一种基于生成模型零样本学习策略的转向架未知故障诊断方法

• Format: Patent
• Language: Chinese
• Published: 27.05.2025
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; MEASURING; PHYSICS; TESTING; TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR

本发明涉及高速列车转向架故障检测技术领域，公开一种基于生成模型零样本学习策略的转向架未知故障诊断方法，使用多体动力学分析软件SIMPACK对于电力机车进行转向架机械故障模拟实验收集正常与故障数据；针对列车运行影响因素进行故障属性语义矩阵搭建；根据数据属性与故障数据进行特征提取；搭建基于扩散模型的未知故障数据特征样本生成模型，利用随机噪声与未知类故障属性组合进行未知类故障的数据特征生成；构建故障诊断分类模型，实现数据特征到故障属性再到故障类别的映射。本发明完成针对高速列车转向架已知类故障与未知类故障的诊断，提高故障诊断的准确率和泛化性，为高速列车转向架的安全使用提供全面保障。 The invention relates to the technical field of high-speed train bogie fault detection, and discloses a bogie unknown fault diagnosis method based on a generative model zero sample learning strategy, and the method comprises the steps: carrying out a bogie mechanical fault simulation experiment on an electric locomotive through employing multi-body dynamics analysis software SIMPACK, and collecting normal and fault data; establishing a fault attribute semantic matrix for the train operation influence factors; performing feature extraction according to the data attributes and the fault data; building an unknown fault data feature sample generation model based on a diffusion model, and performing unknown fault data feature ge