Abstract Historical failure instances of a system with diversified degradation patterns will pose great challenge for prognostics. Consequently, it is challenging to accurately predict the remaining useful life (RUL) using a prognostic model trained from such data. To solve this problem, this paper proposes a just-in-time learning-based data-driven prognostic method for reciprocating compressors with diverse degradation patterns and operating modes. The proposed framework employs a just-in-time learning (JITL) scheme to deal with the stochastic nature of fault evolution and the diversity of degradation patterns. Moreover, a data-driven forecasting model that features a randomized and smoothed gradient boosting decision tree (RS-GBDT) is developed for RUL and uncertainty predictions. The effectiveness of the proposed approach was validated on temperature measurements collected from 13 valve failure cases of an industrial reciprocating compressor.

中文翻译：

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用于阀门故障预测的基于即时学习的概率梯度提升树

摘要 具有多样化退化模式的系统的历史故障实例将对预测提出巨大挑战。因此，使用从此类数据训练的预后模型准确预测剩余使用寿命 (RUL) 具有挑战性。为了解决这个问题，本文提出了一种基于实时学习的数据驱动预测方法，用于具有不同退化模式和运行模式的往复式压缩机。所提出的框架采用即时学习（JITL）方案来处理故障演化的随机性和退化模式的多样性。此外，还为 RUL 和不确定性预测开发了一种数据驱动的预测模型，该模型具有随机和平滑的梯度提升决策树 (RS-GBDT)。