Video-based diagnosis of a rolling element bearing using a high-speed camera: Feedback on the Survishno 2023 conference contest

The aim of this paper is to provide a feedback on the signal processing contest organized at the Survishno/Resonance conference held in 2023 in Toulouse, France. The aim of the competition was to demonstrate the possibility of diagnosing a bearing operating at a variable rotation speed using high-speed video data only. To this end, a video of an operating faulty bearing was proposed to registered people a month before the event, with the task of extracting the instantaneous rotation speeds of the various rotating parts, and proposing a methodology for identifying the type of fault (which was only known by the contest organizers). Ten teams of researchers from academia and industry were then formed, and proposed different approaches, the results of which were compared with reference data. The diagnostic task proved difficult, with none of the teams achieving the correct diagnosis of the fault. However, it is shown in this paper that by crossing the results of the different teams, it was possible to achieve the correct diagnosis. A tutorial is proposed at the end of the paper, presenting the application of a complete processing chain from video data to order envelope spectral analysis. Results illustrate the ability to recover bearing fault signatures, and also show the possibility to enhance the diagnosis by taking advantage of the fine tracking of the position of each part of the system offered by the video.

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Publication Details

Journal
Mechanical Systems and Signal Processing
Published
2025-04-01
DOI
https://doi.org/10.1016/j.ymssp.2025.112601
Citations
2
Primary Topic
Gear and Bearing Dynamics Analysis
Type
article
Field-Weighted Citation Impact
1.43
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article

Video-based diagnosis of a rolling element bearing using a high-speed camera: Feedback on the Survishno 2023 conference contest

Cécile Capdessus, Gianluca D’Elia, François Girardin, Alexandre Mauricio et al.
2 citations
Mechanical Systems and Signal Processing
Gear and Bearing Dynamics Analysis
1.43
article

Video-based diagnosis of a rolling element bearing using a high-speed camera: Feedback on the Survishno 2023 conference contest

Cécile Capdessus, Gianluca D’Elia, François Girardin, Alexandre Mauricio, Luigi Garibaldi, Jean‐Luc Dion, Julien Griffaton, Hugo André, Deepti Kunte, Stefania Lo Feudo, Franck Renaud, Alessandro Paolo Daga, Luca Viale, Adrien Marsick, Jan Helsen, Mahsa Yazdanianasr, Marco Cocconcelli, Mohamed El Badaoui, Julien Roussel, Kayacan Kestel, Konstantinos Gryllias, Quentin Leclère, Jérôme Antoni, Yunhyeok Han, Johann Miranda Fuentes, Douw Marx, Didier Rémond, Rui Zhu, Jimmy Touzet, Fadi Karkafi, Toby Verwimp, Arthur Burel, Abdallah El Hidali, Thomas Poupon, Cédric Peeters, Layla Kordylas
article en
2 citations

Abstract

The aim of this paper is to provide a feedback on the signal processing contest organized at the Survishno/Resonance conference held in 2023 in Toulouse, France. The aim of the competition was to demonstrate the possibility of diagnosing a bearing operating at a variable rotation speed using high-speed video data only. To this end, a video of an operating faulty bearing was proposed to registered people a month before the event, with the task of extracting the instantaneous rotation speeds of the various rotating parts, and proposing a methodology for identifying the type of fault (which was only known by the contest organizers). Ten teams of researchers from academia and industry were then formed, and proposed different approaches, the results of which were compared with reference data. The diagnostic task proved difficult, with none of the teams achieving the correct diagnosis of the fault. However, it is shown in this paper that by crossing the results of the different teams, it was possible to achieve the correct diagnosis. A tutorial is proposed at the end of the paper, presenting the application of a complete processing chain from video data to order envelope spectral analysis. Results illustrate the ability to recover bearing fault signatures, and also show the possibility to enhance the diagnosis by taking advantage of the fine tracking of the position of each part of the system offered by the video.

Mechanical Systems and Signal ProcessingVol. 230
Université Claude Bernard Lyon 1 (FR), University of Modena and Reggio Emilia (IT), Centre National de la Recherche Scientifique (FR), Vrije Universiteit Brussel (BE), Politecnico di Torino (IT), Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique (FR), Flanders Make (Belgium) (BE), Safran Electronics (Canada) (CA), Institut Supérieur de Mécanique de Paris (FR), Safran (France) (FR), Laboratoire de Mécanique des Contacts et des Structures (FR), Institut National des Sciences Appliquées de Lyon (FR), Université Jean Monnet (FR), KU Leuven (BE)
Openalex Percentile: Top 21%
Gear and Bearing Dynamics Analysis
1.43
DOIOpen Access PDFOpenAlex
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