Intelligent acoustic detection of defective porcelain station post insulators

This work presents an intelligent acoustic methodology for detection of defective porcelain station post insulators, which are widely used in substations in the form of column presenting several sheds. The acoustic emission inspection aims to detect cracks or fissures in a particular shed, which will have its insulating capacity severely decreased, if cracked. The test is done by gently striking the shed with an appropriate instrument, connected to the tip of an insulated pole. The resulting acoustic emissions are recorded at the substation. A database is created with these audio files and two approaches are considered in order to emphasize the important attributes and to compact the information: Wavelet Energy Coefficients and Spectral Subband Centroid Energy Vectors. Finally, to add reliability, automation and ability to generalize and to adapt to new situations, an Artificial Neural Network is employed. The average classification accuracy is above 62% when using Wavelet Energy Coefficients and above 98% when using Spectral Subband Centroid Energy Vectors.