Experiments in the application of neural networks to rotating machine fault diagnosis

Reports a set of experiments performed to test the ability of a backpropagation network to classify the condition of an operating desktop fan based on its vibration signature. The trained network was used to detect and classify faults commonly occurring in industrial fans, i.e. impeller unbalance and cracked impeller blade. The discussion of the experimental results raises a number of issues relating to the application of this technique to the condition monitoring of industrial fans