Analytical workflows are heavily used in large and data intensive companies. An important application of such workflows in Siemens is equipment analytics when equipment KPIs and reports are computed by aggregating equipment's operational, master, and analytical data. In Siemens this data satisfies big data dimensions and this dependence poses significant challenges in authoring, reuse, and maintenance of analytical workflows by engineers and data scientists. In this work we propose to address these problems by relying on semantic technologies: we use ontologies to give a high level representation of equipment's operational and master data and offer a high level language to express KPIs over ontologies. We implemented our approach, integrated it with KNIME, and evaluated at Siemens. This is a preliminary work and we are excited about its further extensions.

Rule-based diagnostics of equipment is an important task in industry. In this paper 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 used 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 study computational complexity of SigRL: execution of diagnostic programs, provenance computation, as well as automatic verification of redundancy and inconsistency in diagnostic programs.

Rule-based diagnostics of power generating equipment is an important task in industry. 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 SemDia. The attendees will be able to write diagnostic programs in SemDia 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 SemDia provides to trace the origin of diagnostic results.

In industrial power generation plants, subsystem monitoring and analytics play a vital role in quantifying the knowledge about different factors that impact their overall performance. Multi-dimensional performance metrics, e.g. thermal efficiency, in-service time, mean-time-to-failure etc., are calculated that may have different data constraints, modelling techniques, and execution frameworks. Automating these calculations and combining multiple metrics to form a single performance index (e.g. reliability) is a challenging task as it requires considerable domain-specific expertise and consolidation of performance-related data and its underlying models. In this paper, we propose to use ontologies to assist domain analyst to first, capture appropriate semantic data of an individual performance metric, and later to provide means to integrate and execute multiple metrics to accurately reflect the overall performance of a plant. We present our prototypical implementation, its evaluation; furthermore, we discuss an ontology model that currently describes three distinct analytical models and its related data based on the case study of Siemens gas turbines. We also demonstrate how ontologies can support to infer the appropriate aggregation method in calculating composite indices.