Model free fault diagnosis for large scale cyber physical systems

Detecting and isolating faults in Cyber-Physical Systems (CPSs), e.g., critical infrastructures, smart buildings/cities and the Internet-of-Things, are tasks that do generally scale badly with the CPS size. This work introduces a model-free Fault Detection and Diagnosis System (FDDS) designed having in mind scalability issues, so as to be able to detect and isolate faults in CPSs characterised by a large number of sensors. Following the model-free approach, the proposed FDDS learns the nominal fault-free conditions of the large-scale CPS autonomously by exploiting the temporal and spatial relationships existing among sensor data. The novelties in this paper reside in a) a clustering method proposed to partition the large-scale CPS into groups of highly correlated sensors in order to grant scalability of the proposed FDDS, and b) the design of model- and fault-free mechanisms to detect and isolate multiple sensor faults, and disambiguate between sensor faults and time variance of the physical phenomenon the cyber layer of CPS inspects.