Olga Kovalenko

Supporting the engineering of cyber-physical production systems with the AutomationML analyzer

The engineering phase of Cyber-Physical Production Systems (CPPS) is a multi-disciplinary process in which representatives of diverse engineering disciplines collaborate to deliver a complex CPPS. To ensure optimal project management as well as to avoid risks of inconsistencies between engineering models created by engineers from different disciplines, support is needed for integrating and subsequently analyzing diverse engineering data. AutomationML is an emerging data exchange format for engineering data which makes the first step towards the easier exchange of engineering data. Yet, there is a lack of tool support for integrating, making sense of and analyzing AML files. In this paper, we explore the use of Semantic Web and Linked Data technologies to provide extended functionality on top of AML that allows advanced data analytics on engineering data such as intuitive browsing of interlinked engineering models and queries for project-wide verification and validation activities. As a result of these investigations, we present the AutomationML Analyzer prototypical implementation to showcase some of the functionalities made possible by Semantic Web and Linked Data technologies in this context.

Automating Cross-Disciplinary Defect Detection in Multi-disciplinary Engineering Environments

Multi-disciplinary engineering (ME) projects are conducted in complex heterogeneous environments, where participants, originating from different disciplines, e.g., mechanical, electrical, and software engineering, collaborate to satisfy project and product quality as well as time constraints. Detecting defects across discipline boundaries early and efficiently in the engineering process is a challenging task due to heterogeneous data sources. In this paper we explore how Semantic Web technologies can address this challenge and present the Ontology-based Cross-Disciplinary Defect Detection (OCDD) approach that supports automated cross-disciplinary defect detection in ME environments, while allowing engineers to keep their well-known tools, data models, and their customary engineering workflows. We evaluate the approach in a case study at an industry partner, a large-scale industrial automation software provider, and report on our experiences and lessons learned. Major result was that the OCDD approach was found useful in the evaluation context and more efficient than manual defect detection, if cross-disciplinary defects had to be handled.

Engineering Process Improvement in Heterogeneous Multi-disciplinary Environments with Defect Causal Analysis

Multi-disciplinary engineering environments, e.g., in automation systems engineering, typically involve different stakeholder groups and engineering disciplines using a variety of specific tools and data models. Defects in individual disciplines can have a major impact on product and process quality in terms of additional cost and effort for defect repair and can lead to project delays. Early defects detection and avoidance in future projects are key challenges for project and quality managers to improve the product and process quality. In this paper we present an adaptation of the defect causal analysis (DCA) approach, which has been found effective and efficient to improve product quality in software engineering contexts. Applying DCA in multi-disciplinary engineering environments enables a systematic analysis of defects and candidate root causes, and can help providing countermeasures for product and process quality. The feasibility study of the adapted DCA has shown that the adaptation is useful and enables improving defect detection and prevention in multi-disciplinary engineering projects and fosters engineering process improvement.

Evaluation of Technologies for Mapping Representation in Ontologies

Ontology mapping is needed to explicitly represent the relations between several ontologies, which is an essential task for applications such as semantic integration and data transformation. Currently, there is no standard for representing mappings. Instead, there are a number of technologies that support the representation of mappings between the ontologies. In this paper we introduce a set of mapping categories that were identified based on requirements for the data integration projects of an industry partner. An evaluation of available technologies for mapping representation regarding the support for introduced mapping categories has been performed. The results of the evaluation show that the SPARQL Inference Notation would fit the best in the described use case scenario.

Modeling AutomationML: Semantic Web technologies vs. Model-Driven Engineering

Modeling engineering knowledge explicitly and representing it by means of standardized modeling languages and in machine-understandable form enables advanced engineering processes in industrial and factory automation. This affects positively both process and product quality. In this paper we explore how the AutomationML format, an emerging data exchange standard, that supports the Industry 4.0 vision, can be represented by means of two established modeling approaches - Model-Driven Engineering (MDE) and Semantic Web. We report observed differences w.r.t. resulting model features and model creation process and, additionally, present the application possibilities of the developed models for engineering process improvement in a production system engineering context.

Semantic mapping support in AutomationML

In production system engineering, the machine-understandable definition of relations between engineering information views is important to enable automating dependency checking between these views. Unfortunately, in automation engineering there is no standardized representation of relations and dependencies, which makes it difficult to automate consistency checking. In this paper we derive requirements for describing relations and dependencies in the semantics of typical engineering models. We investigate the emerging data exchange standard AutomationML regarding the representation of semantic mapping types that represent relations and dependencies between engineering models. Major result is the identification on how semantic mapping types are modeled in AutomationML to find similarities and differences, which can help to improve the machine-understandable modeling of the dependencies in AutomationML to enable the automation of engineering processes.

Semantic Matching of Engineering Data Structures

An important element of implementing a data integration solution in multi-disciplinary engineering settings, consists in identifying and defining relations between the different engineering data models and data sets that need to be integrated. The ontology matching field investigates methods and tools for discovering relations between semantic data sources and representing them. In this chapter, we look at ontology matching issues in the context of integrating engineering knowledge. We first discuss what types of relations typically occur between engineering objects in multi-disciplinary engineering environments taking a use case in the power plant engineering domain as a running example. We then overview available technologies for mappings definition between ontologies, focusing on those currently most widely used in practice and briefly discuss their capabilities for mapping representation and potential processing. Finally, we illustrate how mappings in the sample project in power plant engineering domain can be generated from the definitions in the Expressive and Declarative Ontology Alignment Language (EDOAL).

Towards evaluation and comparison of tools for ontology population from spreadsheet data

Semantic Web technologies and ontologies increasingly provide mission-critical capabilities for a variety of applications, not only in the Web, but also in industry projects to facilitate semantic integration and interoperability between heterogeneous systems. Due to this proliferation in the use of ontologies, technologies and tools have been developed to facilitate the ontology engineering process and ontology population, as a part of this process. As spreadsheets are widely used to store and exchange data, academia and industry have developed a range of tools and mapping techniques to support the (semi-)automated translation of spreadsheet data into OWL/RDF. Existing tools vary in many aspects, therefore it can be difficult to select tool that fits best for a specific usage context. In this paper we analyzed several types of end users, which could be interested to apply such tools in their workflow, and their specific needs. Based on this analysis we propose an evaluation framework that could facilitate tools comparison; and c) search for an appropriate tool for a specific task/problem. In order to validate the proposed evaluation framework, a qualitative analysis of a set of six tools for ontology population has been performed.

Semantic Web Solutions in Engineering

The Industrie 4.0 vision highlights the need for more flexible and adaptable production systems. This requires making the process of engineering production systems faster and intends to lead to higher quality, but also more complex plants. A key issue in improving engineering processes in this direction is providing mechanisms that can efficiently and intelligently handle large-scale and heterogeneous engineering data sets thus shortening engineering processes while ensuring a higher quality of the engineered system, for example, by enabling improved cross-disciplinary defect detection mechanisms. Semantic Web technologies (SWTs) have been widely used for the development of a range of Intelligent Engineering Applications (IEAs) that exhibit an intelligent behavior when processing large and heterogeneous data sets. This chapter identifies key technical tasks performed by IEAs, provides example IEAs and discusses the connection between Semantic Web capabilities and IEA tasks.

Semantic Modelling and Acquisition of Engineering Knowledge

Ontologies are key Semantic Web technologies (SWTs) that provide means to formally and explicitly represent domain knowledge in terms of key domain concepts and their relations. Therefore, the creation of intelligent engineering applications (IEAs) that rely on SWTs depends on the creation of a suitable ontology that semantically models engineering knowledge and the representation of engineering data in terms of this ontology (i.e., through a knowledge acquisition process). The tasks of semantic modelling and acquisition of engineering knowledge are, however, complex tasks that rely on specialized skills provided by a knowledge engineer and can therefore be daunting for those SWT adopters that do not possess this skill set. This chapter aims to support these SWT adopters by summing up essential knowledge for creating and populating ontologies including: ontology engineering methodologies and methods for assessing the quality of the created ontologies. The chapter provides examples of concrete engineering ontologies, and classifies these engineering ontologies in a framework based on the Product-Process-Resource abstraction. The chapter also contains examples of best practices for modelling common situations in the engineering domain using ontology design patterns, and gives an overview of the current tools that engineers ca use to lift engineering data stored in legacy formats (such as, spreadsheets, XML files, and databases, etc.) to a semantic representation.