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

上传者: DeepLearning_ | 上传时间: 2022-12-13 13:25:58 | 文件大小: 2.71MB | 文件类型: 7Z
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

文件下载

资源详情

[{"title":"( 49 个子文件 2.71MB ) 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","children":[{"title":"K-means.py <span style='color:#111;'> 2.48KB </span>","children":null,"spread":false},{"title":"AR.py <span style='color:#111;'> 4.11KB </span>","children":null,"spread":false},{"title":"Rules.txt <span style='color:#111;'> 3.43MB </span>","children":null,"spread":false},{"title":"XGboost.py <span style='color:#111;'> 3.72KB </span>","children":null,"spread":false},{"title":"figure_result.py <span style='color:#111;'> 998B </span>","children":null,"spread":false},{"title":"svm.py <span style='color:#111;'> 2.03KB </span>","children":null,"spread":false},{"title":"image","children":[{"title":"基于多元高斯分布的运行状态趋势分析.png <span style='color:#111;'> 53.30KB </span>","children":null,"spread":false},{"title":"基于K-均值聚类的运行状态趋势分析.png <span style='color:#111;'> 50.78KB </span>","children":null,"spread":false},{"title":"基于关联规则的运行状态趋势分析.png <span style='color:#111;'> 68.35KB </span>","children":null,"spread":false},{"title":"多元高斯分布","children":[{"title":"多元高斯13B一次风机335000-345000.jpg <span style='color:#111;'> 31.84KB </span>","children":null,"spread":false},{"title":"13B一次风机270000-310000运行状态.jpg <span style='color:#111;'> 129.08KB </span>","children":null,"spread":false},{"title":"多元高斯13B一次风机40000-60000.jpg <span style='color:#111;'> 38.34KB </span>","children":null,"spread":false},{"title":"14A一次风机390000-410000.png <span style='color:#111;'> 23.07KB </span>","children":null,"spread":false},{"title":"13B一次风机40000-60000运行状态.jpeg <span style='color:#111;'> 135.19KB </span>","children":null,"spread":false},{"title":"多元高斯13B一次风机270000-310000.jpg <span style='color:#111;'> 46.37KB </span>","children":null,"spread":false}],"spread":true},{"title":"关联规则","children":[{"title":"13A_45000_60000运行状态.png <span style='color:#111;'> 81.94KB </span>","children":null,"spread":false},{"title":"13A_45000_60000.png <span style='color:#111;'> 17.23KB </span>","children":null,"spread":false}],"spread":true},{"title":"基于随机森林的运行状态趋势分析.png <span style='color:#111;'> 65.53KB </span>","children":null,"spread":false},{"title":"谏壁电厂一次风机运行趋势","children":[{"title":"14A17年1月-18年11月运行状态.jpg <span style='color:#111;'> 121.40KB </span>","children":null,"spread":false},{"title":"14A一次风机2017年运行状态.png <span style='color:#111;'> 102.65KB </span>","children":null,"spread":false},{"title":"14B17-1-12运行状态.jpg <span style='color:#111;'> 127.02KB </span>","children":null,"spread":false},{"title":"13A17-1-12运行状态.jpg <span style='color:#111;'> 128.49KB </span>","children":null,"spread":false},{"title":"13B17-1-12运行状态.jpg <span style='color:#111;'> 127.67KB </span>","children":null,"spread":false},{"title":"14B汽引风机2018年运行状态.png <span style='color:#111;'> 79.29KB </span>","children":null,"spread":false},{"title":"13A汽引风机2017-1-12月份运行趋势.png <span style='color:#111;'> 90.67KB </span>","children":null,"spread":false},{"title":"14A汽引风机2017年运行趋势.png <span style='color:#111;'> 96.51KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"14A17Pre.xlsx <span style='color:#111;'> 398.43KB </span>","children":null,"spread":false},{"title":"oneclass.py <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false},{"title":"vectors.xls <span style='color:#111;'> 5.50KB </span>","children":null,"spread":false},{"title":"figure.py <span style='color:#111;'> 3.61KB </span>","children":null,"spread":false},{"title":"result.py <span style='color:#111;'> 4.85KB </span>","children":null,"spread":false},{"title":"figureqiyin.py <span style='color:#111;'> 1.28KB </span>","children":null,"spread":false},{"title":"figure_hist.py <span style='color:#111;'> 1.44KB </span>","children":null,"spread":false},{"title":"figure_xiangguanguanxi.py <span style='color:#111;'> 945B </span>","children":null,"spread":false},{"title":"RF.py <span style='color:#111;'> 1.54KB </span>","children":null,"spread":false},{"title":"clusting.xlsx <span style='color:#111;'> 673.64KB </span>","children":null,"spread":false},{"title":"figuredatatime.py <span style='color:#111;'> 1.66KB </span>","children":null,"spread":false},{"title":"multivariate.py <span style='color:#111;'> 2.51KB </span>","children":null,"spread":false},{"title":"figure_yuzhi.py <span style='color:#111;'> 2.99KB </span>","children":null,"spread":false},{"title":"BPNet.py <span style='color:#111;'> 2.71KB </span>","children":null,"spread":false},{"title":"项目说明.md <span style='color:#111;'> 955B </span>","children":null,"spread":false},{"title":"Clean.py <span style='color:#111;'> 1.89KB </span>","children":null,"spread":false},{"title":"clustering.py <span style='color:#111;'> 5.04KB </span>","children":null,"spread":false},{"title":"dataM.py <span style='color:#111;'> 1.55KB </span>","children":null,"spread":false},{"title":"Skearn_k_means.py <span style='color:#111;'> 724B </span>","children":null,"spread":false},{"title":"li_san.xlsx <span style='color:#111;'> 1.18MB </span>","children":null,"spread":false},{"title":"MulPre.py <span style='color:#111;'> 2.70KB </span>","children":null,"spread":false},{"title":"Rules.xls <span style='color:#111;'> 291.50KB </span>","children":null,"spread":false},{"title":"process.py <span style='color:#111;'> 2.78KB </span>","children":null,"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明