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Article Dans Une Revue International Journal of Prognostics and Health Management Année : 2023

Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating Conditions

Moncef Soualhi
  • Fonction : Auteur
Khanh Nguyen
Kamal Medjaher
Guy Clerc

Résumé

Rotating machines are widely used in several fields such as railways, renewable energies, robotics, etc. This diversity of application implies a large variety of faults of critical components susceptible to fail. For this purpose, prognostics and health management (PHM) is deployed to effectively monitor these components through the detection, diagnostics as well as prognostics of faults. In the literature, there exist numerous methods to ensure the above monitoring activities. However, few of them consider different failure types using heterogeneous data and various operating conditions. Also, there are no dominant methods that can be generalized for monitoring. For this reason, the genericity of these methods and their applicability in several systems is a crucial issue. To help researchers to achieve the above challenges, this paper presents a detailed description of data sources from experimental test benches. These data-sets correspond to different case studies that monitor the health states of multiple critical components in various operating conditions using numerous sensors.

Dates et versions

hal-04288579 , version 1 (16-11-2023)

Identifiants

Citer

Moncef Soualhi, Abdenour Soualhi, Khanh Nguyen, Kamal Medjaher, Guy Clerc, et al.. Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating Conditions. International Journal of Prognostics and Health Management, 2023, 14 (2), ⟨10.36001/ijphm.2023.v14i2.3497⟩. ⟨hal-04288579⟩
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