Causal Discovery in Electronic Circuits and Its Application in Fault Diagnosis

Mohammed Tuhin Rana, Soham Chakraborty, Murti V. Salapaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This article demonstrates that causal discovery approaches can be applied to analog electronic circuits made of Bipolar Junction Transistors (BJTs) to find out the causal relationships among different variables of the circuit. Moreover, the obtained causal relationship structure in the form of a Directed Acyclic Graph (DAG), can be used for diagnosis and analysis of such circuits. First, it is shown that the operation process of a transistor has an inherent notion of causality, which is then exploited to show that the various parameters of a BJT circuit can be expressed in the form of Structural Equation Models (SEM). The results demonstrated using data generated using LTspice establishes that the causal structure of a BJT circuit can be retrieved using data driven causal discovery algorithms. This opens new horizons for analysis and diagnosis of BJT circuits. An example case study of circuit diagnosis is presented to showcase the capability and efficiency of the proposed method.

Original languageEnglish (US)
Title of host publication2023 62nd IEEE Conference on Decision and Control, CDC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8223-8228
Number of pages6
ISBN (Electronic)9798350301243
DOIs
StatePublished - 2023
Event62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore
Duration: Dec 13 2023Dec 15 2023

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference62nd IEEE Conference on Decision and Control, CDC 2023
Country/TerritorySingapore
CitySingapore
Period12/13/2312/15/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

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