Detecting changes at the sensor level in Cyber-Physical Systems: Methodology and Technological Implementation

Self-adaptive Cyber-Physical Systems (CPSs) enrich CPSs functionalities by introducing self-configuration, self-management, and self-healing skills. Such skills, which are crucial to support adaptation mechanisms, take advantage of the ability to detect changes in the acquired datastreams, e.g., induced by faults affecting sensors/actuators or time-variant environments. In turn, change detection permits CPSs to enable adaptive mechanisms such as reconfiguration of some functionalities to track or mitigate the effect of the change. This paper introduces a novel methodology together with a technological implementation specifically designed for detecting changes affecting the sensor acquisitions in units of CPSs. The methodology requires: 1) learning the signal model; 2) design a model-free change detection test; 3) design a change-point method to validate the detected change. A technological implementation of the proposed methodology encompassing linear predictive models, the ICI-based change detection test and the Mann-Whitney change-point method is introduced and tested on the ST STM32 Nucleo platform. The high detection accuracy altogether with the low computational load and memory occupation make the proposed methodology (and its technological implementation) well suited for self-adaptive CPSs.