 
Summary: 4 th World Congress on Industrial Process Tomography, Aizu, Japan
Modelling and predicting flow regimes using wavelet
representations of ERT data
D. A. Goodwin, R. G. Aykroyd and S. Barber
Department of Statistics, University of Leeds, Leeds, LS2 9JT, UK, robert@maths.leeds.ac.uk
ABSTRACT
The aim of industrial process control is to convert measurements, taken while the process is evolving,
into parameters which can be used to control the process. That is to monitor an active process and
predict unacceptable or suboptimal behaviour before it has occurred. To be of practical use this must
all be computationally efficient allowing realtime feedback. Electrical tomography measurements have
the potential to provide useful data without intruding into the industrial process, but produce highly
correlated and noisy data, and hence need sensitive analysis. The commonly used approaches,
based on regularized image reconstruction are slow, and still require image postprocessing to extract
control parameters. An alternative approach is to directly work with the measurement data.
Wavelets have proven to be highly effective at extracting information from noisy data. We demonstrate
the use of wavelets in relating such electrical measurements to the state of flow within a pipe, and
hence in classifying the state of the flow to one of a number of regimes. Wavelets are an ideal tool for
our purpose since their multiscale nature enables the efficient description of both transient and long
term signals. Furthermore, only a small number of wavelet coefficients are needed to describe
complicated signals and the wavelet transform is computationally efficient. The resulting wavelet
