针对风电齿轮箱高速级齿轮传动系统齿根裂纹扩展程度识别难题,该文提出基于广义BP神经网络（GBPNN）的齿轮传动系统齿根裂纹故障模式识别方法。构建计及齿根裂纹扩展方向与路径的齿轮副时变啮合刚度解析模型及风电齿轮箱高速级齿轮-轴-轴承耦合的多自由度动力学模型,分析不同齿根裂纹扩展程度对系统振动特征的影响规律,并利用GBPNN对齿根裂纹故障模式进行识别。研究结果表明：齿轮故障振动周期冲击信号将沿着传动轴进行传递,但传动轴柔性会使其幅值产生明显的衰减;利用GBPNN并结合各轴段节点处振动加速度的峰值、峭度、统计矩阵参数以及方差,可有效实现对齿轮齿根裂纹故障模式的识别。
Abstract
To solve the problem that it is difficult to identify tooth crack in the high-speed stage of wind turbine transmission, this paper presents a method of tooth crack identification based on GBPNN. An analytical model of time-varying mesh stiffness considering the propagation path and direction of root cracks was constructed, and a multi-degree-of-freedom dynamic model of wind turbine high-speed stage were considered to establish. This paper analyzes the influence of root crack on dynamic characteristics, and the failure mode is identified by GBPNN. The results show that the gear fault vibration periodic shock signals will be transmitted along the transmission shaft, but the flexibility of transmission shaft will cause obvious attenuation of its amplitude; by using GBPNN and combining the peak, kurtosis, statistical matrix parameters and variance of the vibration acceleration at each shaft node, the gear root crack pattern can be effectively identified.
关键词
风电机组齿轮箱 /
动力学模型 /
故障识别 /
时变啮合刚度 /
齿根裂纹 /
广义BP神经网络
Key words
wind turbine gearbox /
dynamic models /
fault diagnosis /
time varying mesh stiffness /
tooth root crack /
generalized BP neural network（GBPNN）
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基金
国家重点研发计划（2018YFB1501300）; 中国博士后科学基金面上资助项目（2020M673125）; 中央高校基本科研业务费（2020CDCGJX026）
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