2021•Unpublished venueRequires access

Time-to-Failure Prediction of Electronic Devices Based on Hawkes Point Process

Lili Guan, Jinglong Guan, Jiacheng Li

Open publisher page 0 citations

Abstract

With the widespread use of ultra-large-scale integrated circuits in aerospace, the trend in aerospace electronics is to have more complex structures and higher levels of automation. Although this trend has improved the performance of products, it also caused a series of problems for maintenance assurance, such as high repair cost of damaged devices, which can seriously affect the integrity of aerospace electronic systems and depreciate their life cycle. To address this problem, this paper proposes a method to predict the damage time of aerospace electronics based on the Hawkes point process, which can provide advance warning for the replacement of electronics. The proposed method takes advantage of the Hawkes point process in time series modeling to further improve the accuracy of the prediction. Experiments in 19 modules of an aerospace electronic device demonstrate that the proposed method can accurately predict the failure time of the aerospace electronic device through the survival function.

About this research paper

What this paper is about

With the widespread use of ultra-large-scale integrated circuits in aerospace, the trend in aerospace electronics is to have more complex structures and higher levels of automation. Although this trend has improved the performance of products, it also caused a series of problems for maintenance assurance, such as high repair cost of damaged devices, which can seriously affect the integrity of aerospace electronic systems and depreciate their life cycle. To address this problem, this paper proposes a method to predict the damage time of aerospace electronics based on the Hawkes point process, which can provide advance warning for the replacement of electronics. The proposed method takes advantage of the Hawkes point process in time series modeling to further improve the accuracy of the prediction. Experiments in 19 modules of an aerospace electronic device demonstrate that the proposed method can accurately predict the failure time of the aerospace electronic device through the survival function.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

With the widespread use of ultra-large-scale integrated circuits in aerospace, the trend in aerospace electronics is to have more complex structures and higher levels of automation. Although this trend has improved the performance of products, it also caused a series of problems for maintenance assurance, such as high repair cost of damaged devices, which can seriously affect the integrity of aerospace electronic systems and depreciate their life cycle. To address this problem, this paper proposes a method to predict the damage time of aerospace electronics based on the Hawkes point process, which can provide advance warning for the replacement of electronics. The proposed method takes advantage of the Hawkes point process in time series modeling to further improve the accuracy of the prediction. Experiments in 19 modules of an aerospace electronic device demonstrate that the proposed method can accurately predict the failure time of the aerospace electronic device through the survival function.

Key concepts: Aerospace, Electronics, Computer science, Process (computing), Reliability engineering, Point (geometry), Automation, Engineering

Related papers

Back to paper searchBrowse research topicsOriginal source
Time-to-Failure Prediction of Electronic Devices Based on Hawkes Point Process — Research Paper | ScholarLens