Demo – Paper 651

Semantic Rule-Based Equipment Diagnostic Gulnar Mehdi, Evgeny Kharlamov, Ognjen Savkovic, Guohui Xiao, Elem Guzel Kalayci, Sebastian Brandt, Ian Horrocks, Mikhail Roshchin and Thomas Runkler Demo Download paper (preprint)

Abstract

Rule-based diagnostics of power generating equipment is an important task in industry. Diagnostics typically requires rules that process sensor signals, determine patterns in the signals, and produce notification messages when undesirable patterns are found. In Siemens such rules are data-dependent in the sense that they rely on specific characteristic of individual equipment and thus a complex diagnostic task may require a rule set with up to hundreds of such rules. Authoring and maintaining such rule sets is a challenging task that requires automation. In this demo we present how semantic technologies can enhance diagnostics. In particular, we present our semantic rule language sigRL that is inspired by the real diagnostic languages in Siemens. sigRL allows to write compact yet powerful diagnostic programs by relying on a high level data independent vocabulary, diagnostic ontologies, and queries over these ontologies. We present our diagnostic system SemDig. The attendees will be able to write diagnostic programs in SemDig using sigRL over 50 Siemens turbines. We also present how such programs can be automatically verified for redundancy and inconsistency. Moreover, the attendees will see the provenance service that SemDig provides to trace the origin of diagnostic results.