摘要
针对复杂工况下水泵机电运行参数趋势预测的问题,建立基于多任务学习(multi-task learning,MTL)和注意力机制(attention mechanism,AM)的水泵机电运行参数趋势预测模型。充分利用历史工况数据,使用多任务学习分析方法,寻找历史工况数据的共同特征;在预测新工况数据变化趋势时,引入注意力机制动态分配共同特征映射时的权重系数,突出关键共同特征,提升模型的预测精度;根据模型监测统计量阈限分析,建立机组运行监测多级预警模型,优化运维管理策略。以某泵站机组实际运行工况数据进行测试并与不同模型计算结果进行对比分析,结果表明:与传统单任务学习和静态共同特征映射权重的模型相比,基于多任务学习和注意力机制的模型,其统计量T 2和Q均未超过95%和99%的控制限,表明该预测模型具有很好的稳定性和准确性。
The safe and stable operation of the pumping station system is of great significance for ensuring supply for domestic water agricultural irrigation,and industrial water.Therefore,real-time monitoring of pump station operating parameters and establishing predictive models for fault diagnosis and intelligent alarm of unit equipment have significant application value.The data-driven method for fault diagnosis is currently a hot topic in the research of pump station equipment status monitoring.However,there are problems such as insufficient data samples,difficulty in feature extraction,and insufficient generalization ability in practical application.Addressing the challenge of predicting the trends in operating parameters of water pump units under complex working conditions,a prediction model for operating parameters of water pump units was proposed based on multitask learning method and attention mechanism.Firstly,the historical working condition data was fully utilized,and a multi-task learning model was established to find the common characteristics of the historical working condition data on the basis of traditional principal component analysis methods.Secondly,an attention mechanism was introduced to dynamically allocate weight coefficients for common feature mapping when predicting the trend of parameter changes under new operating conditions,highlighting key common features and improving the accuracy of the prediction.Based on the actual operating data of a pumping station hub unit,the performance of the model was tested.By monitoring the statistical parameters T~2and Q,which reflecting the stability and accurately of the model,results showed that the prediction model proposed has good stability and prediction accuracy under 98%and 95%control thresholds.On this basis,a multi-level equipment operation monitoring and alarm model was also preliminarily established.The alarm level is divided into three levels:yellow,orange,and red.Management personnel can take different disposal measures based on the alarm level,s
作者
邵知宇
薛美玲
何聪
李精伟
唐鸿儒
SHAO Zhiyu;XUE Meiling;HE Cong;LI Jingwei;TANG Hongru(School of Electrical and Energy Power Engineering,Yangzhou University,Yangzhou 225009,China;Jiangsu Jiangdu Water Conservancy Project Management Office,Yangzhou 225009,China;Nanjing Institute ofelectronic equipment,Nanjing,210007,China)
出处
《南水北调与水利科技（中英文）》
CAS
CSCD
北大核心
2024年第5期959-966,977,共9页
South-to-North Water Transfers and Water Science & Technology
基金
国家自然基金青年基金项目(62103358)
江苏省高效节能大型轴流泵站工程研究中心开放课题资助项目(ECHEAP017)。
关键词
趋势预测
水泵机组
多任务学习
注意力机制
状态监测
多级预警
trend prediction
water pump unit
multi-task learning
attention mechanism
condition monitoring
multilevel early warning
