Weak crack identification of compressor impeller with hybrid methods of PNNs and SVD

Abstract

Damage to a compressor impeller can sometimes cause serious accidents, heavy casualties and property loss, etc. Therefore, it is necessary to conduct damage monitoring and identification for the compressor impeller. A damage identification method based on probabilistic neural networks (PNNs) with modal information fusion is proposed for a compressor impeller. The modal shape of the compressor impeller can be acquired by experimental modal analysis. Combining waveform capacity dimension, a singular value decomposition is applied to extract damage feature information from the system modal shape. The two damage indicators are fused by a multi-dimensional feature vector. Finally, a PNN model is constructed and used to identify structural damage. The experimental results indicate that the proposed method is effective in detecting damage to the compressor impeller.