Class-based neural network method for fault location of large-scale analogue circuits

Y. He; Y. Tan; Y. Sun

doi:10.1109/ISCAS.2003.1206417

Class-based neural network method for fault location of large-scale analogue circuits

Y. He, Y. Tan, Y. Sun

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

7 Citations (Scopus)

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Abstract

A new method for fault diagnosis of large-scale analogue circuits based on the class concept is developed in this paper. A large analogue circuit is decomposed into blocks/sub-circuits and the nodes between the blocks are classified into three classes. Only those sub-circuits related to the faulty class need to be treated. Node classification reduces the scope of search for faults, thus reduced after-test time. The proposed method is more suitable for real-time testing and can deal with both hard and soft faults. Tolerance effects are taken into account in the method. The class-based fault diagnosis principle and neural network based method are described in some details. Two non-trivial circuit examples are presented, showing that the proposed method is feasible.

Original language	English
Title of host publication	Procs of the 2003 Int Symposium on Circuits and Systems
Subtitle of host publication	ISCAS '03
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	733-736
Volume	5
DOIs	https://doi.org/10.1109/ISCAS.2003.1206417
Publication status	Published - 2003

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10.1109/ISCAS.2003.1206417

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title = "Class-based neural network method for fault location of large-scale analogue circuits",

abstract = "A new method for fault diagnosis of large-scale analogue circuits based on the class concept is developed in this paper. A large analogue circuit is decomposed into blocks/sub-circuits and the nodes between the blocks are classified into three classes. Only those sub-circuits related to the faulty class need to be treated. Node classification reduces the scope of search for faults, thus reduced after-test time. The proposed method is more suitable for real-time testing and can deal with both hard and soft faults. Tolerance effects are taken into account in the method. The class-based fault diagnosis principle and neural network based method are described in some details. Two non-trivial circuit examples are presented, showing that the proposed method is feasible.",

author = "Y. He and Y. Tan 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 = "2003",

doi = "10.1109/ISCAS.2003.1206417",

language = "English",

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pages = "733--736",

booktitle = "Procs of the 2003 Int Symposium on Circuits and Systems",

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AU - He, Y.

AU - Tan, 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 - 2003

Y1 - 2003

N2 - A new method for fault diagnosis of large-scale analogue circuits based on the class concept is developed in this paper. A large analogue circuit is decomposed into blocks/sub-circuits and the nodes between the blocks are classified into three classes. Only those sub-circuits related to the faulty class need to be treated. Node classification reduces the scope of search for faults, thus reduced after-test time. The proposed method is more suitable for real-time testing and can deal with both hard and soft faults. Tolerance effects are taken into account in the method. The class-based fault diagnosis principle and neural network based method are described in some details. Two non-trivial circuit examples are presented, showing that the proposed method is feasible.

AB - A new method for fault diagnosis of large-scale analogue circuits based on the class concept is developed in this paper. A large analogue circuit is decomposed into blocks/sub-circuits and the nodes between the blocks are classified into three classes. Only those sub-circuits related to the faulty class need to be treated. Node classification reduces the scope of search for faults, thus reduced after-test time. The proposed method is more suitable for real-time testing and can deal with both hard and soft faults. Tolerance effects are taken into account in the method. The class-based fault diagnosis principle and neural network based method are described in some details. Two non-trivial circuit examples are presented, showing that the proposed method is feasible.

U2 - 10.1109/ISCAS.2003.1206417

DO - 10.1109/ISCAS.2003.1206417

M3 - Conference contribution

VL - 5

SP - 733

EP - 736

BT - Procs of the 2003 Int Symposium on Circuits and Systems

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Class-based neural network method for fault location of large-scale analogue circuits

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