基于分类算法的汽动给水泵组故障预测  

Fault prediction for turbine driven boiler feed water pump set based on classification algorithm

作　　者：徐红伟[1] 刘振宇[1] 李崇晟[1] XU Hongwei;LIU Zhenyu;LI Chongsheng(Xi'an Thermal Power Research Institute Co., Ltd., Xi'an 710054, China)

机构地区：[1]西安热工研究院有限公司,陕西西安710054

出　　处：《热力发电》2019年第4期128-134,共7页Thermal Power Generation

基　　金：国家重点研发计划项目(2017YFF0210500)~~

摘　　要：汽动给水泵组是火电厂热力系统的重要辅助设备,对汽动给水泵组有效的故障预测有助于其状态检修。本文通过基于统计特征的特征提取方法及Relief特征选择算法,实现厂级监控信息系统历史数据到分类模型输入参数的合理转化,并采用5种分类算法分别针对2个电厂汽动给水泵组的小机叶片断裂和给水泵动、静平衡盘碰磨实际故障案例,建立了正常与故障状态的分类模型。实际数据验证表明:BP神经网络、支持向量机和组合分类算法分类效果更优,可提前4～10周识别设备故障的潜在风险,该结果为其他设备故障预测提供了新的思路。The turbine driven boiler feed water pump set is an important auxiliary part of the thermodynamic cycle system in thermal power plants. Effective fault prediction for the turbine driven boiler feed water pump set is helpful to its condition-based maintenance. In this paper, by a feature extraction method based on statistics and a feature selection algorithm named Relief, the history data in plant-level supervision information system (SIS) is converted to the input parameters of classification models rationally. Moreover, combing with the actual fault cases in two power plants, the blades fracture of small turbine in turbine driven feedwater pump set and dynamic/static balance disk rubbing in feedwater pump, five classification algorithms are applied to establish the classification models for distinguishing normal state and fault state. The actual data verification shows that, the BP neural network, support vector machine (SVM) and combined classification algorithm have better performance than other methods, which can identify the potential risks of faults 4~10 weeks in advance. The result provides new idea for other equipment failure prediction.

关 键 词：汽动给水泵组 故障预测 BP神经网络 支持向量机 组合分类 状态检修 

分 类 号：TH163.3[机械工程—机械制造及自动化] TM621.7[电气工程—电力系统及自动化]