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Prognosis of Dynamical System Components with Varying Degradation Patterns using model-data-fusion

机译：使用模型 - 数据融合具有不同劣化模式的动态系统成分的预后

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摘要

Failure prognosis is used to predict the future degradation and remaining useful life (RUL) of components. However, identification of future degradation and RUL of components is challenging when similar components in the same or different working conditions show varying degradation patterns. This is again more challenging when the component shows highly nonlinear degradation behavior, which may lead to erroneous RUL prediction and wrong prognosis. In this paper, a hybrid approach with the fusion of model-based and data-driven approaches is developed for the prognosis of dynamical system components whose degradations may follow different nonlinear trends. Here, degradation levels of different components are identified by using bond graph model-based distributed prognosis approach. However, artificial neural network based degradation models learned from the run-to-failure data of the components are used to predict the future degradation patterns and RUL of the components. The developed hybrid approach is applied to an electronic circuit test bed.
机译：失败预后用于预测组件的未来降解和剩余使用寿命（RUL）。然而，当相同或不同的工作条件中的类似组分显示不同的降解模式时，识别未来的降解和组件的rul是具有挑战性的。当组件显示出高度非线性降解行为时，这再次挑战，这可能导致错误的rul预测和错误预后。在本文中，开发了一种具有模型和数据驱动方法的融合的混合方法，用于降解可能遵循不同非线性趋势的动态系统组件的预后。这里，通过使用基于键曲线图模型的分布式预后方法来识别不同组分的劣化水平。然而，从组件的碰到故障数据中学习的基于人工神经网络的基于劣化模型用于预测组件的未来劣化模式和ruL。开发的混合方法应用于电子电路试验台。

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