An efficient CNN-transformer hybrid approach for water turbine unit failure prediction

Article type: Research Article

Authors: Zhang, Kefeng^* | He, Ming | Guo, Junxin

Affiliations: Guodian Sinkiang Jilintai Hydro Power Development Co., Ltd. Lli Kazak, Xinjiang, China

Correspondence: [*] Corresponding author: Kefeng Zhang, Guodian Sinkiang Jilintai Hydro Power Development Co., Ltd. Lli Kazak, Xinjiang 835199, China. E-mail: [email protected].

Abstract: Water Turbine Unit is the core equipment of water power generation. It is the most important research object in hydroelectric power. The analysis of daily monitoring data of Water Turbines can evaluate its running state and avoid the loss caused by failure. In this paper, A hybrid neural network architecture which CNN combines Transformer is proposed to evaluate the health state of water turbines based on multivariate long time series data. It has two core components: (i) dilated convolution applies to capture low-level and local semantic information, then the output of convolutional layers is divided into subseries-level patches by time, these patches are regarded as input tokens of Transformer layers; (ii) utilizing self-attention of Transformer to extract high-level and global semantic information. Patching design naturally has benefit as follow: (i) local semantic information is retained in Transformer input tokens and high-level semantic confirm to common sense of human understanding through low-level construction; (ii) the length of Transformer input sequence is greatly shorted and attention can be more concentrated compared with point-wise form. Meanwhile, the computation and memory usage reduces at the same time. The experimental result indicates that the hybrid architecture can achieves excellent performance in time series understanding.

Keywords: CNN and Transformer hybrid, health condition, water turbine, multivariate time series, time series analysis

DOI: 10.3233/JCM-237074

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 823-834, 2024