Accuracy Lifetime Prediction Methodology for RV Reducer

Liangbo Yao (), Cheng Qiu, Yikun Yang, Yu Gong and Jinfeng Li
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Liangbo Yao: Yanqi Lake Institute of Basic Manufacturing Technology Co.
Cheng Qiu: Yanqi Lake Institute of Basic Manufacturing Technology Co.
Yikun Yang: Yanqi Lake Institute of Basic Manufacturing Technology Co.
Yu Gong: Yanqi Lake Institute of Basic Manufacturing Technology Co.
Jinfeng Li: Yanqi Lake Institute of Basic Manufacturing Technology Co.

A chapter in Data-Driven Methods for Reliability and Safety Engineering: Applications in Industrial Systems, 2026, pp 47-60 from Springer

Abstract: Abstract To address the critical challenges associated with limited predictive accuracy and narrow applicability in existing precision degradation and lifetime prediction models for high-precision RV reducers, this study introduces a novel methodology through three primary contributions. First, a three-stage Gamma process-based accuracy degradation model is developed by systematically capturing the characteristic deterioration patterns of RV reducers, effectively enhancing the stochastic representation of change points beyond the capabilities of conventional approaches. Second, a Monte Carlo bootstrap-based data augmentation method is implemented to improve the reliability of degradation trajectory estimation under small-sample conditions. Third, a lifetime prediction framework, driven by accelerated testing for accuracy degradation and incorporating the equivalent lifetime calculation method, is developed to overcome the applicability limitations of traditional models. Case study validation confirms that the proposed three-stage model reduces relative error in remaining useful life estimation by 75%. This methodology offers precise support for proactive health management of in-service RV reducers in industrial robotics and high-precision transmission applications.

Keywords: RV reducer; Gamma process; Prediction of accuracy lifetime; Monte Carlo bootstrap method; Change points (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-032-22873-4_5

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DOI: 10.1007/978-3-032-22873-4_5

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