Condition-based component replacement of the pneumatic valve with the unscented particle filter

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外文摘要：This paper investigates the condition-based maintenance (CBM) that concerns the component replacement strategy based on the estimation of the failure probability distribution。 To obtain the accurate estimation of the distribution， specifically in non-linear case， an improved model-based Monte Carlo method， the unscented particle filter (UPF)， is introduced。 With the estimation of the failure probability， the replacement is determined by minimizing a decision variable called the expected cost per unit time， which considers both the replacement upon failure and preventive replacement。 Simulated experiments are performed with regards to a pneumatic valve， a normally-closed and gas-actuated valve， whose dynamic physical model is studied a lot in recent years。 The experiment results illustrate that with the accurate prediction of the probability distribution of the component's remaining life， we can effectively realize the condition-based component replacement and risk-informed life-extension in many application domains， such as nuclear， aerospace and chemical ones。

外文关键词：

Approximation methodsAtmospheric measurementsDegradationNoiseParticle filtersValvesVectorsconditiion-based replacementprognosisunscented particle filter

作者：

Tao, Tao、Zhao, Wei、Zio, Enrico、Li, Yan-Fu、Sun, Jinping

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作者单位：

Group 203, School of Electronic and Information Engineering, Beihang University, Beijing, China

会议名称：

IEEE Prognostics and System Health Management Conference

会议地点：

Zhangjiajie(CN)

会议母体文献：

IEEE 2014 Prognostics and System Health Management Conference

页码：

290-296

出版时间：

2014

DOI：

10.1109/PHM.2014.6988181