Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning-Reference-Cited by-同舟云学术

Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

Published:2023 Issue: Volume:11 Page:25132-25151
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Maior Caio Bezerra Souto¹^ORCID,Araújo Lavínia Maria Mendes¹^ORCID,Lins Isis Didier¹^ORCID,Moura Márcio Das Chagas¹^ORCID,Droguett Enrique López²^ORCID

Affiliation:

1. Center for Risk Analysis, Reliability Engineering and Environmental Modeling (CEERMA), Universidade Federal de Pernambuco (UFPE), Recife, Brazil

2. Department of Civil and Environmental Engineering, University of California at Los Angeles, Los Angeles, CA, USA

Funder

Human Resources Program (PRH 38.1) through the Agência Nacional de Petróleo, Gás Natural e Biocombustíveis (ANP) and the Brazilian Funding Authority for Studies and Projects

Pró-Reitoria de Pós-Graduação (PROPG) of Universidade Federal de Pernambuco

Foundation of Support for Science and Technology of Pernambuco

Conselho Nacional de Desenvolvimento Científico Tenológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10065474.pdf?arnumber=10065474

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4. Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal

5. An End-to-End Model Based on Improved Adaptive Deep Belief Network and Its Application to Bearing Fault Diagnosis

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