“The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize nonlinear systems through a generalized kernel representation for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis, we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance with the use of this bridge. In order to have a better understanding of the results obtained, both unsupervised and supervised neural networks are chosen as the learning tools to identify the generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This article is a perspective article, whose contribution lies in proposing and formalizing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems.”

中文翻译：

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CIS 出版物聚焦 [Publication Spotlight]


“自动化系统的复杂性和智能性日益增加，需要开发智能故障诊断 （IFD） 方法。通过依靠可疑空间的概念，本研究为非线性动态系统开发了可解释的数据驱动的 IFD 方法。更具体地说，我们通过广义核表示来参数化非线性系统，用于系统建模和相关的故障诊断。获得的一个重要结果是内核表示的统一形式，适用于无监督学习和有监督学习。更重要的是，通过严格的理论分析，我们发现了一些基于监督和无监督学习的实体之间存在桥梁（即双射映射）。值得注意的是，设计的 IFD 方法与使用此电桥实现了相同的性能。为了更好地理解获得的结果，选择无监督和有监督神经网络作为识别广义核表示和设计 IFD 方案的学习工具;然后使用可逆神经网络在它们之间架起桥梁。本文是一篇有观点的文章，其贡献在于提出和形式化可解释的智能学习方法的基本概念，为非线性动态系统的系统建模和数据驱动的 IFD 设计做出了贡献。