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An enhanced encoder–decoder framework for bearing remaining useful life prediction
Measurement ( IF 5.6 ) Pub Date : 2020-11-20 , DOI: 10.1016/j.measurement.2020.108753
Lu Liu , Xiao Song , Kai Chen , Baocun Hou , Xudong Chai , Huansheng Ning

In recent years, data-driven approaches for remaining useful life (RUL) prognostics have aroused widespread concern. Bearings act as the fundamental component of machinery and their conditioning status is closely associated with the normal operation of equipment. Hence, it is crucial to accurately predict the remaining useful life of bearings. This paper explores the degradation process of bearings and proposes an enhanced encoder–decoder framework. The framework attempts to construct a decoder with the ability to look back and selectively mine underlying information in the encoder. Additionally, trigonometric functions and cumulative operation are employed to enhance the quality of health indicators. To verify the effectiveness of the proposed method, vibration data from PRONOSTIA platform are utilized for RUL prognostics. Compared with several state-of-the-art methods, the experimental results demonstrate the superiority and feasibility of the proposed method.



中文翻译:

增强的编解码器框架,用于预测剩余使用寿命

近年来,数据驱动的剩余使用寿命(RUL)预测方法已引起广泛关注。轴承是机械的基本组成部分,其调节状态与设备的正常运行密切相关。因此,准确预测轴承的剩余使用寿命至关重要。本文探讨了轴承的退化过程,并提出了一种增强的编码器-解码器框架。该框架试图构造一种解码器,使其具有回头并有选择地挖掘编码器中的基础信息的能力。另外,采用三角函数和累积运算来提高健康指标的质量。为了验证所提出方法的有效性,将PRONOSTIA平台的振动数据用于RUL预测。

更新日期:2020-11-21
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