Interaction of model-driven engineering and signal-based online monitoring of production systems: Towards Requirement-aware evolution

Haubeck, Christopher; Lamersdorf, Winfried; Ladiges, Jan; Fay, Alexander; Fuchs, Julia; Legat, Christoph; Vogel‐Heuser, Birgit

doi:10.1109/iecon.2014.7048868

Cited by 3 publications

(1 citation statement)

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“…However, model based analysis and simulation only verifies models of the systems but not the systems themselves. Often assumptions about properties have to be made which may results in erroneous models which render the verification less useful [20]. Accordingly, all assumptions made during modelling have to be true to verify the real system.…”

Section: Model-driven Engineering and Model-driven Testingmentioning

confidence: 99%

Automated Determining of Manufacturing Properties and Their Evolutionary Changes from Event Traces

2016

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Production plants are usually kept in operation for several decades. During this long operational phase operation requirements and other production conditions change frequently. Accordingly, the plants have to be adjusted in behavior and/or structure by adapting software and physics of the plant to avoid degeneration. Unfortunately, in industrial practice, changes, especially smaller ones, are often performed ad-hoc without appropriate adaptation of formal models or documentation. As a consequence, knowledge about the process is only implicitly available and an evaluation of performed changes is often omitted, resulting in sub-optimal production performance. Present research approaches to overcome these deficiencies usually concentrate on (a) manual modelling with manual or automatic analysis on a high level of abstraction; or (b) on automatic model generation from observations without lifting gathered knowledge to easy interpretable indicators. The approach presented in this paper combines both methods (a) and (b) by learning models from observation of input / output signals of the production plant's control system. Semantics are added by using a priori information modelling which is less tedious compared to modelling the process itself. The learned models are used to automatically detect changes by continuously comparing their behavior with real plant behavior during operation as well as to evaluate performed changes. An analysis of the models results in high-level property values such B Jan Ladiges

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Section: Model-driven Engineering and Model-driven Testingmentioning

confidence: 99%