基于selffer net构建的人工智能模型在风机叶根螺栓失效预测场景下的应用
Application of Artificial Intelligence Model Based on Self Net in the Failure Prediction Scenario of Fan Root Bolt
姜洋 1张来祥 1徐斌 1朴云涛 2柳会哲3
作者信息
- 1. 黑龙江省华富电力投资有限公司,黑龙江哈尔滨 150000
- 2. 黑龙江省华富电力投资有限公司依兰分公司,黑龙江依兰 154800
- 3. 华电新能源集团黑龙江分公司,黑龙江哈尔滨 150000
- 折叠
摘要
针对风机叶根螺栓失效问题,文章提出了一种基于selffer net构建的叠加树人工智能模型.该模型集成了回溯训练层,叠加树模型,遮蔽特征模型,以及LightGBM模型,实现了对风机叶根螺栓失效概率的预测.经过实验验证,该模型在预测风机叶根螺栓失效概率方面具有较高的准确性和稳定性,为风机叶根螺栓失效预测场景提供了有效的解决方案.
Abstract
In response to the failure problem of fan blade root bolts,this paper proposes a stacked tree artificial intelligence model based on self buffer net.This model integrates backtracking training layer,overlay tree model,Masked feature model,and LightGBM model to predict the failure probability of fan blade root bolts.After experimental verification,the model has high accuracy and stability in predicting the failure probability of fan blade root bolts,providing an effective solution for predicting the failure scenario of fan blade root bolts.
关键词
风机叶根螺栓失效预测/selffer/net/遮蔽特征模型/LightGBMKey words
failure prediction of fan blade root bolts/self net/masked feature model/LightGBM引用本文复制引用
出版年
2024
NETL
NSTL
万方数据