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

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...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.88457-88468
Main Authors: Park, Hyun Joon, Kim, Taehyeong, Kim, Young Seok, Min, Jinhong, Sung, Ki Woo, Han, Sung Won
Format: Article
Language:English
Subjects:
Attention mechanism
automobile
Automobile industry
Automobiles
Automotive engineering
Automotive parts
Coders
Component reliability
Cost control
Costs
Data models
Decision making
Performance evaluation
Predictive models
Production costs
Quality assurance
Quality control
Reliability
Reliability analysis
reliability prediction
Survival analysis
transformer
Transformers
Warranties
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3200472