The vision of moving towards more autonomous systems in the industrial domain requires the e cient use of information. The frequent absence of well described assets and the poorly structured and nonavailable live data is considered as one of the main roadblocks towards this future. This paper addresses these issues by creating an ontology for the MTConnect standard. MTConnect provides the basis to describe machines, their device structure including sensors and their measurement values. The ontology is tested using live data and we further provided multiple queries to execute certain machine health tests.
The Industry 4.0 vision [
The MTConnect standard is published as a document4 (PDF) and consists of an implementation in a machine-readable format (XML). This paper proposes the implementation of MTConnect as machine-interpretable ontology (OWL) to achieve two things: First, to preserve the semantics of the reference within the model and second, to enable its interlinking with other datasets to form the basis of the Industry 4.0 vision. Semantic-based approaches facilitate information ows in contrast to established closed data silo environments as well as vendorlogged in ERP systems. Furthermore, we de ned multiple machine healthcare
evaluation queries (SPARQL) to lay the groundwork for standardized machine evaluation methods.
The ontology tested using a qualitative evaluation by checking its semantic correctness, by creating sample data and by running multiple sample queries.
After all, the ontology contains 80 classes, 12 object properties and 43 datatype properties. We further developed 12 SPARQL queries for checking the machine health and for retrieving structural information. 2
The related work section is organized as follows: We rst review existing industrial ontologies and relevant standards, then we provide an overview on existing sensor standards and nally list important device-based ontologies.
While the European industry standardized the Open Platform
Communications Uniformed Architecture (OPC UA) standard 5 [
Several ontologies have been created in the manufacturing and engineering
domain. Speci cally, in [
A considerable number of ontologies have been also developed focusing on the
modelling of sensors and their measurements. SSN7 (Semantic Sensor Network)
ontology [
Ontologies are further used for representing the structure of devices and
the di erent components that they consist of. As part of the Smart Appliances
Project9, the Saref ontology [
Taking into consideration all the described approaches, the MTConnect ontology is the rst attempt to model industrial machines and their data based on the MTConnect standard.
6 https://www.nist.gov/
7 https://www.w3.org/TR/vocab-ssn/
8 http://www.opengeospatial.org/
9 https://sites.google.com/site/smartappliancesproject/home
The MTConnect ontology is developed manually based on the provided
documentation of the MTConnect standard as well as existing data published
conforming to MTConnect. The ontology le (RDF) is expressed in the Turtle (Terse
RDF Triple Language) [
The building of our ontology follows the top-down approach [
@prefix rdfs : <http ://www.w3. org /2000/01/ rdf schema#> . @prefix owl: <http ://www.w3. org /2002/07/owl#> . @prefix rdf: <http ://www.w3. org/1999/02/22 rdf syntax ns#> . @prefix xsd: <http ://www.w3. org /2001/XMLSchema#> . @prefix mto: <https ://raw . githubusercontent .com/ vocol /MTConnect/master/
MTConnect. t t l> . mto: Component rdf: type owl: Class ; rdfs : label " Component "^^ xsd: string ; rdfs : comment " Defines the structure of the physical or logical parts of a device that provide more precise definition for the structure of the device ."@en.
Listing 1.1. Class de nition in Turtle 10 http://www.mtconnect.org/standard-documents/ 11 https://smstestbed.nist.gov/vds/ 12 https://www.w3.org/2005/Incubator/ssn/ssnx/ssn 13 https://w3id.org/saref# 14 http://semanticweb.cs.vu.nl/2009/11/sem/ 15 https://www.w3.org/2001/sw/BestPractices/OEP/SimplePartWhole/ 16 https://github.com/vocol/MTConnect
We started the development of the ontology by rst de ning the perceived core concept of the standard: Device. A device is de ned as a single piece of equipment e.g. a machine or any set of elements that operate together to perform a function. The di erent parts that form a device are described by using the concept Component (Listing 1.1) which contained the subclasses Axe, Sensor, Controller, System, Actuator, Stock, Interface and Door. Furthermore, each of those subclasses contain more subclasses. For example, the class Axe consists of the subclasses LinearAxe and RotaryAxe, which are types of axes. The class Path is the subclass of the Controller. This hierarchical structure is preserved in our ontology.
The measurement values of the sensors are de ned by MTConnect using so called data items. Therefore, we de ned the class Dataitem which contains subclasses such as Temperature, Availability, Condition, Angle and Pressure to represent these numeric or non-numeric values that are related to the machine status. The object property ssn:hasPart is use connect the di erent device parts and ssn:hasSubSystem to de ne the respective sub components. The datatype property saref:has Name is used to de ne the device name.
The datatype property sem:hasTimeStamp de nes the exact date and time that an event occurred and the part:partOf describes the relationship between di erent parts of a device.
Figure 1 provides an overview of the MTConnect ontology main concepts. We evaluated the ontology using a qualitative evaluation. We tested the semantic correctness of the ontology, we created sample instances based on live data to ensure its completeness and nally, we developed and tested multiple SPARQL queries to provide proof that the ontology is able to return the expected results in certain machine health and structural queries. While executing the queries, no performance issues were observed. 4.1
We used the OOPS! (OntOlogy Pitfall Scanner!)17 for inspecting gaps in the context of the created ontology. As a result, no errors or warning were raised in this test. 4.2
We further created sample instances based on live stream data published by National Institute of Standards and Technology (NIST)18 to the validate the completeness of our ontology. The format of the live stream data is XML and available via the Smart Manufacturing Systems endpoint19). Due to the poor experience with automated methods, we decided to create a few the sample instances manually. mto : Agie01 a ssn : Device , owl : NamedIndividual ; rdfs : label " Agie01 "; saref : hasName " GFAgie01 "; saref : hasDescription " Agie Mikron HPM600U - GF Agie Charmilles
HPM600U "; saref : hasManufacturer " Agie Charmilles "; saref : hasModel " HPM600U "; mto : hasCreationtime " 2018 -02 -15 T13 :05:28 Z" ^^ xsd : dateTime ; mto : hasUuid " mtc_adapter001 "; rdfs : isDefinedBy mto : .
Listing 1.2. Device individual de nition in Turtle
As a result, six samples corresponding to di erent machines are created based on our developed MTConnect ontology. Listing 1.2 depicts an example class instance expressed in Turtle. 4.3
In order to check the health status of the machines and their responses, we examined the condition values that are introduced by the MTConnect documentation. The provided minimum and maximum danger values indicate the operational status of the machines and notify for possible abnormal machine behaviors. Listing 1.3 displays a query to retrieve all the devices which have a WARNING condition and returns the condition message. 17 http://oops.linkeddata.es/ 18 https://www.nist.gov/ 19 https://smstestbed.nist.gov/vds/ SELECT ? device ? message WHERE { ? device ssn : hasPart ? component .
? component mto : hasDataItem ? dataitem . ? dataitem mto : hasCondition " WARNING "; sem : hasTimeStamp ? timestamp ; mto : hasConditionMessage ? message . }
Listing 1.3. SPARQL condition query
With the use of SPARQL queries it is also possible to observe the structural design of a machine. For example, Listing 1.4 shows all the components and subcomponents that are part of a device.
SELECT * WHERE { ? device ssn : hasPart ? component .
? component ssn : hasSubSystem ? subcomponent .} ORDER BY ASC (? device )
Listing 1.4. SPARQL structural query
We created in total 12 SPARQL queries related to the MTConnect ontology which are all available on the GitHub repository. All queries are executed in the GraphDB20 environment and were executed within 0.5 seconds such that no performance issues were observed. 5
This paper introduces the MTConnect ontology, which is based on the MTConnect manufacturing standard, along with standardized SPARQL queries which can be reused from companies and facilitate the control and management of their manufacturing machines.
Overall, the combination of semantic web technologies and industrial standards can enhance interoperability between companies by adding semantic value to the data representation. We plan to engage with the MTConnect standardization community to move this goal forward.
To compare in the future developed MTConnect-compliant systems, we consider the provision of MTConnect benchmark datasets an important next target. We plan to do a quantitative evaluation in the near future to test the performance of our ontology.
An observation: By adding maximum and minimum values for certain danger values (e.g. Temperature) in the standard, one could develop more expressive SPARQL queries to build more autonomous systems. However, we assume that the reason for the lack of those values might be that it depends on the vendor of the device.
Finally, as part of future work, we suggest that the MTConnect ontology could be combined with machine learning algorithms in order to support predictive maintenance techniques in the manufacturing sector. 20 https://ontotext.com/products/graphdb/ This work has been supported by the German Federal Ministry of Education and Research (BMBF) in the Industrial Data Space research project (grant no. 01IS15054) and by the German Federal Ministry of Transport and Digital Infrastructure (BMVI) in the LIMBO research project (grant no. 19F2029I).