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基于机器学习实现发电厂辅机故障预警系统源码+项目说明.7z
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基于机器学习实现发电厂辅机故障预警系统源码+项目说明.7z 针对电厂辅机故障率高,传统的基于机理的模型预警不及时,经常误诊的问题,设计了基于机器学习的新型故障预警模型 面对三种不同的使用场景,分别设计了基于聚类和关联规则的预警模型、基于随机森林的预警模型、与基于多元高斯分布和人 工神经网络的预警模型 使用某电厂一次风机的实际数据进行验证,所设计的三种预警模型能够提前约60min发出预警,给电厂运行人员提供指导 除了以上的算法,还使用一分类、支持向量机、XGBoost算法等对数据进行了处理 【特别强调】 1、csdn上资源保证是完整最新,会不定期更新优化; 2、请用自己的账号在csdn官网下载,若通过第三方代下,博主不对您下载的资源作任何保证,且不提供任何形式的技术支持和答疑!!!
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基于机器学习实现发电厂辅机故障预警系统源码+项目说明.7z (49个子文件)
K-means.py 2KB
AR.py 4KB
Rules.txt 3.43MB
XGboost.py 4KB
figure_result.py 998B
svm.py 2KB
image
基于多元高斯分布的运行状态趋势分析.png 53KB
基于K-均值聚类的运行状态趋势分析.png 51KB
基于关联规则的运行状态趋势分析.png 68KB
多元高斯分布
多元高斯13B一次风机335000-345000.jpg 32KB
13B一次风机270000-310000运行状态.jpg 129KB
多元高斯13B一次风机40000-60000.jpg 38KB
14A一次风机390000-410000.png 23KB
13B一次风机40000-60000运行状态.jpeg 135KB
多元高斯13B一次风机270000-310000.jpg 46KB
关联规则
13A_45000_60000运行状态.png 82KB
13A_45000_60000.png 17KB
基于随机森林的运行状态趋势分析.png 66KB
谏壁电厂一次风机运行趋势
14A17年1月-18年11月运行状态.jpg 121KB
14A一次风机2017年运行状态.png 103KB
14B17-1-12运行状态.jpg 127KB
13A17-1-12运行状态.jpg 128KB
13B17-1-12运行状态.jpg 128KB
14B汽引风机2018年运行状态.png 79KB
13A汽引风机2017-1-12月份运行趋势.png 91KB
14A汽引风机2017年运行趋势.png 97KB
14A17Pre.xlsx 398KB
oneclass.py 1KB
vectors.xls 6KB
figure.py 4KB
result.py 5KB
figureqiyin.py 1KB
figure_hist.py 1KB
figure_xiangguanguanxi.py 945B
RF.py 2KB
clusting.xlsx 674KB
figuredatatime.py 2KB
multivariate.py 3KB
figure_yuzhi.py 3KB
BPNet.py 3KB
项目说明.md 955B
Clean.py 2KB
clustering.py 5KB
dataM.py 2KB
Skearn_k_means.py 724B
li_san.xlsx 1.18MB
MulPre.py 3KB
Rules.xls 292KB
process.py 3KB
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