Machine Learning (ML) has achieved great successes in recent decades, both in research and in practice. In Cyber-Physical Systems (CPS), ML can for example be used to optimize systems, to detect anomalies or to identify root causes of system failures. However, existing algorithms suffer from two major drawbacks: (i) They are hard to interpret by human experts. (ii) Transferring results from one systems to another (similar) system is often a challenge. Concept learning, or Representation Learning (RepL), is a solution to both of these drawbacks; mimicking the human solution approach to explain-ability and transfer-ability: By learning general concepts such as physical quantities or system states, the model becomes interpretable by humans. Furthermore concepts on this abstract level can normally be applied to a wide range of different systems. Modern ML methods are already widely used in CPS, but concept learning and transfer learning are hardly used so far. In this paper, we provide an overview of the current state of research regarding methods for learning physical concepts in time series data, which is the primary form of sensor data of CPS. We also analyze the most important methods from the current state of the art using the example of a three-tank system. Based on these concrete implementations1, we discuss the advantages and disadvantages of the methods and show for which purpose and under which conditions they can be used.
翻译:近几十年来,机器学习(ML)在研究和实践中都取得了巨大成功。网络物理系统(CPS)中,ML可以用来优化系统,发现异常现象或查明系统故障的根源。但是,现有的算法存在两大缺陷:(一) 人类专家很难解释。 (二) 从一个系统向另一个系统(类似)系统转移结果往往是一个挑战。概念学习或代表学习(RepL)是解决这两个缺陷的一个办法;模仿人类解决办法,以解释可及性和可转移性:通过学习诸如物理数量或系统状态等一般概念,模型可以被人类解释。此外,这一抽象层面的概念通常可以适用于不同的系统。现代ML方法在CPS中已经广泛使用,但概念学习和转移学习往往很少被使用。在本文中,我们概述了关于学习时间序列中物理概念方法的研究现状,这是CPS系统传感器数据的主要形式。我们还分析了目前使用的三种关键方法的优势,从这些优势的角度,从目前使用的方法的角度分析这些优势。