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Journal of Intelligent Manufacturing
Erschienen in: Journal of Intelligent Manufacturing 8/2023

15.09.2022

A systematic review of data-driven approaches to fault diagnosis and early warning

verfasst von: Peng Jieyang, Andreas Kimmig, Wang Dongkun, Zhibin Niu, Fan Zhi, Wang Jiahai, Xiufeng Liu, Jivka Ovtcharova

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 8/2023

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Abstract

As an important stage of life cycle management, machinery PHM (prognostics and health management), an emerging subject in mechanical engineering, has seen a huge amount of research. Here the authors present a comprehensive overview that details previous and current efforts in PHM from an industrial big data perspective. The authors first analyze the historical development of industrial big data and its distinction from big data of other domains and summarize the sources, types, and processing modes of industrial big data. Then, the authors provide an overview of common representation and fusion (data pre-processing) methods of industrial big data. Next, the authors comprehensively review common PHM methods in the data-driven context, focusing on the application of deep learning. Finally, two industrial cases from our previous studies are included in this paper to demonstrate how the PHM technique may facilitate the manufacturing industry. Furthermore, a visual bibliography is developed for displaying current results of PHM in an appropriate theme. The bibliography is open source at “https://​mango-hund.​github.​io/​”. The authors believe that future research endeavors will require an understanding of this previous work, and our efforts in this paper will make it possible to customize and integrate PHM systems quickly for a variety of applications.
Vorheriger Artikel A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing
Nächster Artikel A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

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Metadaten
Titel
A systematic review of data-driven approaches to fault diagnosis and early warning
verfasst von
Peng Jieyang
Andreas Kimmig
Wang Dongkun
Zhibin Niu
Fan Zhi
Wang Jiahai
Xiufeng Liu
Jivka Ovtcharova
Publikationsdatum
15.09.2022
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 8/2023
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-022-02020-0

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    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

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    Bildnachweise