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The finite-time fault detection problem is investigated for a class of nonlinear quantized large-scale networked systems with randomly occurring nonlinearities and faults. A nonlinear Markovian jump system model with partially unknown transition probabilities is employed to describe this Makov data assignment pattern. Based on obtained model, in finite-time stable framework, the desired mode-dependent fault detection filters are constructed such that the augmented error systems are finite-time stochastically stable with attenuation level. Especially, the sufficient conclusions provide quantitative relationship between network characteristic, quantization level, and finite-time system parameter with finite-time fault detection performance. The effectiveness of the proposed methods is demonstrated by simulation examples.

Finite-Time Fault Detection for Large-Scale Networked Systems with Randomly Occurring Nonlinearity and Fault