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2021-04-09
Song, M., Lind, M..  2020.  Towards Automated Generation of Function Models from P IDs. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 1:1081—1084.
Although function model has been widely applied to develop various operator decision support systems, the modeling process is essentially a manual work, which takes significant efforts on knowledge acquisition. It would greatly improve the efficiency of modeling if relevant information can be automatically retrieved from engineering documents. This paper investigates the possibility of automated transformation from P&IDs to a function model called MFM via AutomationML. Semantics and modeling patterns of MFM are established in AutomationML, which can be utilized to convert plant topology models into MFM models. The proposed approach is demonstrated with a small use case. Further topics for extending the study are also discussed.
2020-02-24
Brenner, Bernhard, Weippl, Edgar, Ekelhart, Andreas.  2019.  A Versatile Security Layer for AutomationML. 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). 1:358–364.
The XML-based data format AutomationML enables vendor-independent exchange of design data between discipline-specific design tools. It is based on Computer Aided Engineering Exchange (CAEX) and hence, compatible with the W3C standards XMLEnc (XML encryption) and XMLDsig (XML signatures). However, despite the importance of protecting engineering data, so far no concept has been presented to ensure and control on a fine-grained level the confidentiality, authenticity and accessibility of information stored in AutomationML files. In this paper, we introduce a basic access control scheme for AutomationML that enables to define user read and write access for each component. Furthermore, the scheme supports non-repudiation based on a change history and so-called "signature chains". It is also capable of supporting views and restricted access to components. The scheme is based on cryptographic measures – i.e. cryptographic hashing, symmetric encryption, signatures, and asymmetric encryption – and enforces its access control mechanisms through encryption to protect against unauthorized reading, and through signature chains to protect against unauthorized manipulation and to ensure non-repudiation. This approach has the benefit to be independent of the underlying file and operating system, storage location, etc., and it keeps full CAEX-conformity by extending AutomationML.This concept can serve as basis for software tools that support AutomationML and want to integrate access control features directly into AutomationML.