A centrifugal fan blade damage identification method based on the multi-level fusion of vibro-acoustic signals and CNN

Abstract

A single sensor's blade damage identification is difficult because of the complex noise environment. At the same time, the multi-source signals include complete information of fault characteristics. Aiming to effectively fuse multi-sensor signals and improve identification accuracy, a centrifugal fan blade damage identification method based on the multi-level fusion of vibro-acoustic signals and a one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, acoustic and vibration signals are fused at the data level respectively by a data-adaptive synchronization weighted fusion algorithm. Secondly, the proposed 1D-CNN network extracts feature from the fused acoustic and vibration signals. Finally, the extracted features are fused by a fully connected layer. The experimental results show that the proposed method achieves 100% recognition accuracy at four speeds. By analyzing different signal to noise ratios (SNR), this method has higher diagnostic accuracy and better robustness compared with single sensor, single-level fusion, and other diagnostic methods.