In this paper, the distributed state and fault estimation problem is discussed for a class of nonlinear time-varying systems with probabilistic quantizations and dynamic event-triggered mechanisms. To reduce resource consumption, a dynamic event-triggered strategy is exploited to schedule the data communication among sensor nodes. In addition, the measurement signals are quantized and then transmitted through the network, where the probabilistic quantizations are taken into consideration. Attention is focused on the problem of constructing a distributed estimator such that both the plant state and the fault signal are estimated simultaneously. By using the matrix difference equation method, certain upper bound on the estimation error covariance is first guaranteed and then minimized at each iteration by properly designing the estimator parameters. Subsequently, for the proposed distributed estimation algorithm, the estimator performance is analyzed and a sufficient condition is established to guarantee that the estimation error is exponentially bounded in mean-square sense. Finally, an illustrative example is provided to verify the usefulness of the developed estimation scheme.

本文讨论了一类具有概率量化和动态事件触发机制的非线性时变系统的分布式状态和故障估计问题。为了减少资源消耗，利用动态事件触发策略来调度传感器节点之间的数据通信. 此外，测量信号被量化，然后通过网络传输，其中考虑了概率量化。注意力集中在构建分布式估计器的问题上，以便同时估计工厂状态和故障信号。通过使用矩阵差分方程方法，首先保证估计误差协方差的一定上限，然后通过适当设计估计器参数在每次迭代中最小化。随后，对于所提出的分布式估计算法，分析了估计器的性能，并建立了一个充分条件来保证估计误差在均方意义上呈指数有界。最后，