Modelling and predicting flow regimes using wavelet representations of ERT data


D. Alex Goodwin, Robert G. Aykroyd, & Stuart Barber.
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 sub-optimal behaviour before it has occurred. To be of practical use this must all be computationally efficient allowing real-time 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 post-processing 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 longterm 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 models can be used to classify flow into one of a set of regimes, either for later study of the flow profile or for monitoring of an ongoing process. We illustrate our methods by application to simulated data sets.

Some key words:Wavelets, logistic regression, classification, process monitoring.


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