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Automatic model separation and application to diagnosis in industrial automation systems

: Windmann, Stefan; Niggemann, Oliver

Postprint urn:nbn:de:0011-n-3372101 (2.9 MByte PDF)
MD5 Fingerprint: 1df8ef9e131143129603e2d104a71d97
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Erstellt am: 28.4.2015

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
ICIT 2015, IEEE International Conference on Industrial Technology. Vol.3 : 17-19 March 2015, Seville, Spain
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4799-7800-7
ISBN: 978-1-4799-7801-4
International Conference on Industrial Technology (ICIT) <2015, Sevilla>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

In this paper, automatic separation of hybrid system models for industrial automation systems is considered. The proposed method facilitates efficient separation of system-level models into component-level models. Such component-level models allow for model-based diagnosis since a close relation exists between anomalies on a component-level and fault causes. The approach is based on the concept of separation variables which relate models for components such as electric drives to system modes, i.e. phases of continuous system behavior. For automation systems, the system modes are defined by sequences of discrete control events. Separation variables determine for each system mode active components which contribute to the overall output signal on the system-level. System modes and separation variables are automatically learned from training data with normal system behavior. The proposed method allows for both model-based diagnosis and efficient model learning.