Multi-sensor and multi-level information fusion model for compressor blade crack detection

Abstract

In order to complete the early crack detection of compressor blades under complex working conditions, a multi-sensor and multi-level information fusion model is proposed. Firstly, based on the designed correlated degree of cyclostationarity (CDCS) fusion rule, data-level fusion of multi-sensor signals is performed. The fused signal has more obvious fault features and lower redundancy. Then, based on the proposed multi-scale attention module (MSAM), a dual-branch one-dimensional convolutional neural network (1D-CNN) is designed to extract the features from the fused signal, and the dynamic feature fusion module is used to complete the feature fusion of the two branches, so as to obtain more rich and complete features. Finally, the fused features are input into the softmax classifier to complete the blade crack detection. The effectiveness and superiority of the proposed method are verified by a series of ablation experiments and comparison experiments under cross-speed conditions.