Thomas Hubauer

How Semantic Technologies Can Enhance Data Access at Siemens Energy

We present a description and analysis of the data access challenge in the Siemens Energy. We advocate for Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. Finally, we describe our preliminary installation and evaluation of the platform in Siemens.

Optique: OBDA Solution for Big Data

Accessing the relevant data in Big Data scenarios is increasingly difficult both for end-user and IT-experts, due to the volume, variety, and velocity dimensions of Big Data.This brings a hight cost overhead in data access for large enterprises. For instance, in the oil and gas industry, IT-experts spend 30-70% of their time gathering and assessing the quality of data [1]. The Optique project ( http://www.optique-project.eu/ ) advocates a next generation of the well known Ontology-Based Data Access (OBDA) approach to address the Big Data dimensions and in particular the data access problem. The project aims at solutions that reduce the cost of data access dramatically.

Empowering Adaptive Manufacturing with Interactive Diagnostics: A Multi-Agent Approach

This paper presents a novel approach towards proactive manufacturing control that integrates automated diagnostics with human interaction, resulting in a flexible adaptation of machine capabilities which helps to avoid damage in case of abnormalities. The model-based interpretation process supports predictive diagnostics using abductive reasoning, relying on plausibility thresholds and human intervention to resolve the resulting ambiguity between competing solutions. This enables the system to detect and avoid potential failure states before they actually occur. The proposed architecture additionally integrates intelligent products as mobile sensors, improving robustness and dependability of the production system.

Embedded EL + reasoning on programmable logic controllers

Many industrial use cases, such as machine diagnostics, can benefit from embedded reasoning, the task of running knowledge-based reasoning techniques on embedded controllers as widely used in industrial automation. However, due to the memory and CPU restrictions of embedded devices like programmable logic controllers (PLCs), state-ofthe- art reasoning tools and methods cannot be easily migrated to industrial automation environments. In this paper, we describe an approach to porting lightweight OWL 2 EL reasoning to a PLC platform to run in an industrial automation environment. We report on initial runtime experiments carried out on a prototypical implementation of a PLC-based

Semantic-Guided Feature Selection for Industrial Automation Systems

\mathcal{EL}

Extending statistical data quality improvement with explicit domain models

+-reasoner in the context of a use case about turbine diagnostics.

Case Studies and Evaluation

Modern industrial automation systems incorporate a variety of interconnected sensors and actuators that contribute to the generation of vast amounts of data. Although valuable insights for plant operators and engineers can be gained from such data sets, they often remain undiscovered due to the problem of applying machine learning algorithms in high-dimensional feature spaces. Feature selection is concerned with obtaining subsets of the original data, e.g. by eliminating highly correlated features, in order to speed up processing time and increase model performance with less inclination to overfitting. In terms of high-dimensional data produced by automation systems, lots of dependencies between sensor measurements are already known to domain experts. By providing access to semantic data models for industrial data acquisition systems, we enable the explicit incorporation of such domain knowledge. In contrast to conventional techniques, this semantic feature selection approach can be carried out without looking at the actual data and facilitates an intuitive understanding of the learned models. In this paper we introduce two semantic-guided feature selection approaches for different data scenarios in industrial automation systems. We evaluate both approaches in a manufacturing use case and show competitive or even superior performance compared to conventional techniques.

Optique System: Towards Ontology and Mapping Management in OBDA Solutions

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.

METHOD AND DEVICE FOR CONTROLLING AN INDUSTRIAL SYSTEM

This chapter reports on the practical results of our research. We first present an overview of the RAbIT system, which we developed as library for solving relaxed abduction problems. RAbIT provides both a glass-box algorithm for \( {\mathcal{E}\mathcal{L}}^{ + } \) knowledge bases, and a black-box variant with a broader range of supported representation languages.