Failure Rate Identification of a Reparable System Governed by Coupled ODE-PDEs and Deep Learning based Implementation
Failure Rate Identification of a Reparable System Governed by Coupled ODE-PDEs and Deep Learning based Implementation
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DOI：
10.23919/acc55779.2023.10156339
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Weiwei Hu;Alexander Tepper;B. Xie;Qing Zhang
中科院分区：
文献类型：
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作者：
Weiwei Hu;Alexander Tepper;B. Xie;Qing Zhang
本文研究了三状态可修系统的机器故障率识别问题，并利用深度学习实现了该问题。数学模型由一个包含耦合的偏微分方程和积分微分方程的分布参数系统控制。这项工作的目标是使用采样系统输出测量来确定故障率。提出了基于深度学习的故障率识别方法。数值算例说明了设计和结果。
This paper is concerned with the problem of machine failure rate identification of a 3-state reparable system and its implementation via deep learning. The mathematical model is governed by a distributed parameter system involving coupled partial and integro-differential equations. The objective of this work is to identify the failure rates using the sampled system output measurements. Deep learning based failure rate identification methods are proposed. Numerical examples are provided to illustrate the designs and results.