风电机组关键部件故障趋势预测方法研究

项目名称： 风电机组关键部件故障趋势预测方法研究

项目编号： No.51305135

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 机械、仪表工业

项目作者： 滕伟

作者单位： 华北电力大学

项目金额： 23万元

中文摘要： 风电机组是可再生能源产业中的重要装备，而我国在役机组存在故障率高、寿命短等问题。关键部件故障趋势预测是以风电机组的主轴轴承、齿轮箱和发电机的运行状态为基础，结合历史数据、结构特性和运行工况，对机组未来可能出现的故障进行预测和判断，实现风电产业的预知维护和高效运营。项目针对风电机组传动链长、故障点多、频带覆盖宽等特点，研究其关键部件多载波故障调制模型；考虑风载荷等复杂激励对风电机组的影响，提出基于盲源解卷的方法进行振动信号分解，以获取故障敏感独立成分和噪声，构建反映故障变化程度的特征指标，实现故障特征与随机非故障能量的分离；以隐半Markov模型为理论基础，建立基于故障特征数据驱动的故障预测模型，预测关键部件故障状态发展趋势，通过对比分析与现场跟踪的方法验证预测模型的准确性。项目的研究对于形成风电产业合理高效的维修体制，降低风电机组的运营维护成本具有重要的理论与现实意义。

中文关键词： 风电机组；故障检测；特征提取；寿命预测；运行维护

英文摘要： Wind turbine is the crucial equipment in renewable energy industry, whereas there lie some disadvantages such as high failure rates and short life-span in our domestic wind turbine in service. Based on the current operation status of bearing of main axis, gearbox and generator in wind turbine, and combining histroy data, structural characteristics and operation conditions, failure prediction for key components is an attractive technology to predict and determine the future failure status and realize precognition maintenance and high efficient operation for wind power industry. In this project, a multi-carrier-wave failure modulation model is built with the consideration of the characteristics of long drivetrain, multi-failure points and wide frequency band etc. Considering the effect of random wind load acting on wind turbine, the blind source deconvolution is adopted to decompose the vibration signal and independent components which are sensitive to fault and noise are obtained. Through constructing the characteristic index reflecting the fault level, the fault feature is separated from the non-failure energe in the vibration signal. Through hidden semi-Markov model, a failure prediction model is built based on data driven method of extracted fault feature to predict the failure trend of key components in wind

英文关键词： Wind turbines；Fault detection；Feature extraction；Life prognostic；Operation and maintenance