A multivariate statistical analysis technique for on-line fault prediction

Youree, Roger K.; Yalowitz, Jeffrey S.; Corder, Aaron; Ooi, Teng K.

doi:10.1109/phm.2008.4711447

Cited by 13 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mr. Alzghoul et al used three classification algorithms to investigate the performance and products availability of each algorithm [9]. In addition to this paper, other researches also used data stream mining for machine monitoring and reliability analysis [10], online failure prediction [11] and tool condition monitoring [12].…”

Section: Related Workmentioning

confidence: 99%

A learning-based system for predicting sport injuries

Liu

Sun²,

Bai

et al. 2018

MATEC Web Conf.

View full text Add to dashboard Cite

In the big data era, learning-based techniques have attracted more and more attentions in many industry areas. The sport injury prediction is one of the most critical issues in data analysis of soccer teams.However, learning-based methods have not been widely used due to the poor data quality and computational capacity. In this paper, we propose a learning-based model to forecast sport injuries according to the data from various information systems. We first reduce the attributes that have significant impact on the injury risk by using learning-based methods.Then, we provide an algorithm based on the random forest method to prevent the over-fitting problem. We have evaluated the proposed model with the real-world data. The experimental results show that our model works efficiently and achieves low error rates.

show abstract

Section: Related Workmentioning

confidence: 99%

A learning-based system for predicting sport injuries

Liu

Sun²,

Bai

et al. 2018

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…Data analysis is the method of analyzing data to infer about their nature and fidelity [27]. When analyzing very large amounts of data, statistical analysis can be used to analyze a smaller amount of data.…”

Section: Sample Applications Of Random Number Generatorsmentioning

confidence: 99%

Random Number Generator

2005

Dictionary of Statistics &Amp; Methodology

View full text Add to dashboard Cite

“…It addresses the velocity dimension of big data. Several researchers have naturally discussed the issue of data streams in FDD, including [11,[17][18][19][20][21][22][23][24][25][26] (a more detailed review of related work is presented in Section 4).…”

Section: Introductionmentioning

confidence: 99%

Monitoring Big Data Streams Using Data Stream Management Systems: Industrial Needs, Challenges, and Improvements

Alzghoul

2023

Advances in Operations Research

View full text Add to dashboard Cite

Real-time monitoring systems are important for industry since they allow for avoiding unplanned system stops and keeping system availability high. The technical requirements for such systems include being both scalable and online, as the amount of generated data is increasing with time. Therefore, monitoring systems must integrate tools that can manage and analyze the data streams. The data stream management system is a stream processing tool that has the ability to manage and support operations on data streams in real-time. Several researchers have proposed and tested real-time monitoring systems which have the ability to search big data streams. In this paper, the research works that discuss the analysis of online data streams for fault detection in industry are reviewed. Based on the literature analysis, the industrial needs and challenges of monitoring big data streams are presented. Furthermore, feasible suggestions for improving the real-time monitoring system are proposed.

show abstract

A multivariate statistical analysis technique for on-line fault prediction

Cited by 13 publications

References 5 publications

A learning-based system for predicting sport injuries

A learning-based system for predicting sport injuries

Random Number Generator

Monitoring Big Data Streams Using Data Stream Management Systems: Industrial Needs, Challenges, and Improvements

Contact Info

Product

Resources

About