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Data-driven assistance functions for industrial automation systems

: Windmann, Stefan; Niggemann, Oliver

Postprint urn:nbn:de:0011-n-3700230 (1.7 MByte PDF)
MD5 Fingerprint: 21abf9dfe4006f34193d33284af35104
Erstellt am: 10.12.2015

12th European Workshop on Advanced Control and Diagnosis, ACD 2015 : 19–20 November 2015, Pilsen, Czech Republic
Bristol: IOP Publishing, 2015 (Journal of physics. Conference series 659)
Art. 012045, 12 S.
European Workshop on Advanced Control and Diagnosis (ACD) <12, 2015, Pilsen>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

The increasing amount of data in industrial automation systems overburdens the user in process control and diagnosis tasks. One possibility to cope with these challenges consists of using smart assistance systems that automatically monitor and optimize processes. This article deals with aspects of data-driven assistance systems such as assistance functions, process models and data acquisition. The paper describes novel approaches for self-diagnosis and self-optimization, and shows how these assistance functions can be integrated in different industrial environments. The considered assistance functions are based on process models that are automatically learned from process data. Fault detection and isolation is based on the comparison of observations of the real system with predictions obtained by application of the process models. The process models are further employed for energy efficiency optimization of industrial processes. Experimental results are presented for fault detection and energy efficiency optimization of a drive system.