A centrifugal fan blade damage identification method based on the multi-level fusion of vibro-acoustic signals and CNN. (August 2022)

• Record Type: Journal Article
• Authors: Zhang, Tao; Xu, Feiyun; Jia, Minping
• Is Part Of: Measurement. Volume 199(2022)
• Publication Date: 2022-08
• Subjects: Centrifugal fan -- Acoustic vibration fusion -- Convolutional neural network -- Blade damage identification; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111475
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A new method for blade crack damage identification is proposed. A Multi-sensor data-adaptive synchronous weighted fusion algorithm is proposed. This method combines the advantages of data-level and feature-level fusion. The performance of the proposed method is better than other identification methods. 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.