电力系统自动化2016，Vol.40Issue(14)：129-134,6.DOI:10.7500/AEPS20160316013
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
Application of Big Data Processing Technology in Fault Diagnosis and Early Warning of Wind Turbine Gearbox
摘要
Abstract
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 .关键词
风电机组/故障诊断/故障预警/弹性分布式数据集/内存批处理/流数据处理Key words
wind turbine/fault diagnosis/failure warning/resilient distributed dataset ( RDD)/memory batch processing/streaming data processing引用本文复制引用
张少敏,毛冬,王保义..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[J].电力系统自动化,2016,40(14):129-134,6.基金项目
国家自然科学基金资助项目（61300040）；河北省高等学校科学研究计划资助项目（Z2012077）。This work is supported by National Natural Science Foundation of China （ No ．61300040） and Scientific Research Project of Hebei Province （ No ． Z2012077）． （）
