Compact Fault Dictionaries for Efficient Sensor Fault Diagnosis in IoT-enabled CPSs

Abstract

The recent advances in the area of Internet-of-Things (IoT) have allowed for the implementation of complex large-scale Cyber-Physical Systems (CPSs). This phenomenon calls for efficient and scalable solutions for the new challenges being introduced. Sensor fault diagnosis has emerged as a priority in various IoT-enabled CPSs, especially for critical infrastructure applications where multiple IoT devices might be in use. In this work, we examine the problem of building a compact fault dictionary which allows for efficient real-time model-based multiple sensor fault detection and isolation. The problem under consideration is formulated as a combinatorial set problem and then efficiently encoded using Zero-suppressed binary Decision Diagrams (ZDDs), which are specialized data structures based on Boolean theory. The proposed approach is highly scalable with respect to the total number of sensor fault scenarios considered. Using the respective ZDD as a fault dictionary reduces the memory requirements by several orders of magnitude when compared to the conventional approach. This is achieved while allowing the fault isolation process to occur in linear time to the size of the dictionary. Our experimental results show that it takes between 0.002s to 0.012s for performing the fault isolation process in the range of tested systems.