Hydraulic metal structure health diagnosis based on data mining technology

Yang, Guang-Hong; Xiao, Feng; Yang, Kun

doi:10.1016/j.wse.2015.04.010

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Because DM technology has considerable advantages in the processing of large amounts of data, many industries, especially scientific research, finance, and education, have been utilizing DM technology (Yang, Feng, & Yang, 2015). DM has gained considerable attention, thus increasing popularity in the economic world (Koh & Low, 2004).…”

Section: Practical Applications Of Data Miningmentioning

confidence: 99%

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

Kasemsap

2016

International Journal of Social and Organizational Dynamics in IT

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This article reviews the literature in the search for the multifaceted applications of data mining (DM), business intelligence (BI), and knowledge management (KM). The literature review highlights the overviews of DM, BI, and KM; the practical applications of DM, BI, and KM; and the prospects of DM, BI, and KM in terms of marketing, business, human resources, and manufacturing. DM plays a key role in organizing huge amount of data and condensing it into valuable information. BI involves the delivery and integration of relevant and useful business information in an organization. KM allows companies to manage a system of core competencies in order to maximize business opportunities and minimize the risk of losing business opportunities. The findings present valuable insights and further understanding of the way in which DM, BI, and KM efforts should be focused.

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Section: Practical Applications Of Data Miningmentioning

confidence: 99%

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

Kasemsap

2016

International Journal of Social and Organizational Dynamics in IT

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“…Usage of Weibull distribution in machine reliability analysis was done by [15,16]. Hydraulic systems was the main interest of research for authors of [17] in which they compared knowledge and data driven methods for failure detection, optimization and maintenance on hydrosystems was described in [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of the Hydro-System of Excavator SchRs 800 Using Weibull Distribution

Dašić¹,

Živković²,

Karić³

2015

AMM

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In this paper is given the use Weibull distribution (WD) as theoretical reliability model for analysis of the hydro-system of excavator SchRs 800, which is accepted on the basis of Pearson (χ2), Kolmogorov-Smirnov (KS) and Cramér-von Mises (CvM) goodness-of-fit tests. The time of work without failure of the hydro-system of excavator SchRs 800 for accepted Weibull model of reliability for probability of 50 % is T50%=0.3417⋅103[h], for probability of 80 % is T80%=0.1884⋅103[h] and for probability of 90% is T90%=0.127⋅103[h].

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Digital twin-driven intelligent operation and maintenance platform for large-scale hydro-steel structures

Li,

Zhang,

Zheng

et al. 2024

Advanced Engineering Informatics

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Hydraulic metal structure health diagnosis based on data mining technology

Cited by 8 publications

References 11 publications

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

Reliability Analysis of the Hydro-System of Excavator SchRs 800 Using Weibull Distribution

Digital twin-driven intelligent operation and maintenance platform for large-scale hydro-steel structures

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