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.

Automatic processing of data for the purpose of determining operating states and identifying faults has become essential for many modern industrial systems. Typical sources of this data include hundreds of sensors mounted at the industrial machinery measuring qualities such as temperature, vibration, pressure, and many more. However, sensors are complex technical devices, which means that they can fail and their readings may contain noise or imprecise values. Such low quality data makes it hard to solve the original task of assessing system and process status. We present an approach which brings together several well-known techniques from computer science and statistics and enhances monitoring of technical systems by improving results of detection and correction of data quality issues in sensor data. The application domain and the dependencies between its objects are represented as a knowledge-based model, while statistics identifies data anomalies, such as outlying or missing values, in sensor measurement data. Combining information from the knowledge-based model and statistical computations allows to validate and improve data analysis results. We demonstrate the proposed approach on a real-world industrial use case from the power generation domain. Our evaluation shows that the combined solution improves precision indexes while maintaining high accuracy and recall values.