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首页> 外文期刊>Mechanical systems and signal processing >Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model
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Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model

机译:通过基于状态空间的退化模型融合在线监测数据来减少预测不确定性

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摘要

The objective of this study is to develop a state-space-based degradation model and associated computational techniques to reduce failure prognostics uncertainty by fusing on-line monitoring data. A key problem in failure prognostics for an individual system under actual operating conditions is uncertainty management. In this study, the various uncertainty sources in failure prognostics are analyzed, and an appropriate uncertainty quantifying and managing mechanism is proposed, accounting for both the item-to-item variability and the degradation process variability. The method is demonstrated on a crack growth data set, and the results show that the proposed prognostics method has the ability to provide a failure time prediction with less uncertainty by fusing sensor data, which are beneficial for risk assessment and optimal maintenance decision-making.
机译:这项研究的目的是开发一种基于状态空间的退化模型和相关的计算技术,以通过融合在线监测数据来减少故障预测的不确定性。在实际操作条件下单个系统的故障预测中的关键问题是不确定性管理。在这项研究中,分析了故障预测中的各种不确定性来源,并提出了一种适当的不确定性量化和管理机制,同时考虑了项目之间的差异和退化过程的差异。在裂纹扩展数据集上证明了该方法,结果表明,所提出的预测方法能够通过融合传感器数据来提供具有较少不确定性的故障时间预测,这对风险评估和最佳维护决策具有帮助。

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