Two-level fault diagnosis RBF networks for auto-transformer rectifier units using multi-source features

The auto-transformer rectifier unit (ATRU) is one of the most widely used avionic secondary power supplies. Timely fault identification and location of the ATRU is significant in terms of system reliability. A two-level fault diagnosis method for the ATRU using multi-source features (MSF) is proposed in this paper. Based on the topology of the ATRU, three key electrical signals are selected and analyzed to extract appropriate features for fault diagnosis. Mathematic expressions and simulation results of the feature signals under different fault modes are presented in the paper. Therefore, a unique MSF system is developed and a two-level fault diagnosis method based on radial basis function network groups is proposed. On the first level, the overall fault set is classified into three subsets and then on the second level, three radial basis function neural networks are constructed and trained to realize accurate fault localization. To verify the diagnosis performance of the proposed method, several comparative tests are implemented on a 12-pulse ATRU system, which shows that this method has a lower computational cost, better diagnostic accuracy and increased stability when compared with alternative methods.