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In this article, the concepts of transfer and continual learning are introduced. The ensuing review reveals promising approaches for industrial deep transfer learning, utilizing methods of both classes of algorithms. In the field of computer vision, it is already state-of-the-art. In others, e.g. fault prediction, it is barely starting. However, over all fields, the abstract differentiation between continual and transfer learning is not benefitting their practical use. In contrast, both should be brought together to create robust learning algorithms fulfilling the industrial automation sector's requirements. To better describe these requirements, base use cases of industrial transfer learning are introduced., 12 pages, 4 figures, 2 tables. Accepted for publication by IEEE Industrial Electronics Magazine

The utilization of deep learning in the field of industrial automation is hindered by two factors: The amount and diversity of training data needed as well as the need to continuously retrain as the use case changes over time. Both problems can be addressed by industrial deep transfer learning allowing for the performant, continuous and potentially distributed training on small, dispersed datasets. As a specific example, a dual memory algorithm for computer vision problems is developed and evaluated. It shows the potential for state-of-the-art performance while being trained only on fractions of the complete ImageNet dataset at multiple locations at once.

Autonomous systems are increasingly discussed in the domain of industrial automation. However, there is no consensus in the literature about the definition of industrial autonomous systems, what characteristics do they possess and how to distinguish them from intelligent industrial automation systems. Another important aspect is the comparison between the degree of automation and autonomy. Addressing this confusion, this paper starts with an analysis of the historical development of the term autonomy. Based on the similarities in the definitions, that were identified in a literature review, a comprehensive definition of industrial autonomous systems is presented, and four main characteristics of an industrial autonomous system are derived. Consequently, the characteristics for the realization of an industrial autonomous system (1) systematic process execution, (2) adaptability, (3) self-governance and (4) self-containedness are described in detail. Another objective of this contribution is to assist the developers of industrial autonomous systems to take the above-mentioned characteristics into account. Furthermore, the developer is advised which abilities can be used to achieve the corresponding essential characteristics of industrial autonomous systems. To illustrate the characteristics and abilities of an industrial autonomous system, this paper gives a detailed description of three realized cases of application in industry.

Zusammenfassung Durch zunehmende Agilität im Entwicklungsprozess, kürzere Lebenszyklen und sich ändernde Kunden- und Gesetzgeberanforderungen müssen Produktionssysteme wandlungsfähig sein. Aber jede Veränderung des Systemverhaltens muss anschließend auch erneut abgesichert werden. Zur Absicherung sicherheitskritischer Funktionen eignen sich formale Verifikationsmethoden. Allerdings ist hierzu ein hoher Modellierungsaufwand notwendig, was den Einsatz formaler Verifikationsmethoden in der Praxis hemmt. Zudem empfiehlt sich eine Überprüfung der zur formalen Verifikation eingesetzten Modelle. Zur Bewältigung der Herausforderungen für die Absicherung, die eine Rekonfiguration während des Betriebs nach sich zieht, werden in diesem Beitrag zwei vielversprechende und sich gegenseitig ergänzende Ansätze für die lebenszyklusübergreifende Absicherung von Produktionssystemen vorgestellt.

Zusammenfassung Unvorhersehbare Prozessereignisse und Anomalien sind Treiber erhöhter Ineffizienzen in Form von schwankender Produktqualität. In diesem Beitrag wird ein datengetriebener Ansatz zur Qualitätsoptimierung vorgestellt, auf dessen Basis Anomalien charakterisiert werden, die zur Entwurfszeit des Systems nicht bekannt waren. Es wird eine Netzarchitektur in Form eines Sequence-to-Sequence Netzes mit Long Short-Term Memory (LSTM) Zellen vorgestellt. Dadurch kann vorhergesagt werden, welche Anpassung am Stellgrößenverhalten vorgenommen werden muss, um erwartete Anomalien zu kompensieren. Dadurch wird das Qualitätsergebnis langfristig in der Toleranz gehalten. Der Ansatz wird prototypisch anhand von zwei Prozessketten der diskreten Fertigung umgesetzt und evaluiert.

Society today depends on autonomous systems, such as intelligent service systems, self-driving trains, and remote surgeries.1 The ultimate validation of the Turing test is that we often do not recognize autonomous systems. This growing usage poses many challenges, such as how to provide transparency, which rules or learning patterns are applied in a complex situation, and if these rules are the right ones. Validation is the key challenge, of which we will provide an overview in this article.

Zusammenfassung Bei der Entwicklung automatisierter Systeme im Bereich der Automatisierungstechnik haben Ingenieure viele Aufgaben oft unter Zeitdruck zu bearbeiten, unter anderem Simulationen. Um Ingenieure bei diesen aufwändigen Simulationsaufgaben zu unterstützen, wird ein Framework vorgestellt, das Simulationen basierend auf strukturiertem Wissen parallel und dezentral koordiniert und durchführt. Hierbei können verschiedene Lösungsansätze eines Problems automatisiert simuliert und miteinander verglichen werden. Komplexe Problemstellungen, d. h. umfangreiche und häufig aus mehreren voneinander abhängigen Teilen bestehende Aufgaben, werden hierzu in Teilprobleme zerlegt und ihre Lösungsansätze separat mit dem jeweils geeignetsten Softwaretool bearbeitet. Zur flexiblen Koordination der dezentralen Simulationen werden kooperierende Softwareagenten eingesetzt. Diese autonomen Einheiten kommunizieren miteinander, um die parallelen Tätigkeiten während eines Simulationsprojekts zu steuern. Evaluiert wird das vorgestellte Konzept anhand der Anwendungsgebiete des Regelungsentwurfs und der multiphysikalischen Simulation. Hierzu wurden entsprechende prototypische Agentensysteme mithilfe des entwickelten Frameworks realisiert. Zusätzlich zum Framework zur parallelen Simulation wird auch ein Konzept zur Ergänzung des Agentensystems um eine lernende Case-Based-Reasoning-Komponente vorgestellt. Durch die Ergänzung des Agentensystems um Lernfähigkeit, konnte die Performance der Simulationen weiter gesteigert werden. Aufgrund der parallel durchgeführten Simulation verschiedener Ansätze mit teils stark variierenden Rechenzeiten, stehen Ergebnisse häufig bereits früher zu Verfügung. Auch die Qualität der Simulationsergebnisse kann durch die Untersuchung verschiedener Ansätze mit dem Agentensystem verbessert werden.

In response to an aging society, the development and application of medical assistance systems is increasing and becoming an integral part of various daily life activities. To enable effective assistance, designing intuitive and elderly-appropriate user interfaces is necessary and represents a challenge due to the lack of standardized design processes. A possible approach for the design is to apply the usability engineering process based on the IEC 62366 standard for medical devices. In this contribution, the design process of a user interface for an automated pill dispenser is shown based on applying the standard, a literature review and a user evaluation.

Digital Twins will change how systems and products are engineered and operated. Individual virtual representations of assets help to develop, maintain and change single components or whole factories. Aerospace engineering, product design and intelligent manufacturing are hot spots for the use of Digital Twins. Simultaneously, globalized markets lead to a growing awareness of dependability and quality, which increases the importance of verification and validation. The Digital Twin could prove to be key enabler for efficient verification and validation processes. This paper presents the results of the literature review of approaches that use Digital Twins for verification and validation purposes. Many solutions have been found for a wide range of challenges in various fields of application. This survey discusses the underlying methods and the elements of Digital Twins already in use. Most research approaches focus on simulations and three methodological clusters of approaches sharing similar ideas were identified.

Recently, ontologies and semantic data technologies have increasingly come back into the focus of research due to the emerging use of knowledge graphs. However, even though the modeling and simulation community has recognized the potential of using this technology for the modeling process, for example for automatic model generation, adaptation or to represent simulation expert knowledge, a general and reusable approach for the aforementioned purposes is still missing. Therefore, in this paper a state of the art review for using knowledge representation during modeling and simulation processes of complex technical systems is conducted, such as factories or process plants with specific focus on the production and logistic domain. Based on that, requirements and benefits of knowledge graphs in this specific domain are evaluated.

Fault prediction based upon deep learning algorithms has great potential in industrial automation: By automatically adapting to different usage contexts, it would greatly expand the usefulness of current predictive maintenance solutions. However, restrictions regarding the centralized accumulation of data necessary for such automatic adaption call for a distributed approach to training these algorithms. Therefore, in this paper, a continual learning based algorithm for fault prediction is presented, allowing for distributed, cooperative learning by elastic weight consolidation. This algorithm is then evaluated on a large NASA turbofan engine dataset and shows promising results regarding the performant training on decentral sub-datasets for industrial automation scenarios.

Large-scale manufacturing systems consist of multiple complex process steps that are highly interdependent and characterized by dynamic nonlinear physics. These processes are subject to model uncertainties and external disturbances that cause reduced process stability and fluctuant product quality. To avoid defects and to ensure a high product quality, an adequate detection and control of disturbance effects is needed. Hence, the paper presents a novel adaptive control scheme with disturbance rejection property for discrete large-scale manufacturing systems. To model the complex process physics and disturbance effects, Long short-term memory (LSTM) networks are applied. These recurrent networks improve the modeling of time-variant systems regarding short-term and long-term dependencies. The adaptive control scheme is predicated on a self-learning and cooperative prediction of process and disturbance to achieve an improved performance. Based on this neural prediction, a compensation of disturbance effects and thus a part-centered quality optimization can be achieved. The approach is tested and evaluated for a plant of hot forging.

Flexible intralogistics systems use automated guided vehicles (AGV) to transport goods. In assembly and warehouses, AGVs and human workers often work side by side. For optimal navigation, AGVs must consider human movement and estimate future positions of workers. Using real-time locating systems (RTLS) to improve human-robot collaboration enables more energy-efficient and safer AGV wayfinding strategies. This paper gives a summary on the topics RTLS, AGV wayfinding and trajectory prediction and introduces the momentum-based approach to predicting future worker positions in factories and warehouses. The results show that ultra-wideband-based RTLS are very well suited for trajectory prediction in the production sector.

Tracking each workpiece provides two major advantages in forging technology. First, the matching of physical workpiece with the monitored process information facilitates root-cause analysis for product quality. Second, the following process steps can be adapted according to the incoming workpiece properties to improve the robustness of hot forging process chain. The paper presents a general tracking methodology and tagging experiments on aluminium and steel forgings for harsh drop-forging technology. Furthermore, a framework for streaming and processing large amounts of real-time data as well as a multidimensional approach to model and analyse the workpiece information for individual and batch-tracking are presented.

Zusammenfassung Änderungen der Funktionalität von Automatisierungssystemen werden zunehmend an der Software durchgeführt. Insbesondere bei verteilten Automatisierungssystemen lassen sich die Auswirkungen von Software-Änderungen oftmals nur schwer abschätzen. Dies stellt insbesondere Anlagenbetreiber vor große Herausforderungen bei der Absicherung von Funktions-Änderungen in der Betriebsphase. Dieser Beitrag beschreibt einen modellbasierten Ansatz, mit dem Auswirkungen von Software-Änderungen erkannt werden können und das betroffene Teilsystem automatisiert über modellbasierte Verifikationsverfahren abgesichert werden kann. Das präsentierte Konzept beruht auf der Komposition eines Systemmodells aus diskreten Verhaltensmodellen der Komponenten des Automatisierungssystems. Abschließend wird das Konzept anhand unterschiedlicher Änderungsszenarien evaluiert.

The heterogeneity and dynamic of IoT-systems pose new challenges for their simulation, which can be met by a modular co-simulation. Therefore several existing co-simulation approaches are presented and evaluated. A new concept for a dynamic co-simulation of IoT-systems utilizing a multi-agent-system is presented, wherein each IoT-component is simulated in a separate simulation tool. Each separate simulation is represented by an agent, and therefore able to enter a running co-simulation dynamically during runtime, which allows for a “Plug-and-Simulate” behavior. The connection between agents and simulation tools is realized by an interface concept. The presented concept is evaluated by a prototypical implementation.

Manufacturing individual products requires individual production. To implement the production of unique products with traditional engineering approaches is thoroughly complex. Therefore, decentralized production systems on the basis of cyber physical systems (CPS) promise a realization schema and thereby, the reduction of the overall systems’ complexity. Cyber physical systems are rather more complex in communication, time requirements and coordination, but are less complex from a modularization point of view and enable new ways of diagnosis and maintenance. The digitalization of production and new concepts of Industry 4.0 enable direct machine to machine communication, exchange of information and making of common decisions. An approach for future manufacturing in which individual products are scheduled incrementally is presented in this paper. In the presented approach, the product invites itself production resources to offer process steps in an auction. The product automatically ranks the offers according to requirements which are predefined by the customer. The best bids are asked to optimize their offers within a negotiation process. To evaluate our approach, a software-agent based demonstrator was implemented.

The increasing complexity of distributed automation systems requires new methods to verify the correct functionality. Model-based verification is an established approach to test the behavior of the system under test, before going into operation. To apply model-based techniques the overall system model of the automation system is needed. Due to the high complexity of the overall system and the changing dependencies caused by reconfigurations or software modifications an overall system model is seldom available or maintained. In this paper, we propose a modeling technique to manage the complexity of the overall system by modularization which is dedicated to distributed systems. This is presented in a formal way. Thereby, the modeling techniques regard the requirements of a service-oriented-architecture and the properties of automation systems, like interfaces to the technical process and parallelism caused by the distribution. In addition, we present calculation rules how to build up the overall system model automatically which can be used to verify system requirements.

Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are often unknown due to complex system dynamics, efficient anomaly detection is necessary. Conventional detection approaches rely on statistical and time-invariant methods that fail to address the complex and dynamic nature of anomalies. With advances in artificial intelligence and increasing importance for anomaly detection and prevention in various domains, artificial neural network approaches enable the detection of more complex anomaly types while considering temporal and contextual characteristics. In this article, a survey on state-of-the-art anomaly detection using deep neural and especially long short-term memory networks is conducted. The investigated approaches are evaluated based on the application scenario, data and anomaly types as well as further metrics. To highlight the potential of upcoming anomaly detection techniques, graph-based and transfer learning approaches are also included in the survey, enabling the analysis of heterogeneous data as well as compensating for its shortage and improving the handling of dynamic processes., 14 pages, 6 figures, 4 tables. Accepted for publication by Computers in Industry

Zusammenfassung Zur Ermittlung des Systemverhaltens werden beim Engineering in Kombination mit der modellgestützten Entwicklung häufig Simulationen eingesetzt. Dieser Beitrag stellt einen agentenkoordinierten Ansatz für die parallele Simulation von multiphysikalischen Problemstellungen auf heterogenen Rechenressourcen vor. Durch die unterschiedlichen eingesetzten Simulationsmethoden steigt hierbei die Zuverlässigkeit beim Erreichen der Ergebnisse.

Sharing knowledge and experience between test and diagnosis of production systems is a key to reduce faults and increase the availability. In distributed productions with several factories on different locations, the knowledge sharing becomes an important issue because direct communication between the test and maintenance teams is hardly possible. Based on test and diagnosis of an industrial quality assurance system we propose a combined decision support system for test and diagnosis that automatically shares the knowledge between both support functions. Using a decision support system motivates users to properly formalize their knowledge and reduces communication overhead. Maintainers are supported by an automatic fault diagnosis based on fault symptoms. Testers are supported by a test progress overview and a test case prioritization. By linking test cases to fault symptoms, the diagnosis is enriched by a test case recommendation for further fault isolation and the test case prioritization is expanded by a fault symptom coverage as a criterion that addresses in-field faults. As demonstrated with the industrial quality assurance system, both leads to synergy effects that improve the support functions and reduce overhead communication.

This paper highlights the necessity for a digital twin of production systems in the automotive industry. The survey on the use case of a digital twin of the production line is conducted with the production planning department of a major automobile manufacturer. The central part of the paper describes the current and future challenges for production planners in integration planning, which are determined by a survey. There are new trends in the automotive industry identified as a result of this survey. The study identifies exactly the information required by a digital twin of the production plant to improve the integration planning process. Furthermore, this survey provides answers to the benefits of integration planning by an automatically generated digital twin.

OPC UA is one of the most important communication protocols for IIoT applications in the automation domain. One important requirement for Industry 4.0 scenarios is standardized semantics, which is introduced into OPC UA through Companion Specifications. Nevertheless, OPC UA still lacks a comfortable interface for accessing these huge graphs, especially on the edge- and cloud-layer. Of course, there is a query language specified for OPC UA. However, to the best of our knowledge, there is no publicly available implementation.In this paper, we will present how SPARQL can be used to query OPC UA graphs natively and also will present rules to transform OPC UA queries into SPARQL queries. Furthermore, we will highlight some issues of OPC UA Query and how they can be bypassed by native SPARQL. Finally, we were able to correctly execute all nine example queries of OPC UA Part 4 Annex B (complex examples) with both approaches.

The communication protocol OPC UA is one of the most important IIoT enablers within the automation domain. OPC UA not only aims to provide interoperability on the transport layer, but also interoperability of the semantic layer shall be addressed based on so-called Companion Specifications. However, the lack of OPC UA formal semantics makes automatic validation of OPC UA data models impossible. Another drawback is the shortage of available tools for OPC UA, such as an implementation of the query engine for the specified OPC UA query language. In this paper we provide a formal translation of OPC UA models to the Semantic Web standard OWL, thus making OPC UA implicit semantics, that is described in the documentation, explicit, by means of OWL axioms. Moreover, we outline how this mapping can be used to offer validation and querying of OPC UA data models based on already existing Semantic Web technology.

Zusammenfassung Serviceorientierte Architekturen ermöglichen flexible Steuerungsstrukturen. Allerdings steigt dadurch die Komplexität beim Engineering der Komponenten des Steuerungsnetzwerks. Im Rahmen dieser Veröffentlichung wird aufgezeigt, dass eine modellgetriebene Entwicklung kombiniert mit einer wiederverwendbaren, modular-aufgebauten Systemsoftware den Umgang mit Komplexität beim Engineering vereinfachen kann.

Intelligent industrial automation devices based on cyber-physical systems can establish ad-hoc networks of manufacturing systems to produce individual products. To realize those, all network-participants, with their offered services and properties need to be described in an explicit semantic. A cloud-based concept is conceived in this paper which includes a semantic to describe the manufacturing systems, their characterization as well as the specified user requirements. A cloud-based concept called “e-Production” maps the abilities of the manufacturing systems with the customer requirements and identifies the best collaborative units for production regarding to energy, costs and other quality characteristics.

Increasing competition in the automotive industry makes cost-saving integration of more and more vehicle derivatives and variants such as sedans, coupes, cabriolets as well as electrical and combustion engine models into existing production systems necessary. At the same time, the production planners face a major challenge while integrating vehicle bodies-in-white. In contrast to the original concept and rough planning state, the automated production plants are continuously optimized during the detail planning phase as well as after the start of production as a result of improved processes and model upgrading. For fast integration of new vehicles, a current digital image of the real production plant - the so-called Digital Twin - is groundbreaking. This Digital Twin of a factory consists of a current bill of resources for cost calculation and a current layout planning state. The paper describes a concept for creating a Digital Twin of a body-in-white production system for the concept and rough planning projects. In the internal concept planning phase, planners do cost calculations and layouts for ordering factory suppliers. However, for integration planning, the original concept and rough planning project have to be updated. Therefore, a new concept has been developed which uses current information from the cyber-physical system and a current 3D scan to update the bill of resources and the layout planning on demand.

Ope UA is one of the most important communication protocols for Industry 4.0 applications in the automation domain. However, to really reach the status of an Internet-of-Things protocol, OPC UA also has to give some answers to upcoming cross-domain questions. Only if OPC UA is able to bridge the gap between several different domains, it will be accepted as real Internet-of-Things protocol and therefore supported even better by the APIs of the big IT players. One technology which already interconnects nearly every domain is the REST architecture. A lot of research already focused on the combination of these two technologies. However, in this paper we will show the first approach which covers all OPC UA requirements and therefore enables also use cases like server reconfiguration. We will also give some insights into our concept, which was contributed to the OPC UA specification as part of the Vl.04 release.

The paradigm of multi-agent systems is used in various application scenarios like manufacturing control or multi-agent assistance systems that aim at providing assistance to the user of the system in a specific task or situation. With an increasing number of applications of multi agent assistance systems in the industrial automation domain, the different architectures of these applications can be compared and differences and similarities can be identified. This paper presents the results of the literature review of 18 agent-based assistance systems from different domains. Within the reviewed architectures, seven different agent types could be identified. In a further step, design patterns based on the application domain and the level of automation have been identified and discussed. These design patterns can be used to facilitate the development of future multi agent assistance systems.

The Internet of Things (IoT) is about innovative functionality and better productivity by seamlessly connecting devices. But a major threat is the lack of architecture standards for the industrial Internet and connectivity in the IoT. This article reviews recent IoT architecture evolution and what it means for industry projects.

This paper presents a method to describe and increase the software quality of automation systems by a quantitative determination of the software reliability. Therefore, the software development process has been analyzed to identify the essential factors influencing the software reliability. These are the usage of a procedure model, the test factor and the human factor of the software developers themselves. These factors have been considered to create a neuro-fuzzy-based concept, which characterizes and consolidates them to realize an estimation of the software quality. The possibility to estimate the software quality, especially dynamically already during the software development process, will entail a huge benefit to programmers and manufacturers. Effects, methods or circumstances influencing the software quality in a negative or positive way can be numerically described and rated. On basis of that, necessary optimizations in the development process can be performed in time to guarantee a high quality of the software.

Internet-of-Things-(IoT)-systems will pose new challenges for the simulation of such systems, like the dynamic entering of components during runtime and the huge heterogeneity of the components. Various existing approaches for the simulation of IoT-systems are described. Additionally a concept is presented, which meets the described challenges. To satisfy the heterogeneity each component is modelled individually in separate modelling tools. The concept utilizes a multi-agentsystem to simulate the communication between the different models and to enable them to be integrated during simulation time. With the presented concept it is also possible to integrate real components into the simulation.

Modern manufacturing systems based on cyber-physical systems with a growing amount of software allow frequent updates and reconfigurations to adapt the systems to volatile usage scenarios in the production. A diverse system environment arises even for similar or equal subsystems based on the same platform used at different locations. A major challenge for such systems is the regression test after changes or updates. The resources for the regression test, in a dedicated test environment or deployed to the assembly lines, are limited. To plan the test in the best possible way, a lot of dependencies, relationships and experiences from former tests and tests from other locations have to be considered. This paper describes an assistance system which supports the planning of the regression test in such distributed manufacturing scenarios by combining manual modeling with automated data processing. Therefore the system calculates a cross-location test progress and suggests a prioritized test case sequence.

In this document we describe an approach to interconnect existing industrial automation systems with cooperation networks, like the internet of things, even if the industrial automation system uses a different communication protocol. To realize this, we have developed the concept of connector which provides system-dependent interfaces for different industrial automation and a universal interface for the cooperation network. Between these interfaces the messages are processed and translated by services, which act as a representation of the industrial automation system.

The increasing complexity of software in production systems leads to a growing relevance of testing. This trend will continue due to the flexibilization, and IT networking of production systems. This paper proposes a discussion about the requirements of future production systems which will be reconfigurable, decentralized controlled and will consist of autonomous components. From these predicted requirements, new challenges for functional testing are derived. To elucidate these challenges, three use cases illustrate when testing measures will be necessary within the operation phase of production systems.

Future automated systems are connected to each other in a production network with the goal to manage, schedule and operate the manufacturing process of individual products autonomously. Therefore a semantic language is necessary to empower the systems to fulfil their tasks. In this content eCl@ss is often mentioned as applicable to describe products. In this paper we conduct a study to describe an intelligent barrel with eCl@ss in terms of completeness in objective and functional description to evaluate if it is sufficient for the needs of cyber physical systems (CPS).

With growing complexity of premium cars, the end-of-line test systems also increase in complexity. The test systems have to provide more and more functionality like flashing of electronic control units (ECUs) and sensor calibration. Current end-of-line test systems evolved to complex networked IT-systems, which consist of various components and subsystems from different suppliers. Automotive production maintenance engineers are challenged to keep the availability of the test system on a high level to not cause production delays. In a case study with automotive test experts, we considered fault diagnosis and test case selection as two major tasks to maintain a high system availability. The experts combine their knowledge and experience about fault-prone system parts and former faults to optimize fault diagnosis and test case selection for regression testing. To support the experts to manage the growing complexity, we propose a combined fault diagnosis and test case selection assistance system. The combination of both techniques enables synergy effects by supporting the fault diagnosis with test case selection and by considering fault data in regression testing. This paper presents the concept of that combined assistant system and describes a prototypical realization used in an exemplary scenario.

Due to increasing requirements on functionality (e.g. self-diagnosis, self-optimization) or flexibility (e.g. self-configuration), future automation systems are demanded to be more and more intelligent. Therefore the systems are desired to learn new knowledge from other systems or its environment. The purpose of this work is to propose a prospective concept of learning assistant for networked automation systems. With the help of the assistant, an automation system can obtain new knowledge by collaborating with other systems to improve its prior knowledge. So that the system user is liberated from continuously providing new knowledge to an automation system.