Prognostics in Highly Accelerated Limit Testing Using Deep Learning Data Analysis

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Published Sep 4, 2023
Tadahiro Shibutani

Abstract

In this study, an anomaly detection analysis of electronic components was conducted using deep learning algorithms on time-series data of voltage monitored during highly accelerated limit testing (HALT) on inverters used in automobiles and other vehicles. We demonstrated that the anomaly detection technology of time-series data using deep learning could detect equipment anomalies/failures to achieve effective data representation, improving the reliability assurance technology with HALT.

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Keywords

Prognostics, Highly Accelerated, Limit Testing, Anomaly detection, Deep Learning

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Regular Session Papers