CRFormer: Complementary Reliability Perspective Transformer for Automotive Components Reliability Prediction Based on Claim Data

Hyun Joon Park, Taehyeong Kim, Young Seok Kim, Jinhong Min, Ki Woo Sung, Sung Won Han

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)

    Abstract

    Reliability prediction has been studied in many industries for managing stocks and reducing quality assurance costs and production costs. Particularly, in the automotive industry, reliability prediction is performed based on two automobile reliability perspectives, time and mileage. To maximize cost savings, researchers attempted reliability prediction with short-term inputs. However, limited information on short-term inputs resulted in unsatisfactory prediction results for the long warranty periods. Additionally, the overall evaluation metrics could not reflect the pattern-wise performance, such as the increasing failure patterns. This study proposes Complementary Reliability perspective Transformer (CRFormer) based on Transformer encoder to achieve enriched representations from a short-term input sequence. CRFormer fuses different automobile reliability perspective information and automobile features to compensate for the limited information on short-term input. The performance of CRFormer is evaluated based on automobile claim data accumulated over 16 years. Results showed that compared to previous methods in terms of overall, pattern-wise, and pattern similarity evaluation metrics, CRFormer achieved outstanding performance in time and mileage reliability prediction. Lastly, visualization results and survival analysis based on accurate model prediction can be used to support decision-making to reduce quality assurance costs and production costs.

    Original languageEnglish
    Pages (from-to)88457-88468
    Number of pages12
    JournalIEEE Access
    Volume10
    DOIs
    Publication statusPublished - 2022

    Bibliographical note

    Publisher Copyright:
    © 2013 IEEE.

    Keywords

    • Attention mechanism
    • automobile
    • reliability prediction
    • transformer

    ASJC Scopus subject areas

    • General Engineering
    • General Materials Science
    • General Computer Science

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