Cochlear Filter Cepstral Coefficients of Acoustic Signals for Mechanical Faults Identification of Power Transformer

Acoustic recognition was always used in fault diagnosis in mechanical equipment and observation systems. For the operated transformer, acoustic signals were mainly originated from mechanical vibrations of winding and core and then emitted in the air. Consequently, abundant information about mechanical structures and running conditions of transformer was contained in acoustic signals. Based on the better recognition ability and robust of human hearing system, cochlear filter cepstral coefficients (CFCCs) of acoustic signals of transformer was calculated in the paper. Through the construction of training set and testing set from the CFCCs, the probabilistic neural network (PNN) was trained to recognize the typical mechanical faults of transformers. The load experiment of a dry type transformer with the voltage rating of 10kV was made in different load power when the transformer is in normal and typical mechanical faults conditions. Calculated results have shown that the proposed method is capable of describing the features of acoustic signals and recognizing the mechanical faults of power transformer with high accuracy.