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Vibration analysis of rotating machinery can give an indication of possible faults, thus allowing maintenance before further damage occurs. Automating this analysis allows machinery to be run uattended for longer periods of time. This paper describes the use of neural networks as a method for automatically classifying the machine condition from the vibration time series. Several methods for the extraction of features to use as neural network inputs are described and compared. These methods are based upon measuring the zero lag higher-order statistics of the measured vibration time series. The time series for horizontal and vertical vibration signals are considered separately and combined to produce time series based upon the radius of the vibration displacement. The experimental set-up used for simulating unbalance and rub faults is described and classification success rates based upon each method reported. In particular, a classification success rate of over 99 per cent has been achieved.

We present the initial results from the FHPCA Supercomputer project at the University of Edinburgh. The project has successfully built a general-purpose 64 FPGA computer and ported to it three demonstration applications from the oil, medical and finance sectors. This paper describes in brief the machine itselfMaxwell -its hardware and software environment and presents very early benchmark results from runs of the demonstrators.

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