Elrab, N. Nikolov, A. Soylu, E. Kharlamov, The Data Value Quest: A holistic semantic
approach at bosch, in: P. Groth, A. Rula, J. Schneider, I. Tiddi, E. Simperl, P. Alexopoulos,
R. Hoekstra, M. Alam, A. Dimou, M. Tamper (Eds.), The Semantic Web: ESWC 2022
Satellite Events - Hersonissos, Crete, Greece, May 29 - June 2, 2022, Proceedings, volume
13384 of Lecture Notes in Computer Science, Springer, 2022, pp. 287–290. doi:10.1007/
978-3-031-11609-4\_42.
[5] F. Giustozzi, J. Saunier, C. Zanni-Merk, A semantic framework for condition monitoring in
industry 4.0 based on evolving knowledge bases, Semantic Web Journal accepted (2022).
[6] K. Janowicz, A. Haller, S. J. Cox, D. Le Phuoc, M. Lefrançois, Sosa: A lightweight ontology
for sensors, observations, samples, and actuators, Journal of Web Semantics 56 (2019)
1–10.
[7] I. Horrocks, B. Motik, Z. Wang, The hermit OWL reasoner, in: I. Horrocks, M. Yatskevich,
E. Jiménez-Ruiz (Eds.), Proceedings of the 1st International Workshop on OWL Reasoner
Evaluation (ORE-2012), Manchester, UK, July 1st, 2012, volume 858 of CEUR Workshop
Proceedings, CEUR-WS.org, 2012. URL: https://ceur-ws.org/Vol-858/ore2012_paper13.pdf.
[8] M. Fernández-López, A. Gómez-Pérez, N. Juristo, Methontology: From ontological art
towards ontological engineering, in: AAAI 1997, 1997.
[9] C. Burleson, M. E. Gutierrez, N. Mihindukulasooriya, Linked data platform best practices
and guidelines, https://www.w3.org/TR/ldp-bp/, 2014.
[10] E. Tsalapati, J. Tribe, P. A. Goodall, R. I. Young, T. W. Jackson, A. A. West, Enhancing rfid
system configuration through semantic modelling, The Knowledge Engineering Review
36 (2021) e11. doi:10.1017/S0269888921000096.
[11] M. Fernandez-Lopez, A. Gomez-Perez, N. Juristo, Methontology: from ontological art
towards ontological engineering, in: Proceedings of the AAAI97 Spring Symposium,
Stanford, USA, 1997, pp. 33–40.
[12] M. Poveda-Villalón, A. Gómez-Pérez, M. C. Suárez-Figueroa, Oops! (ontology pitfall
scanner!): An on-line tool for ontology evaluation, Int. J. Semant. Web Inf. Syst. 10 (2014)
7–34. doi:10.4018/ijswis.2014040102.