Skip to main navigation Skip to search Skip to main content

Design and application of a multi-modal process tomography system

  • Brian S. Hoyle
  • , Xiaodong Jia
  • , F. J. W. Podd
  • , H. I. Schlaberg
  • , H. S. Tan
  • , M. Wang
  • , Robert M. West
  • , Richard A Williams
  • , T. A. York

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a design and application study of an integrated
multi-modal system designed to support a range of common modalities:
electrical resistance, electrical capacitance and ultrasonic tomography. Such
a system is designed for use with complex processes that exhibit behaviour
changes over time and space, and thus demand equally diverse sensing
modalities. A multi-modal process tomography system able to exploit
multiple sensor modes must permit the integration of their data, probably
centred upon a composite process model. The paper presents an overview of
this approach followed by an overview of the systems engineering and
integrated design constraints. These include a range of hardware oriented
challenges: the complexity and specificity of the front-end electronics for
each modality; the need for front-end data pre-processing and packing; the
need to integrate the data to facilitate data fusion; and finally the features to
enable successful fusion and interpretation. A range of software aspects are
also reviewed: the need to support differing front-end sensors for each
modality in a generic fashion; the need to communicate with front-end data
pre-processing and packing systems; the need to integrate the data to allow
data fusion; and finally to enable successful interpretation. The review of the
system concepts is illustrated with an application to the study of a complex
multi-component process.
Original languageEnglish
Pages (from-to)1157-1165
Number of pages9
JournalMeasurement Science and Technology
Volume12
Publication statusPublished - 2001

Fingerprint

Dive into the research topics of 'Design and application of a multi-modal process tomography system'. Together they form a unique fingerprint.

Cite this