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arXiv · 1802.00625

Real-Time-Data Analytics in Raw Materials Handling

Christopher Josef Rothschedl·Roland Ritt·Paul O'Leary·Matthew Harker·Michael Habacher·Michael Brandner
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Abstract

This paper proposes a system for the ingestion and analysis of real-time sensor and actor data of bulk materials handling plants and machinery. It references issues that concern mining sensor data in cyber physical systems (CPS). The advance of cyber physical systems has created a significant change in the architecture of sensor and actor data. It affects the complexity of the observed systems in general, the number of signals being processed, the spatial distribution of the signal sources on a machine or plant and the global availability of the data. There are different definitions for what constitutes cyber physical systems: the most succinct and pertinent to the work shown in this paper is the definition given by the IEEE: A CPS is a system with a coupling of the cyber aspects of computing and communications with the physical aspects of dynamics and engineering that must abide by the laws of physics. This includes sensor networks, real-time and hybrid systems. Results computed from sensor and actor data must obey the equations used for modelling the physics of the observed system - this fundamentally poses an inverse problem. Such problems are not covered sufficiently by literature addressing mining of sensor data. Even available standard books on mining sensor data do not discuss the special nature of sensor data. Typically, present approaches of mining data rely on correlation as being a sole, reliable measure for significance. It is not taken into account that the inverse solutions to the model-describing equations are required to establish a semantic link between a sensor observation and its precedent cause. Without this link - without causality - there can be no physics based knowledge discovery.

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Christopher Josef Rothschedl, Roland Ritt, Paul O'Leary, Matthew Harker, Michael Habacher, Michael Brandner. 2018-02-02. Real-Time-Data Analytics in Raw Materials Handling. https://arxiv.org/abs/1802.00625

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