Keyword search (3,448 papers available)


Distributed adaptive fault-tolerant close formation flight control of multiple trailing fixed-wing UAVs.

Author(s): Yu Z, Zhang Y, Jiang B, Yu X, Fu J, Jin Y, Chai T

ISA Trans. 2020 Jul 09;: Authors: Yu Z, Zhang Y, Jiang B, Yu X, Fu J, Jin Y, Chai T

Article GUID: 32680604
Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis.

Author(s): Yang J, Xie G, Yang Y, Zhang Y, Liu W

ISA Trans. 2019 May 30;: Authors: Yang J, Xie G, Yang Y, Zhang Y, Liu W

Article GUID: 31174854
Image denoising via overlapping group sparsity using orthogonal moments as similarity measure.

Author(s): Kumar A, Ahmad MO, Swamy MNS

ISA Trans. 2019 Feb;85:293-304 Authors: Kumar A, Ahmad MO, Swamy MNS

Article GUID: 30392726
Title:Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis.
Authors:Yang J,  Xie G,  Yang Y,  Zhang Y,  Liu W
Link:https://www.ncbi.nlm.nih.gov/pubmed/31174854?dopt=Abstract
DOI:10.1016/j.isatra.2019.05.021
Category:ISA Trans
PMID:31174854
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]