Keyword search (4,163 papers available)

"Diagnosis" Keyword-tagged Publications:

Title Authors PubMed ID
1 Joint enhancement of automatic chest x-ray diagnosis and radiological gaze prediction with multistage cooperative learning Qiu Z; Rivaz H; Xiao Y; 40665596
ENCS
2 Microfluidic Liquid Biopsy Minimally Invasive Cancer Diagnosis by Nano-Plasmonic Label-Free Detection of Extracellular Vesicles: Review Neriya Hegade KP; Bhat RB; Packirisamy M; 40650129
ENCS
3 Alzheimer's early detection in post-acute COVID-19 syndrome: a systematic review and expert consensus on preclinical assessments Vandersteen C; Plonka A; Manera V; Sawchuk K; Lafontaine C; Galery K; Rouaud O; Bengaied N; Launay C; Guérin O; Robert P; Allali G; Beauchet O; Gros A; 37416323
CONCORDIA
4 Primary and Secondary Progressive Aphasia in Posterior Cortical Atrophy Brodeur C; Belley É; Deschênes LM; Enriquez-Rosas A; Hubert M; Guimond A; Bilodeau J; Soucy JP; Macoir J; 35629330
IMAGING
5 X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech Jeancolas L; Petrovska-Delacrétaz D; Mangone G; Benkelfat BE; Corvol JC; Vidailhet M; Lehéricy S; Benali H; 33679361
PERFORM
6 Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines. Shen Y, Khorasani K 32673847
ENCS
7 The Comprehensive Assessment of Neurodegeneration and Dementia: Canadian Cohort Study. Chertkow H, Borrie M, Whitehead V, Black SE, Feldman HH, Gauthier S, Hogan DB, Masellis M, McGilton K, Rockwood K, Tierney MC, Andrew M, Hsiung GR, Camicioli R, Smith EE, Fogarty J, Lindsay J, Best S, Evans A, Das S, Mohaddes Z, Pilon R, Poirier J, Phillips NA, MacNamara E, Dixon RA, Duchesne S, MacKenzie I, Rylett RJ 31309917
PSYCHOLOGY
8 Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis. Yang J, Xie G, Yang Y, Zhang Y, Liu W 31174854
ENCS

 

Title:Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis.
Authors:Yang JXie GYang YZhang YLiu W
Link:https://www.ncbi.nlm.nih.gov/pubmed/31174854?dopt=Abstract
DOI:10.1016/j.isatra.2019.05.021
Publication:ISA transactions
Keywords:Correlation analysisFault diagnosisIntermittent faultsSparse autoencoder
PMID:31174854 Category:ISA Trans Date Added:2019-06-09
Dept Affiliation: ENCS
1 School of automation and information engineering, Xi'an University of Technology, Jinhua South Road, Beilin District, Xi'an, China; School of mechatronics and automotive engineering, Tianshui Normal University, Xihe South Road, Qinzhou District, Tianshui, China.
2 School of automation and information engineering, Xi'an University of Technology, Jinhua South Road, Beilin District, Xi'an, China. Electronic address: guoxie@xaut.edu.cn.
3 School of automation and information engineering, Xi'an University of Technology, Jinhua South Road, Beilin District, Xi'an, China.
4 Concordia University, 1455 de Maisonneuve Blvd. W. Montreal, Quebec H3G 1M8, Canada.

Description:

Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis.

ISA Trans. 2019 May 30;:

Authors: Yang J, Xie G, Yang Y, Zhang Y, Liu W

Abstract

Currently, single fault diagnosis has received mass concern, and the related research achievements are remarkable. However, because of the mutual interaction of subsystems and the coupling of faults characteristics, the diagnosis of multiple intermittent faults commonly existing in industrial systems is still an intractable problem. In order to solve the problem, an improved Constrained Sparse Autoencoder integrated with Correlation Analysis (CA-CSAE) is proposed, further, a diagnosis scheme for multiple intermittent faults is formulated in this paper. The main strategies are as follows. (1) An adaptive loss function and a constraint for initial weight are designed to improve the diversity and accuracy of SAE feature learning. (2) A relational constraint term is constructed to mitigate the effect of data correlation. (3) The evaluation criterion of data correlation degree is put forward to quantify the scope of the method. (4) In order to improve the diagnostic efficiency, ReLU is introduced as the activation function of hidden layer, and L-BFGS algorithm is employed to obtain the optimal solution. (5) Softmax classifier is employed as the output layer to identify fault mode and ensure the reliability of diagnosis results. Finally, comparison experiments and results analysis are conducted to verify the effectiveness and practicability of the proposed method.

PMID: 31174854 [PubMed - as supplied by publisher]





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