Advanced Symbolic Time Series Analysis in Cyber Physical Systems

Abstract

This paper presents advanced symbolic time series analysis (ASTSA) for large data sets emanating from cyber physical systems (CPS). The definition of CPS most pertinent to this paper is: A CPS is a system with a coupling of the cyber aspects of computing and communications with the physical aspects of dynamics and engineering that must abide by the laws of physics. This includes sensor networks, real-time and hybrid systems. To ensure that the computation results conform to the laws of physics a linear differential operator (LDO) is embedded in the processing channel for each sensor. In this manner the dynamics of the system can be incorporated prior to performing symbolic analysis. A non-linear quantization is used for the intervals corresponding to the symbols. The intervals are based on observed modes of the system, which can be determined either during an exploratory phase or online during operation of the system. A complete processing channel is called a single channel lexical analyser; one is made available for each sensor on the machine being observed. The implementation of LDO in the system is particularly important since it enables the establishment of a causal link between the observations of the dynamic system and their cause. Without causality there can be no semantics and without semantics no knowledge acquisition based on the physical background of the system being observed. Correlation alone is not a guarantee for causality. This work was originally motivated from the observation of large bulk mate- rial handling systems. Typically, there are $n = 150\dots250$ sensors per machine, and data is collected in a multi rate manner; whereby general sensors are sampled with $f_s = 1Hz$