Abstract
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 1) 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 2) 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.
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