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Modeling of High-Dimensional Industrial Data for Enhanced PHM using Time Series based Integrated Fusion and Filtering Techniques.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling of High-Dimensional Industrial Data for Enhanced PHM using Time Series based Integrated Fusion and Filtering Techniques./
作者:	Cai, Haoshu.
面頁冊數:	1 online resource (116 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
標題:	Mechanical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29282227click for full text (PQDT)
ISBN:	9798802752180

Modeling of High-Dimensional Industrial Data for Enhanced PHM using Time Series based Integrated Fusion and Filtering Techniques.
Cai, Haoshu.

Modeling of High-Dimensional Industrial Data for Enhanced PHM using Time Series based Integrated Fusion and Filtering Techniques. - 1 online resource (116 pages)

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--University of Cincinnati, 2022.

Includes bibliographical references

Prognostics and Health Management (PHM) has extended its frontiers to more pervasive applications for failure detection, process monitoring, and predictive maintenance in the increasingly complicated manufacturing environment. Meanwhile, as Internet of Things (IoT) technologies are developed rapidly, the research for PHM is facing non-negligible challenges in several aspects. The advancement in the volume, velocity, and variety of the manufacturing data demands improved analytics of PHM solutions. The mass of the manufacturing data demands more efficient selection strategy to exclude the incorrect and useless information. Also, in the industrial environment, the high-dimensional data is usually collected from various sensor recordings with changes and drifts, which constitute the fundamental properties of the stream data. The advanced PHM techniques are required to be capable to capture and track the coming information within the high-dimensional data continuously and adaptively. To deal with the challenges and research gaps, this research proposes a scalable methodology for discrete time series prediction based on industrial high-dimensional data. First, a reference-based fusion strategy is proposed and employed to combine the valuable knowledge from the historical data, to reduce data dimensionality and to exclude the information which is not helpful for further analysis. Second, a state modeling strategy is designed to fuse both the reference data selected by the previous strategy and the past time series data. Also, it formulates an efficient and accurate function to depict the relationship between the predictor and the target. Finally, a Bayesian filter is designed to deal with the strong non-linearity, to propagate in high-dimensional space and to learn the new knowledge continuously in the stream data without losing the properties of the historical data. Finally, three cases from different industrial environments are implemented to justify the feasibility, effectiveness and superiority of the proposed methodology. The proposed methods are evaluated by comparing with benchmarking models selected from the recent literature. The experiment results indicate that the proposed methods hold several advantages, including the improved and enhanced prediction accuracy, statistically credible prediction uncertainty and convenient implementation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798802752180Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Prognostics and Health ManagementIndex Terms--Genre/Form:

542853
Electronic books.

Modeling of High-Dimensional Industrial Data for Enhanced PHM using Time Series based Integrated Fusion and Filtering Techniques.
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