Fault diagnosis of analog circuits with tolerances using artificial neural networks

Y. Deng; Y. He; Y. Sun

doi:10.1109/APCCAS.2000.913491

Fault diagnosis of analog circuits with tolerances using artificial neural networks

Y. Deng
, Y. He
, Y. Sun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

35 Citations (Scopus)

73 Downloads (Pure)

Abstract

This paper proposes a method for analog fault diagnosis using neural networks. The primary focus of the paper is to provide robust diagnosis using a mechanism to deal with the problem of component tolerances and reduce testing time. The proposed approach is based on the k-fault diagnosis method and artificial backward propagation neural network. Simulation results show that the method is robust and fast for fault diagnosis of analog circuits with tolerances.

Original language	English
Title of host publication	In: Procs of IEEE Asia-Pacific Conference on Circuits and Systems, APCCAS 2000
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	292-295
ISBN (Print)	0-7803-6253-5
DOIs	https://doi.org/10.1109/APCCAS.2000.913491
Publication status	Published - 2000

Access to Document

10.1109/APCCAS.2000.913491

904240.pdf

Cite this

@inproceedings{befd97ecfe204ebfad9ffafb92ca3425,

title = "Fault diagnosis of analog circuits with tolerances using artificial neural networks",

abstract = "This paper proposes a method for analog fault diagnosis using neural networks. The primary focus of the paper is to provide robust diagnosis using a mechanism to deal with the problem of component tolerances and reduce testing time. The proposed approach is based on the k-fault diagnosis method and artificial backward propagation neural network. Simulation results show that the method is robust and fast for fault diagnosis of analog circuits with tolerances.",

author = "Y. Deng and Y. He and Y. Sun",

note = "“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.{"} “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”",

year = "2000",

doi = "10.1109/APCCAS.2000.913491",

language = "English",

isbn = "0-7803-6253-5",

pages = "292--295",

booktitle = "In: Procs of IEEE Asia-Pacific Conference on Circuits and Systems, APCCAS 2000",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

}

TY - GEN

T1 - Fault diagnosis of analog circuits with tolerances using artificial neural networks

AU - Deng, Y.

AU - He, Y.

AU - Sun, Y.

N1 - “This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”

PY - 2000

Y1 - 2000

N2 - This paper proposes a method for analog fault diagnosis using neural networks. The primary focus of the paper is to provide robust diagnosis using a mechanism to deal with the problem of component tolerances and reduce testing time. The proposed approach is based on the k-fault diagnosis method and artificial backward propagation neural network. Simulation results show that the method is robust and fast for fault diagnosis of analog circuits with tolerances.

AB - This paper proposes a method for analog fault diagnosis using neural networks. The primary focus of the paper is to provide robust diagnosis using a mechanism to deal with the problem of component tolerances and reduce testing time. The proposed approach is based on the k-fault diagnosis method and artificial backward propagation neural network. Simulation results show that the method is robust and fast for fault diagnosis of analog circuits with tolerances.

U2 - 10.1109/APCCAS.2000.913491

DO - 10.1109/APCCAS.2000.913491

M3 - Conference contribution

SN - 0-7803-6253-5

SP - 292

EP - 295

BT - In: Procs of IEEE Asia-Pacific Conference on Circuits and Systems, APCCAS 2000

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Fault diagnosis of analog circuits with tolerances using artificial neural networks

Abstract

Access to Document

Fingerprint

Cite this