A framework for reconciliating data clusters from a fleet of nuclear power plants turbines for fault diagnosis-Reference-Cited by-同舟云学术

A framework for reconciliating data clusters from a fleet of nuclear power plants turbines for fault diagnosis

Published:2018-08 Issue: Volume:69 Page:213-231
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Al-Dahidi Sameer,Di Maio Francesco,Baraldi Piero,Zio Enrico,Seraoui Redouane

Publisher

Elsevier BV

Subject

Software

Reference68 articles.

1. Review article methods for fault detection, diagnostics, and prognostics for building systems—a review, part I;Katipamula;HVAC&R Res.,2005

2. Development of asset fault signatures for Prognostic and Health Management in the nuclear industry;Agarwal;2014 Int. Conf. Progn. Heal. Manag., IEEE,2014

3. Predictive diagnosis based on a fleet-wide ontology approach;Medina-Oliva;Knowl.-Based Syst.,2014

4. A review on machinery diagnostics and prognostics implementing condition-based maintenance;Jardine;Mech. Syst. Signal Process.,2006

5. Integrated software platform for fleet data analysis, enhanced diagnostics, and safe transition to prognostics for helicopter component CBM;Patrick,2010

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unsupervised dissimilarity-based fault detection method for autonomous mobile robots;Autonomous Robots;2023-10-28

2. A two-stage estimation method based on Conceptors-aided unsupervised clustering and convolutional neural network classification for the estimation of the degradation level of industrial equipment;Expert Systems with Applications;2023-03

3. Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges;IEEE Access;2023

4. Introduction of an industrial transfer learning use case systematization for machine tools;Procedia CIRP;2023

5. Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models;Journal of Engineering for Gas Turbines and Power;2022-01-21