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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Valve Failure Prognostics in Reciprocating Compressors Utilizing Temperature Measurements, PCA-Based Data Fusion, and Probabilistic Algorithms
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Valve Failure Prognostics in Reciprocating Compressors Utilizing Temperature Measurements, PCA-Based Data Fusion, and Probabilistic Algorithms

机译：阀门故障预测在往复式压缩机中利用温度测量，基于PCA的数据融合和概率算法的往复式压缩机

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摘要

In this paper, temperature measurements are utilized to develop health indicators based on principal component analysis toward the probabilistic estimation of the remaining useful life (RUL) of reciprocating compressors in service. Temperature degradation histories obtained from 13 actual valve failure cases constitute the training data in a data-driven prognostic approach. Two data-driven prognostic methodologies are presented and proposed based on probabilistic mathematical models, i.e., gradient boosted trees and nonhomogeneous hidden semi-Markov models. The training and testing process of all models is described in detail. RUL prognostics in unseen data are obtained for all models. Beyond the mean estimates of the RUL, the uncertainty associated with the point prediction is quantified and upper/lower confidence bounds are also estimated. Prediction estimates for 12 real-life failure cases are presented and the pros and cons of each model's performance are highlighted. Several metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding the prognostic capabilities of each of them.
机译：在本文中，利用温度测量来发展基于主成分分析的健康指标，朝向往复式压缩机剩余使用寿命（RUL）的概率估计。从13个实际阀门故障情况下获得的温度劣化历史构成数据驱动的预后方法的培训数据。基于概率数学模型，即梯度提高树木和非均匀隐藏半马尔可夫模型提出并提出了两种数据驱动的预后方法。详细描述了所有模型的培训和测试过程。针对所有型号获得了UNESEN数据中的RUL预测。除了规划的平均估计之外，还量化了与点预测相关的不确定性，并且还估计上/下置信界限。提出了12个现实寿命故障情况的预测估计，突出显示每个模型性能的优缺点。利用几个度量来评估预后算法的性能，并得出关于它们每个每个的预后能力的结论。

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