ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Online Classification of Non-Stationary Industrial Data Streams

12 September 2017, 16:20 – 16:40

Abstract

Submitted by
Idris Eckley
Authors
Idris Eckley (Lancaster University)
Abstract
The long-established need to be able to accurately detect, diagnose and act in (or close to) real-time on process data has become ever more apparent, and computationally challenging, with the advent of high-resolution sensors capable of recording many variables at kHz or even GHz. Such signals are typically non-stationary in structure, with potentially time-varying dependence between the various components. In this talk I will motivate and introduce some recent work in this area that focuses on the challenge of online estimation of the time-dependent coherence and its use within a novel, dynamic classification approach for data streams arising from a collaboration with an industrial partner.

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