Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery. (August 2021)

• Record Type: Journal Article
• Authors: Jin, Tongtong; Yan, Chuliang; Chen, Chuanhai; Yang, Zhaojun; Tian, Hailong; Wang, Siyuan
• Is Part Of: Measurement. Volume 181(2021)
• Publication Date: 2021-08
• Subjects: Light neural network (LiNet) -- Deep learning (DL) -- Fault diagnosis -- Domain adaptation; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109639
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A light learning framework with fewer training parameters is proposed. LiNet can work directly with raw temporal signals. LiNet can realize quick, accurate, efficient rotating machinery fault diagnosis. Domain adaptation ability is improved by combining LiNet with AdaBN. Structural advantages of LiNet is verified in Discussion. Abstract: Many recent studies on deep learning models have focused on increasing accuracy for mechanical fault data sets, while disregarding the influences of model complexity on calculation efficiency and training time, resulting in the late detection of fault types. To recognize fault types quickly and accurately, and improve the efficiency of fault diagnosis, this study proposes an improved fault diagnosis method based on CNN for rotating machineries, called the light neural network (LiNet), with fewer parameters. The method uses raw vibration signals as input. Feature extraction of LiNet is realized by one convolutional block with 3 × 1 convolution kernels, two light_modules consisting of uneven-sized convolution blocks, residual structures, and one convolutional block with 1 × 1 convolution kernels. The classification stage identifies fault types through global average pooling. Finally, adaptive batch normalization (AdaBN) is combined with LiNet to explore the domain adaptation ability of the method. Experiments were carried out using two mechanical datasets, the Southeast University (SEU) gearbox dataset and Case Western Reserve University (CWRU) bearing dataset, to verify the effectiveness of the LiNet. Compared with existing methods, the proposed method is more accurate and achieves nearly 100% accuracy with normal signals while maintaining good performance under different working loads.