基于知识图谱和GPT模型的风电机组行星齿轮箱故障诊断  

Fault Diagnosis of Wind Turbine Planetary Gearbox Based on Knowledge Graph and GPT Model

作　　者：赵岩 ZHAO Yan(Datang Liangshan New Energy Co.,Ltd.,Chengdu 610000,China)

机构地区：[1]大唐凉山新能源有限公司,成都610000

出　　处：《微特电机》2025年第4期78-82,共5页Small & Special Electrical Machines

摘　　要：在风电机组行星齿轮箱故障诊断过程中,由于故障特征与故障状态之间的关系不唯一,直接利用行星齿轮箱输出信号进行故障诊断的效果无法保障。提出基于知识图谱和GPT模型的风电机组行星齿轮箱故障诊断研究方法,利用MOEA/D算法将故障特征选择问题分解为单目标子问题,结合差分进化策略,选定与故障关联性最大的特征;将选定的特征作为GPT模型的输入参量,在小样本学习机制下,输出风电机组行星齿轮箱故障知识图谱;根据行星齿轮箱运行数据在知识图谱中的映射结果,确定风电机组行星齿轮箱的具体故障状态。测试结果表明,该方法能够有效诊断不同故障状态,诊断精度达到0.98,诊断效果能显著提升。in the process of diagnosing faults in the planetary gearbox of wind turbines,the relationship between fault characteristics and fault states is not unique,the effect of fault diagnosis directly using the output signal of planetary gearbox cannot be guaranteed.A research method on fault diagnosis of wind turbine planetary gearbox based on knowledge graph and GPT model was proposed.Using MOEA/D algorithm to decompose the fault feature selection problem into single objective sub problems,combined with differential evolution strategy,select the feature with the highest correlation with the fault.Using the selected features as input parameters for the GPT model,output a knowledge graph of planetary gearbox faults in wind turbines under a small sample learning mechanism.Based on the mapping results of the planetary gearbox operation data in the knowledge graph,determine the specific fault state of the wind turbine planetary gearbox.The test results demonstrated that the method can effectively diagnosed various fault states,achieved a diagnostic accuracy of 0.98,and significantly improved the diagnostic performance.

关 键 词：知识图谱 GPT模型 风电机组行星齿轮箱 故障诊断 MOEA/D算法 差分进化策略 小样本学习 

分 类 号：TM359.9[电气工程—电机]