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 di erent 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 system, semantic interoperability among di erent 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-speci c knowledge. Based on the proposed ontological 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 preventive maintenance.
Industry 4.0 is an inter-disciplinary e ort to inter-connect all resource of a
factory and the factory itself with the Internet to build a smart factory. The tools,
machines, workstations, and human operators are all interconnected to
facilitate the traceability of processes, the adaptive and exible 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
bene ting from a trend of automation in data exchange. The automatic exchange
and analysis of data open up opportunities for manufacturers to further
optimize the production processes. Collecting data from various components of
a production line and analyzing them in a scalable Cloud infrastructure can
signi cantly improve the productivity, reliability, and availability of production
systems in heterogeneous environments [
The management of big data is considered as a challenging task in the context
of condition monitoring (CM) [
To develop such an ICMS, semantic interoperability among di erent
system 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 di erent contexts and domains, thus making it di cult
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
representation of certain domain knowledge, which computationally captures and
structures domain concepts and relationships [
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 ontological framework which consists of a core reference ontology for representing generic CM concepts and relationships and a domain ontology for formalizing manufacturing domain-speci c 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 failure 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
In recent years, several systems and software have been developed to facilitate
the automation of condition monitoring tasks on machines and machine tools.
In [
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 [
For the rst category of ontologies, the Process Speci cation Language (PSL)
ontology [
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
manufacturing processes such as cutting, milling, and feed motions are expressively
described. The MAnufacturings Semantics ONtology (MASON) [
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 [
After reviewing the ontologies that are designed for either CM or manufacturing, we discover that none of them provides satisfactory knowledge representation of both domains. For example, some ontologies tend to focus on a narrow eld, 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 knowledge for characterizing the manufacturing entities or processes which are being monitored but also the knowledge about fault or failure detection and prognostics. 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 manufacturing domains; and (ii) develop an ICMS for fault prognostics, diagnosis and preventive maintenance of production lines. 3
The ontological framework is developed through a Middle-Out approach, which
is a combination of the Top-Down and Bottom-Up approaches [
The development of the ontological framework starts with the choice of a
foundational ontology which de ne 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 speci c domain concepts and relationships. In
our work, we adopted the Uni ed Foundational Ontology (UFO) [
For the middle layer, we develope the core reference ontology for condition
monitoring, named CM-core. According to [
At the bottom layer, a domain ontology called Manufacturing CM Ontology
is developed to formalize domain knowledge that is related to condition
monitoring 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 [
To evaluate and validate our domain ontology, which is used as the main ontology for performing ontological reasoning tasks, we use a web-based ontology 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-Pro ling dimension. To examine the robustness, delity, and quality of the ontology, we also integrate the evaluation conducted by domain experts, for checking its usability for speci c domain tasks.
In the near future, we are going to specialize the Manufacturing CM Ontology into a more speci c domain ontology, named the Production Line CM Ontology, for representing speci c concepts and relationships of manufacturing production lines. 4
Based on the three-layered ontological framework, rule-based reasoning tasks is performed to propose decision makings about machinery fault and failure prediction. We instantiated the Manufacturing CM Ontology with di erent 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 rst rule reasons whether a bearing experiences the state \inner 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 identi cation. 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 identi cation and error detection to be accomplished.
In the near future, we plan to improve the performance of the logical
inference 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 identi cation 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
semantic approach will be applied to tackle the symbol anchoring problems [
Another direction of future work aims to involve the joint utilization of machine learning techniques with semantic technologies. To facilitate the prognostic
results of faults and failures in manufacturing processes, machine learning techniques, 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.
I would like to thank my supervisors, Dr. Cecilia Zanni-Merk, Dr. Christoph Reich, and Dr. Francois de Bertrand de Beuvron for their guidance and supervision during the PhD work. My work has received funding from INTERREG Upper Rhine (European Regional Development Fund) and the Ministries for Research of Baden-Wrttemberg, Rheinland-Pfalz (Germany) and from the Grand Est French Region in the framework of the Science O ensive Upper Rhine HALFBACK project.