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Sökning: WFRF:(Li Yong) > Mälardalens universitet

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1.
  • Petropoulos, Fotios, et al. (författare)
  • Operational Research : methods and applications
  • 2024
  • Ingår i: Journal of the Operational Research Society. - : Taylor & Francis Group. - 0160-5682 .- 1476-9360. ; 75:3, s. 423-617
  • Forskningsöversikt (refereegranskat)abstract
    • Throughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.
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  • Li, Xin, et al. (författare)
  • Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging
  • 2023
  • Ingår i: Reliability Engineering & System Safety. - : Elsevier. - 0951-8320 .- 1879-0836. ; 230
  • Tidskriftsartikel (refereegranskat)abstract
    • Fault diagnosis is of great significance to ensure the reliability and safety of complex bevel gearbox systems. Most existing intelligent fault diagnosis approaches of bevel gearboxes are designed with vibration monitoring. However, the collected vibration data are vulnerable to noise pollution and machinery operating conditions. Besides, traditional fault diagnosis models highly rely on numerous labeled samples, and neglect the high cost of label annotation in real-world applications. Therefore, a novel fault diagnosis approach based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging is proposed for bevel gearboxes in this paper, which has the following properties. Firstly, SPSMM classifies 2D matrix data directly without vectorization, thus fully utilizing the spatial information in infrared images. Secondly, a probability output strategy is designed for SPSMM to calculate the posterior class probability estimation of matrix inputs, and consequently enhance the diagnostic accuracy and robustness of the model. Thirdly, a semi-supervised learning (SSL) framework is proposed for SPSMM to carry out sample transfer from the unlabeled sample pool to the labeled sample pool, which can effectively alleviate the problem of insufficient labeled samples. The superiority of the proposed diagnosis approach is demonstrated with an infrared imaging dataset of a bevel gearbox.
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  • Resultat 1-4 av 4

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