A DOMAIN INDEPENDENT DATA MINING METHODOLOGY FOR PROGNOSTICS

机译：预测学领域独立的数据挖掘方法

获取原文

获取原文并翻译 | 示例

页面导航

摘要
著录项
相似文献
相关主题

摘要

Modern operation of complex systems such as trains and aircraft generates vast amounts of data. This data can be used to help predict component failures which may lead to considerable savings, reduce the number of delays, increase the overall throughput of the organization, and augment safety. Many data mining algorithms, such as neural networks, decision trees, and support vector machines, exist to learn models from vast amounts of data but their application to real world operational data from systems such as aircraft and trains is very challenging. For successful prognostics, several difficulties need to be carefully addressed including data selection, data fusion, data labeling, model integration, and model evaluation. This paper explains these issues and presents a methodology that we have developed to address them in a systematic manner. The paper discusses the application of the methodology to the rail and aerospace industries and highlights open problems.

机译：诸如火车和飞机之类的复杂系统的现代运行会产生大量数据。此数据可用于帮助预测组件故障，这可能导致可观的节省，减少延迟次数，增加组织的整体吞吐量并增强安全性。存在许多数据挖掘算法，例如神经网络，决策树和支持向量机，可以从大量数据中学习模型，但是将其应用于飞机和火车等系统的现实操作数据却极具挑战性。对于成功的预测，需要仔细解决一些困难，包括数据选择，数据融合，数据标记，模型集成和模型评估。本文解释了这些问题，并提出了我们开发的一种系统地解决这些问题的方法。本文讨论了该方法在铁路和航空航天工业中的应用，并强调了未解决的问题。

著录项

来源
《Society for Machinery Failure Prevention Technology Meeting; 20050418-21; Virginia Beach,VA(US)》|2005年|P.347-359|共13页
会议地点 Virginia BeachVA(US)
作者
Sylvain Letourneau; Chunsheng Yang; Chris Drummond; Elizabeth Scarlett; Julio Valdes; Marvin Zaluski;
展开▼
作者单位

Institute for Information Technology National Research Council of Canada, Ottawa;

展开▼
会议组织
原文格式 PDF
正文语种 eng
中图分类 TH171;
关键词
data mining; aircraft health monitoring; train wheel monitoring; component failure prediction;

机译：数据挖掘;飞机健康监测;轮毂监测;部件故障预测;

相似文献

外文文献
中文文献
专利

1. A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data [J] . Trilles Sergio, Belmonte Oscar, Schade Sven, International journal of digital Earth . 2017,第1a3期

机译：一个独立于域的方法，可以实时分析物联网数据流。从环境数据中检测异常检测的概念实施证明
2. Applications domain driven data mining methodology in bioinformatics [J] . Yadan Li, Qinghua Bai, Zhicheng Chen BioTechnology: An Indian Journal . 2014,第9期

机译：生物信息学中应用领域驱动的数据挖掘方法
3. Domain-Driven data mining: A practical methodology [J] . Longhing Cao, Chengqi Zhang International Journal of Data Warehousing and Mining . 2006,第4期

机译：域驱动的数据挖掘：一种实用的方法
4. A DOMAIN INDEPENDENT DATA MINING METHODOLOGY FOR PROGNOSTICS [C] . Sylvain Letourneau, Chunsheng Yang, Chris Drummond, Society for Machinery Failure Prevention Technology Meeting . 2005

机译：预后的域独立数据挖掘方法
5. Transformation of relational database domain into graph-based domain for graph-based data mining. [D] . Palod, Swapnil. 2004

机译：将关系数据库域转换为基于图的域以进行基于图的数据挖掘。
6. Detection of Independent Associations of Plasma Lipidomic Parameters with Insulin Sensitivity Indices Using Data Mining Methodology [O] . Steffi Kopprasch, Srirangan Dheban, Kai Schuhmann, -1

机译：使用数据挖掘方法检测血浆脂质参数与胰岛素敏感性指标的独立关联
7. A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data [O] . TRILLES Sergi, BELMONTE Oscar, SCHADE SVEN, 2016

机译：一种与域无关的方法，可实时分析物联网数据流。从环境数据中异常检测的概念验证实施

A DOMAIN INDEPENDENT DATA MINING METHODOLOGY FOR PROGNOSTICS

摘要

著录项

相似文献

相关主题

期刊订阅