Prognostics aid in the longevity of fielded systems or products. Quantifying the system's current health enable prognosis to enhance the operator's decision-making to preserve the system's health. Creating a prognosis for a system can be difficult due to (a) unknown physical relationships and/or (b) irregularities in data appearing well beyond the initiation of a problem. Traditionally, three different modeling paradigms have been used to develop a prognostics model: physics-based (PbM), data-driven (DDM), and hybrid modeling. Recently, the hybrid modeling approach that combines the strength of both PbM and DDM based approaches and alleviates their limitations is gaining traction in the prognostics domain. In this paper, a novel hybrid modeling approach for prognostics applications based on combining concepts from fuzzy logic and generative adversarial networks (GANs) is outlined. The FuzzyGAN based method embeds a physics-based model in the aggregation of the fuzzy implications. This technique constrains the output of the learning method to a realistic solution. Results on a bearing problem showcases the efficacy of adding a physics-based aggregation in a fuzzy logic model to improve GAN's ability to model health and give a more accurate system prognosis.
翻译:野外系统或产品寿命的预测性帮助。 测量系统目前的健康状况可以预测增强操作者的决策能力,以维护系统的健康。 建立系统预测可能很难, 原因是:(a) 未知的物理关系和/或(b) 数据在远超出问题开始之后出现的异常情况。 传统上,使用三种不同的模型模式来开发预测性模型: 物理模型(PbM)、 数据驱动模型(DDM)和混合模型。 最近,混合模型法将PbM和DDM方法的强度结合起来,并减轻其局限性,这种混合模型法正在预测性领域获得牵引力。 在本文中,根据模糊逻辑概念和基因对抗网络(GANs)的综合概念,对预测性应用采用新的混合模型法。 以FuzzyGAN为基础的方法将基于物理模型的模式嵌入了模糊影响的聚合中。 这种模型法将精准性模型系统的产出限制在预测性域域域域域域中,使GAN的逻辑性模型能够向现实性解决方案学习。