Treatment of Ventricular Assist Device Test Bench Data for Prediction of Failures and Improved Intrinsic Reliability - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2019

Treatment of Ventricular Assist Device Test Bench Data for Prediction of Failures and Improved Intrinsic Reliability

Jeferson C. Dias
  • Fonction : Auteur
  • PersonId : 1054753
Jônatas C. Dias
  • Fonction : Auteur
  • PersonId : 1054754
Edinei Legaspe
  • Fonction : Auteur
  • PersonId : 1022206
Rodrigo Lima Stoeterau
  • Fonction : Auteur
  • PersonId : 1054755
Fabrício Junqueira
  • Fonction : Auteur
  • PersonId : 976750
Newton Maruyama
  • Fonction : Auteur
  • PersonId : 1054756
Paulo E. Miyagi
  • Fonction : Auteur
  • PersonId : 976853
Diolino Filho
  • Fonction : Auteur
  • PersonId : 976751

Résumé

This article regards over analytics of reliability of ventricular assist devices (VAD) used as therapy for advanced heart failure conditions in the face of malfunction related adverse events. This question directs research and the search for a solution proposal, even if prospective, but that promotes the longevity of these devices, increasing the intrinsic reliability. An “In Vitro” test bench is used to obtain variations of dynamic behavior over time; by means of a set of variables and the deviations (failures) compared between the standard and tested devices; since these devices are systems that vary in time. An intelligent systematics obtained through the automation of the test bench, using sensors and actuators to control the independent variables, and the data collection and analysis using the technologies present in the industry 4.0 completes the increase of the reliability of the VAD.
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hal-02295262 , version 1 (24-09-2019)

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Jeferson C. Dias, Jônatas C. Dias, Edinei Legaspe, Rodrigo Lima Stoeterau, Fabrício Junqueira, et al.. Treatment of Ventricular Assist Device Test Bench Data for Prediction of Failures and Improved Intrinsic Reliability. 10th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), May 2019, Costa de Caparica, Portugal. pp.183-190, ⟨10.1007/978-3-030-17771-3_15⟩. ⟨hal-02295262⟩
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