Condition monitoring software is crucial for companies from all industrial branches that take care of the high availability of their automation systems. However, as the automation systems increase in complexity to support numerous business needs, the complexity behind the condition monitoring software development increases as well. It demands a deep understanding of various domain-specific requirements, state-of-the-art architectural concepts, and implementation technologies by software engineers. This paper copes with this complexity by proposing a model-driven approach to support software engineers in designing and implementing condition monitoring software. In that context, the paper contributes with a domain-specific language for condition monitoring software development (DSL4CMSD) along with a code generator that produces a set of python-based microservices. Furthermore, the paper discusses condition monitoring domain-specific requirements and presents a design process for their implementation using the DSL4CMSD. Finally, we evaluate the applicability of our modeling approach on the industrial heat exchanger monitoring case study.

Condition monitoring enables companies from all industrial branches to maintain high availability of their automation systems. Thus, condition monitoring is a fundamental technology to maximize operational productivity by predicting potential fault occurrences that lead to unavailability of the automation systems. Today, performing condition monitoring is successfully achieved using custom-developed software solutions. However, the development of these software solutions is a challenging task as it requires extensive knowledge from multiple engineering disciplines. In this context, there is a high need for a systematic way to formalize this knowledge. Ontologies have had significant success in fulfilling this need by providing a formal model of domain concepts and their relations.In this paper, we present our condition monitoring ontology for automation systems and contribute with a conceptual model. This ontology incorporates ISO standards for condition monitoring and key performance indicators (KPIs). KPIs aggregate numerous sensor values in a few understandable numbers and, therefore, provide more efficient insight in condition of automation systems. To evaluate our work, we developed a condition monitoring knowledge-based system for a centrifugal separator based on the proposed ontology in an industrial project and report about lessons learned.

The process of beer brewing is very complex as it has to fulfill strict demands on the product quality as well as on the availability and the performance of the plant. As a consequence, a condition monitoring of the beer brewing process and its visualization plays an important role such that all relevant deviations are detected as early as possible by the production manager. While a general process for condition monitoring already exists, there currently exists no approach to realize this process for the domain of beer brewing.Therefore, this paper presents a condition monitoring system for the beer brewing process developed in an industrial project. This condition monitoring system is based on Key Performance Indicators (KPIs) that support the production manager in evaluating the actual state of the production processes. A set of brewery-specific KPIs are determined and discussed in the paper. In addition, software architecture and visualization of the KPIs in a brewery-specific dashboard are presented. We evaluate our concept at various beer breweries and report about lessons that we have learned.

Today, software components are provided by global markets in the form of services. In order to optimally satisfy service requesters and service providers, adequate techniques for automatic service matching are needed. However, a requester's requirements may be vague and the information available about a provided service may be incomplete. As a consequence, fuzziness is induced into the matching procedure. The contribution of this paper is the development of a systematic matching procedure that leverages concepts and techniques from fuzzy logic and possibility theory based on our formal distinction between different sources and types of fuzziness in the context of service matching. In contrast to existing methods, our approach is able to deal with imprecision and incompleteness in service specifications and to inform users about the extent of induced fuzziness in order to improve the user's decision-making. We demonstrate our approach on the example of specifications for service reputation based on ratings given by previous users. Our evaluation based on real service ratings shows the utility and applicability of our approach.

In cloud computing, software architects develop systems for virtually unlimited resources that cloud providers account on a pay-per-use basis. Elasticity management systems provision these resources autonomously to deal with changing workload. Such changing workloads call for new objective metrics allowing architects to quantify quality properties like scalability, elasticity, and efficiency, e.g., for requirements/SLO engineering and software design analysis. In literature, initial metrics for these properties have been proposed. However, current metrics lack a systematic derivation and assume knowledge of implementation details like resource handling. Therefore, these metrics are inapplicable where such knowledge is unavailable.

Today, software components are traded on markets in form of services. These services can be service compositions consisting of several services. If a software architect wants to provide such a service composition in the market for trade, she needs to perform several tasks: she needs to model the composition, to discover existing services to be part of that composition, and to analyze the composition's functional correctness as well as its quality, e.g., performance. Up to now, the architect needed to find and use different tools for these tasks. Typically, these tools are not interoperable with each other. We provide the tool SeSAME that supports a software architect in all of these tasks. SeSAME is an integrated Eclipse-based tool-suite providing a comprehensive service specification language to model service compositions and existing services. Furthermore, it includes modules for service matching, functional analysis, and non-functional analysis. SeSAME is the first tool that integrates all these tasks into one tool-suite and, thereby, provides holistic support for trading software services. Thus, it contributes to the acceptance and success of a service market.

The vehicle and crew scheduling problem in public transit aims at finding minimum cost bus and crew schedules such that all trips of a given timetable are operated respecting all operational constraints. In this paper we present a novel hybrid evolutionary algorithm for the multiple-depot integrated vehicle and crew scheduling problem that combines mathematical programming techniques with an evolutionary algorithm. Computational results on randomly generated benchmark instances demonstrate that our approach outperforms the traditional sequential treatment of vehicle and crew scheduling. Furthermore, it is competitive with solution approaches from literature that fully integrate both planning problems.