This represents a reduction of about 89% of the time required to carry out a preliminary analysis supporting the HAZOP experts in such a time-consuming task.
Modelling and Simulation of Semantically-Enriched PN-based Digital Twins
6.3.4 Discussions around the application case
The use of a Petri Net to model and analyse fault propagations in industrial plants has extensively proved to be effective in literature. The approach described in this application case extends the one presented in two research works published by the author of this dissertation in the past few years [127] [126]. In particular, the CPN model has been enriched by adding new types of failure, components and component states, therefore, widening the application range of this method. By leveraging one of the most accurate mathematical models for HAZOP analysis time estimation, a reduction of about 89% of the total time to complete the analysis of a medium complexity case study using the proposed solution based on CPN-HAZOP has been shown. In both new and traditional approaches, the reporting phase was not considered. The HAZOP study resulting from the translating macro does not represent the final step of the analysis, but it is a useful and quick guide for the team of experts during the brainstorming phase.
The automation introduced in the HAZOP technique and the significant reduction of time required for the analysis could also allow the method to be disseminated to less burdensome contexts compared to typical HAZOP analysis application (e.g. plants at risk of a major accident subject to the Seveso Directive). In fact, the proposed approach would allow more accurate risk analysis also in smaller and less complex process plants, where the limited number of employers and, in some cases, their competencies are not consistent with a traditional HAZOP study process.
Further research is required in order to extend the library with more CPN-based component models, to translate the information into a more detailed user-friendly report, and to integrate the so-called “safeguards” in the CPN model. Eventually, the next developments of this smart tool should also include the introduction of stochastic data values in order to better simulate the occurrence of the likelihood of failure causes and allow a semi-quantitative risk assessment.
Conclusion
This dissertation proposes a framework for applying state-of-the-art methods for semantic data and ontology management in the field of manufacturing systems modelling and simulation. The benefits and limitations of such a proposed framework are thoroughly discussed through the analysis of three application cases addressing diverse aspects of the domain in question: workforce allocation, manufacturing knowledge management, quantitative reliability assessment for an assembly station, as well as qualitative risk analysis for a petrochemical plant facing serious hazard and operability issues.
In the context of modelling and simulation for manufacturing system applications, the use of semantic web technologies as a framework has not yet been fully leveraged. Through the proposed approach, it is possible to outline a list of principles and best practices that will help future efforts towards the application of complex digital twin modelling and simulation- oriented solutions built on semantically-enriched manufacturing information systems.
The methodological approach adopted here is, therefore, designed on a set of technical principles and activities which characterize the development of each of the domain-specific ontologies presented in this work:
1. Clear definition of context and scope of the representation 2. Selection of the formats and related serialization
3. Analysis and reuse of the existing ontologies in the domain 4. Creation of each new entity starting from an Upper Ontology
5. Provision of textual definitions for each entity, using existing standards
6. Setting up of unique Identifiers & Naming Conventions for each new entity
7. Provision of a logic along with the set of domain specific entities to foster reasoning and machine-based inference
Therefore, each solution presented in the application cases above is built upon these principles and aim to – partly or fully – answer the three research questions presented in Section 2.2 and repeated below:
RQ1. How can we semantically enrich manufacturing system models and exploit context-awareness for process analysis? RQ2. How can semantics be used to foster reusability and interoperability of manufacturing systems models?
RQ3. How does the analysis of semantically-enriched manufacturing systems impact on modelling and simulation applications? The first application, designed and analysed at the beginning of this research work, contributes to the investigation of ontology modelling and analytics issues related to the exploitation of semantically-enriched data, paving the way towards answering RQ1 and RQ2. As a result of the deployment of a so-called semantic framework for human resource management through semantically-
enriched data, it is possible to draw two possible conclusions. Firstly, the semantic enrichment of historical and real-time information
from shop floor work/task scheduling provides support for an intelligent system, which does not replace the existing information systems but rather empowers the latter with reasoning capabilities and advanced analytics capabilities. Secondly, a real-time task scheduling method towards HR optimization by utilizing Conditional Random Field (CRF) probabilistic models, semantically-enriched information, and semantic query and rule languages, namely SPARQL and SWRL, has been proven to be effective, although, some considerations should be done around the scalability of the proposed approach.
Conclusion
The second application contributes to existing knowledge by answering (almost exhaustively) all three RQs. The findings, indeed, span aspects such as the semantic enrichment of manufacturing system models, the reusability of modelling primitives, the impact of semantically-enriched manufacturing system models in modelling and simulation (M&S) applications. Here, two key aspects are addressed by the proposed ontology-based solution: (i) ontology-driven creation and semantic enrichment of the manufacturing system’s modelling elements (or modelling primitives of a specific modelling language, such as Petri Net formalism); (ii) semantics- driven analysis of a digital twin’s simulation outcomes aimed at enhancing the decision-making process. The study of an ontology- based solution for reliability assessment of an automated assembly station demonstrates how effectively such a solution can support manufacturing system’s data collection, manufacturing process modelling, simulation, and simulation results analysis.
The third application has been presented with the aim of further investigating research questions 1 and 3. The proposed solution does not introduce any new assessment paradigms but rather combines existing technologies (Petri nets and Ontologies) with the purpose of rendering the well-known, as well as, time consuming methodology called HAZOP more effective and less costly. While the use of PNs to model and analyse fault propagations in industrial plants has been already documented in literature, the employment of ontologies to empower such a PN-based manufacturing system model with context-awareness capabilities has been here presented. The proposed solution has proven to reduce about 89% of the time required to carry out a preliminary analysis, thus supporting the Hazard & Operability (HAZOP) experts in such a time-consuming task.
Finally, while recognising some limitations of the second application case, the exploratory research here presented lays the initial groundwork for future research. Starting from the observations above, this dissertation provides a new angle of looking at the digital transformation – key enabler of future modelling and simulation applications – for manufacturing systems, therefore, paving the way towards the development of a new analytical framework for semantics-driven modelling and simulation of context-aware manufacturing systems. Recommendations for further research include:
Investigation of emerging software tools and standards for Model Based System Engineering (MBSE) solutions, which leverage ontologies and semantic reasoning for manufacturing system modelling and simulation.
In depth analysis of alternative languages such as SySML, perhaps, by comparing this with the features of the PN formalism presented in this work. The level of adoption should not be the only indicator for establishing the “right” language to be used, but rather the range of applicability of the latter should be.
Alignment to the most recent developments in Ontology Engineering for manufacturing. In this regard initiatives such as the Industrial Ontologies Foundry (IOF) certainly represents a good source of information and an environment where these aspects can be all discussed and further refined.
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