Model-Driven Approach for Realization of Data Collection Architectures for Cyber-Physical Systems of Systems to Lower Manual Implementation Efforts
Abstract
:1. Integration of Systems and Accessibility of Data as Prerequisites for Industrie 4.0
2. Related Work and State-Of-The-Art
2.1. IIoT Communication Protocols
2.2. Data Collection Architectures
2.3. Modeling Languages and DSLs
2.4. State-Of-The-Art in Model-Driven System Architectures
2.5. Identified Research Gap
3. Requirements and Concept for a Model-Driven Data Collection Architectures
3.1. Requirements for a Model-Driven Development of Data Collection Architectures
- RDSL
- During data collection and analysis projects in the domain of industrial automation, a multitude of disciplines is involved, ranging from data analysts, over IT architectures, to automation engineers and process experts. All of these experts have different backgrounds and use different terminology. Therefore, the approach should be based on a graphical DSL that allows for sharing modeled information intuitively and understandably. Moreover, the graphical notation and the underlying metamodel have to provide the means to capture all relevant aspects needed for the design of data collection architectures in the domain of industrial automation.
- RCom
- The realization of data collection architectures requires the repetitive implementation of numerous communication channels that form the communication part of the architecture, and specific glue code that transforms, analyzes, or stores the collected data. The approach should be capable of automatically generating code for the communication part to reduce manual implementation efforts efficiently.
- RProt
- The significant heterogeneity of IIoT protocols and the uncertainty, whose protocol will be predominant in the future, hinders and slows down industrial adoption of I4.0 principles. In addition, if a product has to be offered for different markets or domains, manufacturers may have to support multiple protocols for the same machine. This further increases the implementation efforts for data collection architectures. Therefore, the approach should feature modular support for relevant IIoT protocols to increase technology adoption.
- RInit
- Initial deployments (development from scratch) of data collection architectures are associated with substantial implementation efforts due to heterogeneity and complexity of CPSoS. MDD has the potential to significantly lower these efforts in comparison to manual programming. However, these efforts’ savings have to be quantifiable and of general validity and not just of qualitative nature, as found in the literature. The quantification has to take the effort to create the MDD toolchain into account to estimate the break-even between MDD and classical manual programming of data collection architectures.
- RMig
- Besides initial deployment, re-deployments, also called migrations, are of significant interest for industrial applicability [10]. Despite the availability of new solutions, which are better suited for the needs of automated production, enterprises hesitate to apply them. This is due to the excessive cost associated with re-implementing all connected systems’ communication interfaces, consequently causing vendor lock-in. In addition, the need to support more than one communication protocol for data collection inside CPSoS can be seen as a migration scenario. This leads to the requirement that the MDD approach has to support migration scenarios of data collection architectures. In addition, effort savings also have to be proofed quantitatively for such a scenario compared to manual programming.
3.2. Concept for Model-Driven Data Collection Architectures
3.2.1. Graphical Notation and Metamodel of the DSL
- a SoftwareContainer for the description of data flows and software functions,
- a PhysicalContainer that reflects the hardware systems and components of the architecture,
- an AnnotationContainer that can carry additional information on properties and requirements, as well as
- a RelationContainer to describe logical links.
3.2.2. Software Framework for IIoT-Protocol Support
3.2.3. Model Transformation to Deployable Communication Code
4. Evaluation of the Model-Driven Approach for Data Collection Architectures
4.1. Lab-Scale Case-Study to Investigate Implementation Effort Savings
4.1.1. Description of Use-Case Featuring a Heterogeneous CPSoS with Superordinate Systems
- the legacy CPPS Festo Modular Production System (MPS) that is interfaced using custom software that provides connectivity over a proprietary TCP protocol and communicates with the plant over a serial RS232 connection;
- the constantly evolving myJoghurt Industrie 4.0 demonstrator, with a state-of-the-art Beckhoff PLC, connectivity over the proprietary Beckhoff ADS protocol as well as standard OPC UA, and around a total of 500 variables (I/Os and internal variables); and
4.1.2. Model-Driven Generation of Communication Architecture
4.1.3. Effort Metrics for Initial Deployment
4.2. Extrapolation Case-Study for a Generalization of Results
4.2.1. General Analysis of the Relations
4.2.2. Break-Even Analysis between Manual Implementation and the Model-Driven Approach
5. Conclusions and Outlook
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Person | Experience Level | Total Effort for Modeling of the Lab-Scale Setup |
---|---|---|
1 | Well-experienced user, strong industrial automation background applied the graphical notation several times. | 2 30 |
2 | Semi-experienced user, medium industrial automation background, applied the notation occasionally. | 4 20 |
2 | Inexperienced user, strong industrial automation background, recently introduced to the notation. | 4 40 |
Protocol | Initial Deployment | Migration | ||
---|---|---|---|---|
AMQP | 67 | 2 | 48 | 0 |
Beckhoff ADS | 81 | 2 | 62 | 0 |
Apache Kafka | 74 | 2 | 55 | 0 |
MQTT | 51 | 2 | 32 | 0 |
OPC UA | 114 | 2 | 95 | 0 |
MEAN | 77.4 | 2 | 58.4 | 0 |
Initial deployment | 10 | 1 |
Migration | 1 | 0 |
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Trunzer, E.; Vogel-Heuser, B.; Chen, J.-K.; Kohnle, M. Model-Driven Approach for Realization of Data Collection Architectures for Cyber-Physical Systems of Systems to Lower Manual Implementation Efforts. Sensors 2021, 21, 745. https://doi.org/10.3390/s21030745
Trunzer E, Vogel-Heuser B, Chen J-K, Kohnle M. Model-Driven Approach for Realization of Data Collection Architectures for Cyber-Physical Systems of Systems to Lower Manual Implementation Efforts. Sensors. 2021; 21(3):745. https://doi.org/10.3390/s21030745
Chicago/Turabian StyleTrunzer, Emanuel, Birgit Vogel-Heuser, Jan-Kristof Chen, and Moritz Kohnle. 2021. "Model-Driven Approach for Realization of Data Collection Architectures for Cyber-Physical Systems of Systems to Lower Manual Implementation Efforts" Sensors 21, no. 3: 745. https://doi.org/10.3390/s21030745