Research on the shaft orbit of hydropower units based on convolutional neural network and the fine-grained indicators. Issue 1 (1st June 2022)

• Record Type: Journal Article
• Authors: Pan, H; Luo, Z L; Xu, J J; Feng, F
• Is Part Of: IOP conference series. Volume 1037:Issue 1(2022)
• Publication Date: 2022-06-01
• Subjects: hydropower unit -- convolutional neural network -- axis track -- fine granularity -- image recognition; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/1037/1/012002
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Shaft orbit is one of the most representative characteristics of vibration signals which reflect the healthy and breakdown status of hydropower units. However, the traditional pattern recognition methods consisting of feature extraction and classifier could not been able to identify the kinds of faults and the degree of severity effectively. In this paper, a novel intelligent method based on convolutional neural network (CNN) and the fine-grained indicators is proposed for auto-diagnosis of faults with different severity levels in hydropower units. Firstly, four fine-grained indicators have been put forward to describe the severity of four kinds of common shafting faults. Secondly, a CNN fault diagnosis model is designed to extract high discriminative features and identify fault categories. Twelve kinds of shaft orbit samples were input into the model with the same size of 64×64.Through reaction of two convolution layers and two pooling layers in feature extraction module, the graphic features were extracted. The faults of hydropower units were identified after the fully connected layer and the SoftMax classifier. The experimental results show that the accuracy of the proposed approach for the fault with different severity is 98.33%, which is better than the traditional learning method. The proposed method retains the pattern information in the axis trajectory to the maximum extent and has the ability to accurately capture features of various severity faults.