Fault diagnosis based on comprehensive geometric characteristic and probability neural network

作者：Chen, Xiaoyue; Zhou, Jianzhong^*; Xiao, Han; Wang, Ercheng; Xiao, Jian; Zhang, Huifeng

来源：Applied Mathematics and Computation, 2014, 230: 542-554.

DOI：10.1016/j.amc.2013.12.122

• Fault diagnosis
• Macroscopic Euler-number
• Fuzzy convex-concave feature
• Boundary-layer feature
• Shaft orbit

Fault diagnosis is very important to ensure the safe operation of hydraulic generator units (HGU). Shaft orbit identification has been highlighted as an effective method for HGU fault diagnosis in the past few years. The purpose of this paper is to propose a novel shaft orbit identification method based on comprehensive geometric characteristics and probability neural network (CGC-PNN) for HGU fault diagnosis. In this method, macroscopic Euler-number (ME), fuzzy convex-concave feature (FCC) and boundary-layer feature (BL) are proposed to represent shaft orbits from three different aspects: structure, region and boundary. Therefore, the most effective and comprehensive image information is fully integrated by the feature vector composed of ME, FCC and BL. Furthermore, probability neural network (PNN) has been introduced as the classifier according to the simplicity of the feature vector. Finally, we apply the proposed method to 800 samples and the experimental results indicate that the proposed method can achieve an efficient accuracy in HGU fault diagnosis.

• 出版日期2014-3-1
• 单位华中科技大学