Data stream forecasting for system fault prediction

支持向量机 数据挖掘 计算机科学 数据流 故障检测与隔离 数据流挖掘 断层(地质) 人工智能 电信 地质学 地震学 执行机构
作者
Ahmad Alzghoul,Magnus Löfstrand,Björn Backe
出处
期刊:Computers & Industrial Engineering [Elsevier BV]
卷期号:62 (4): 972-978 被引量:35
标识
DOI:10.1016/j.cie.2011.12.023
摘要

Competition among today's industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing. In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM). The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
能量球完成签到,获得积分10
1秒前
1秒前
2秒前
3秒前
Anyixx完成签到 ,获得积分10
4秒前
清爽尔安发布了新的文献求助10
6秒前
7秒前
丫丫发布了新的文献求助10
8秒前
huangyikun发布了新的文献求助10
8秒前
叔铭完成签到,获得积分10
9秒前
大个应助ZONG采纳,获得10
11秒前
11秒前
Ma完成签到,获得积分10
12秒前
孙燕应助猪猪hero采纳,获得10
12秒前
会发光的小灰灰完成签到,获得积分10
12秒前
板凳儿cc发布了新的文献求助10
12秒前
黑色天使发布了新的文献求助10
13秒前
13秒前
激情的代曼完成签到,获得积分10
13秒前
14秒前
17秒前
缓慢手机完成签到,获得积分10
17秒前
丫丫完成签到,获得积分10
17秒前
18秒前
时尚俊驰发布了新的文献求助10
18秒前
耍酷的冷雪完成签到,获得积分10
19秒前
wanci应助baonali采纳,获得10
21秒前
ZONG发布了新的文献求助10
22秒前
wuy发布了新的文献求助10
22秒前
123完成签到,获得积分10
23秒前
24秒前
saisyo发布了新的文献求助10
25秒前
隐形曼青应助炸胡娃娃采纳,获得30
26秒前
坦率白萱应助wwl采纳,获得10
26秒前
NexusExplorer应助小晓采纳,获得10
26秒前
27秒前
27秒前
123发布了新的文献求助10
28秒前
搞怪的紫易完成签到,获得积分10
28秒前
WYQ完成签到,获得积分10
28秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989444
求助须知:如何正确求助?哪些是违规求助? 3531531
关于积分的说明 11254250
捐赠科研通 3270191
什么是DOI,文献DOI怎么找? 1804901
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809174