Big Data Validity Evaluation Based on MMTD

Mathematical Problems in Engineering ◽

10.1155/2018/8058670 ◽

2018 ◽

Vol 2018 ◽

pp. 1-6

Author(s):

Ningning Zhou ◽

Guofang Huang ◽

Suyang Zhong

Keyword(s):

Big Data ◽

Data Quality ◽

Evaluation Method ◽

Major Influence ◽

Multiple Dimensions ◽

Data Validity ◽

Data Environment ◽

Evaluation Of Data ◽

Truth Degree ◽

Validity Evaluation

Big data has been studied extensively in recent years. With the increase in data size, data quality becomes a priority. Evaluation of data quality is important for data management, which influences data analysis and decision making. Data validity is an important aspect of data quality evaluation. Based on 3V properties of big data, dimensions that have a major influence on data validity in a big data environment are analyzed. Each data validity dimension is analyzed qualitatively using medium logic. The measuring of medium truth degree is used to propose models to measure single and multiple dimensions of big data validity. The validity evaluation method based on medium logic is more reasonable and scientific than general methods.

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Intelligent Evaluation Method for Complex Systems in The Big Data Environment

2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON) ◽

10.1109/ukrcon.2019.8880024 ◽

2019 ◽

Author(s):

Sergii Kavun ◽

Alina Zamula ◽

Valerii Miziurin

Keyword(s):

Big Data ◽

Complex Systems ◽

Evaluation Method ◽

Data Environment

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A Stochastic Asset Life Prediction Method for Large Fleet Datasets in Big Data Environment

Volume 14: Emerging Technologies; Safety Engineering and Risk Analysis; Materials: Genetics to Structures ◽

10.1115/imece2015-52458 ◽

2015 ◽

Cited By ~ 1

Author(s):

Behrad Bagheri ◽

David Siegel ◽

Wenyu Zhao ◽

Jay Lee

Keyword(s):

Monte Carlo ◽

Big Data ◽

Evaluation Method ◽

Health Management ◽

Prediction Method ◽

Remaining Useful Life ◽

Data Driven ◽

Engineering System ◽

Useful Life ◽

Data Environment

Preventing catastrophic failures is the most important task of prognostics and health management approaches in industry where Remaining Useful Life (RUL) prediction plays a significant role to schedule required preventive actions. Regarding recent advances and trends in data analysis and in Big Data environment, industries with such foreseeing approach are able to maintain their fleet of assets more efficiently with higher assurance. To address this requirement, several physics-based and data-driven methods have been developed to predict the remaining useful life of various engineering systems. In current paper, we present a simple, yet accurate stochastic method for data-driven RUL prediction of complex engineering system. The approach is constructed based on selecting the most significant parameters from raw data by using the improved distance evaluation method as feature selection algorithms. Subsequently, the health value of units is assessed by logistic regression and the assessment output is used in a Monte Carlo simulation to estimate the remaining useful life of the desired system. During Monte Carlo iterations, several features are extracted to help filtering less accurate estimations and improve the overall prediction accuracy. The proposed algorithm is validated in two ways. First of all, the accuracy of RUL prediction is measured by applying the method to 2008 PHM data challenge gas-turbine dataset. Subsequently, gradual changes in RUL prediction of a particular test unit are measured to verify the behavior of the algorithm upon availability of additional historical data.

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Validity Evaluation Method Based on Data Driving for On-Line Monitoring Data of Transformer under DC-Bias

Sensors ◽

10.3390/s20154321 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4321

Author(s):

Yuanda He ◽

Qi Zhou ◽

Sheng Lin ◽

Liping Zhao

Keyword(s):

Evaluation Method ◽

Comprehensive Evaluation ◽

External Noise ◽

Monitoring Data ◽

Monitoring Device ◽

Noise Interference ◽

On Line ◽

Data Validity ◽

Dc Bias ◽

Validity Evaluation

The DC-bias monitoring device of a transformer is easily affected by external noise interference, equipment aging, and communication failure, which makes it difficult to guarantee the validity of monitoring data and causes great problems for future data analysis. For this reason, this paper proposes a validity evaluation method based on data driving for the on-line monitoring data of a transformer under DC-bias. First, the variation rule and threshold range of monitoring data for neutral point DC, vibration, and noise of the transformer under different working conditions are obtained through statistical analysis. Then, the data validity criterion of DC bias monitoring data is proposed to achieve a comprehensive evaluation of data validity based on data threshold, continuity, impact, and correlation. In addition, case studies are carried out on the real measured data of the DC bias magnetic monitoring system of a regional power grid by using this evaluation method. The results show that the proposed method can systematically and comprehensively evaluate the validity of the DC bias monitoring data and can judge whether the monitoring device fails to a certain extent.

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Construction of Multimedia Teaching Resource Management Evaluation Model in the Big Data Environment

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v13i03.8365 ◽

2018 ◽

Vol 13 (03) ◽

pp. 4

Author(s):

Wei Bian

Keyword(s):

Big Data ◽

Resource Management ◽

Evaluation Method ◽

Evaluation Model ◽

Management Evaluation ◽

Teaching Resource ◽

Multimedia Teaching ◽

Proposed Model ◽

Multi Media ◽

Data Environment

In view of the deficiencies in the studies of the multi-media teaching resource management method and resource management evaluation model, a multi-media teaching resource management evaluation model in the big data environment is proposed in this paper based on the existing work. The teaching resource management evaluation method of the proposed model is further studied based on the elaboration of the component elements of the model. Guided by the multimedia teaching resource management evaluation model, the construction method for the multimedia teaching resource management evaluation model is studied in the open big data environment. And the multimedia remote teaching data of 360 encyclopedia and news web pages are used as the basis for experimental verification. The results show that the proposed model and method can effectively evaluate the multimedia teaching resource management.

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