大数据处理技术在风电机组齿轮箱故障诊断与预警中的应用

风电机组状态监测数据具有量大、多源、异构、复杂、增长迅速的电力大数据特点。现有的故障诊断与预警方法在处理大数据时难以在保证精度情况下进行快速处理,故提出了结合Storm实时流数据处理和Spark内存批处理技术的风电机组在线故障诊断与预警模型。以齿轮箱故障诊断与预警为例阐释该模型,引入了Storm处理状态监测数据流,设计了流数据处理的Topology结构;引入Spark,利用弹性分布式数据集(RDD)编程模型实现了朴素贝叶斯(NB)算法和反向传播(BP)算法对设备状态信息进行故障诊断与预测。实验结果显示,该故障诊断与预测方法在保证精度的前提下具有较好的加速比,也证明了该故障诊断与预警模型的有效性和可行性。

Application of Big Data Processing Technology in Fault Diagnosis and Early Warning of Wind Turbine Gearbox

The condition monitoring data of the wind turbogenerator has the characteristics of large quantity, multiple sources, heterogeneity, and complex and rapid growth. Existing fault diagnosis and early warning methods are hardly able to deal with such issues quickly while ensuring accuracy under the big data. A model of wind turbines on-line fault diagnosis and early warning is put forward by referring to real-time streaming data processing technology Storm and memory batch processing technology Spark. And the gearbox fault diagnosis and early warning are taken as an example to explain the model. Firstly Storm is introduced to deal with the state monitoring data flow and the topology structure of flow data processing is designed as well. Secondly a resilient distributed dataset(RDD)programming model is employed to realize a naive Bayes algorithm and back propagation(BP)algorithm, which are used for fault diagnosis and prediction based on device status information. Finally the experimental results show the satisfactory speed-up ratio with ensured precision and the correctness and validity of the fault diagnosis and early warning model. This work is supported by National Natural Science Foundation of China(No. 61300040)and Scientific Research Project of Hebei Province(No. Z2012077).