Mechanical condition monitoring of vacuum circuit breakers using artificial neural network

Using the Vibration signatures obtained during the operations as the original data, a mechanical condition monitoring method for vacuum circuit breaker is developed in this paper. The method combined the time-frequency analysis and the condition recognition based on artificial neural network. During preprocessing, the vibration signature was decomposed into individual frequency bands using the arithmetic of wavelet packets. The signal energy in the main frequency bands was used to form the condition feature vector, which was input to the artificial neural network for condition recognition. By introducing the parameter of approximation degree, a new recognition arithmetic based on Radial Basis Function was constructed. This approach could not only distinguish these conditions that belong to different known condition modes but also distinguish new condition modes.