Abstract
The design of a distributed fault detection and isolation filter is addressed for stochastic nonlinear systems subjected to multiple failure modes. The monolithic process is monitored by a network of communicating detection nodes, in lieu of a singleton, supervising diagnostic unit. Each detection node is a fault-sensitive estimator unit with processing capabilities and limited observation access to the process. The detection nodes exchange post-processed information over a communication network. An average consensus protocol is employed to fuse the detection nodes' outputs to obtain an agreement over the local likelihood functions. The proposed approach conducts online hypothesis testing without a bank of estimators, reducing the computational complexity of the algorithm significantly. Numerical simulations validate the efficiency of the proposed algorithm.
Highlights
Advancements in the fields of communications, electronics, and sensory technology have facilitated the interaction of the physical world with embedded systems
This paper presents a distributed fault diagnosis algorithm for stochastic nonlinear systems that are supervised by communicating sensors
THEORY: PARTICLE FILTERING we provide a succinct description of the standard Particle Filtering (PF) algorithm for estimation, an alternative to the Kalman Filter (KF) suitable for nonlinear dynamical systems
Summary
Advancements in the fields of communications, electronics, and sensory technology have facilitated the interaction of the physical world with embedded systems. In [57] the authors presented the design of an algorithm for distributed FD suited for large-scale systems (102 state variables and higher) for which central processing of the information is computationally prohibitive and the decomposition of the prototypical high-order system to low-order subsystems is imperative. This previous work investigated how networks of information-exchanging FD filters can be designed for the low-order (or ‘‘local’’) partitions of the monolithic process.
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