Abstract: A method for detecting partial discharge events comprising analysing an output from the cable using one or more substantially shift invariant wavelet transforms, and using the analysed signal to identify one or more peaks associated with a partial discharge. Preferably, the shift invariant transform is a stationary wavelet transform (SWT).

Abstract: A neural network for predicting values in non-linear functional mappings having a single hidden layer function generator (12) and an output layer (40). The single hidden layer function generator (12) is operable to receive one or more mapping inputs (x1) and generate a plurality of terms (14) from each mapping input. The plurality of terms generated by the single hidden layer function generator (12) includes at least one trigonometric term selected from the group comprising sin(x1), sin(2x1), sin(3x1), cos(x1), cos(2xl), cos(3xl), cosec(xl), cotan(xl), and being free of Gaussian and Sigmoidal terms.

Abstract: A method and apparatus (12-20, 32) for automatically locating faults in a network of signal carriers (cables) utilises an interrogation waveform which is pulse-like in form and receives composite reflected signals from the carriers which are a time distributed collection of individual reflections. These are sampled and compared using the function of an adaptive filter (52) to minimise the difference signal. The difference signals are analysed to determine significant points of departure which identify the fault location which is then represented as a distance value.