Design of industrial automation systems — Formal requirements in the engineering process

Böttcher, Björn; Badinger, Johann; Moriz, Natalia; Niggemann, Oliver

doi:10.1109/etfa.2013.6648148

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Regarding the projects, e.g. EfA [5], which deals with the development of intelligent assistance systems, can use the causality API for the planning of automation systems. The project IMPROVE [32], in which innovative modelling approaches for production systems are developed, can use the causality API for the detection of system causalities.…”

Section: Diagnosis and Planningmentioning

confidence: 99%

A Generic DigitalTwin Model for Artificial Intelligence Applications

Niggemann

Diedrich

Kühnert

et al. 2021

2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

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Services for Cyber-Physical Systems based on Artificial Intelligence and Machine Learning require a virtual representation of the physical. To reduce modeling efforts and to synchronize results, for each system, a common and unique virtual representation used by all services during the whole system life-cycle is needed-i.e. a DigitalTwin. In this paper such a DigitalTwin, namely the AI reference model AITwin, is defined. This reference model is verified by using a running example from process industry and by analyzing the work done in recent projects.

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Section: Diagnosis and Planningmentioning

confidence: 99%

A Generic DigitalTwin Model for Artificial Intelligence Applications

Niggemann

Diedrich

Kühnert

et al. 2021

2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

Self Cite

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“…A grammar-based design synthesis generates automation system solutions for these requirements. This approach was firstly outlined in [12]. A typical design scenario is described and used as an explanatory example in the following.…”

Section: Solution Approachmentioning

confidence: 99%

“…But, as stated in [6], 'there is no consensus on a notation to define attributes' of an EFM. As explained in preceding work ( [12]), AD4AS differentiates basic templates consisting of only one feature and container templates with several features such as the template saw in figure 3. The composition of templates by a designer results in a hierarchical composition of required entities with attributes and constraints which is expressed as EFM.…”

Section: Solution Approachmentioning

confidence: 99%

Assisted design for automation systems — From formal requirements to final designs

Moriz

Böttcher

Niggemann

et al. 2014

Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA)

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In this paper, the authors present an engineering approach for generating automation system solutions based on formalised requirements. This enables assistant systems which guide engineers during the design phase of todays more and more complex automation systems. A software prototype is used for the evaluation of this approach in practice. The main contribution is to directly use a formal requirements model as input for the automated synthesis of automation systems and to formalise the expert knowledge for this synthesis. The result are consistent, maintainable automation systems and with that shorter and reproducible development cycles.

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“…The availability of a machine is highly depended on the expertise of the person handling it and due to lack of experience and inappropriate use of the machine could result in unplanned outage [1]. The need to automate the decision making process led to automation of technical processes of an industry which resulted in more complex automation systems in industry [2]. Preparing a machine to perform a specific task autonomously based on the input data is the primary function of Machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Building Intelligent Conveyor System using classification techniques in a logistics Industry

2019

ijeat

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 Abstract: Machine learning techniques plays an important role in knowledge discovery and assists humans in decision making. They help to detect patterns and predict the actions/outcome. In a complex industrial environment mode of operations of a machine depends on various internal and external parameters which are often done using expert judgement method which is not accurate and results in machine breakdown thereby resulting in unplanned outage. In this paper, we discussed and demonstrated how machine learning algorithms can help to handle conveyor systems autonomously in an optimum way without any human intervention. A conveyor belt system operational data is used to select the appropriate classification technique for the selected dataset. The details of the dataset collected, algorithms used and the test results are discussed in this paper.

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Design of industrial automation systems — Formal requirements in the engineering process

Cited by 8 publications

References 14 publications

A Generic DigitalTwin Model for Artificial Intelligence Applications

A Generic DigitalTwin Model for Artificial Intelligence Applications

Assisted design for automation systems — From formal requirements to final designs

Building Intelligent Conveyor System using classification techniques in a logistics Industry

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