PT Unknown
AU Zhang, J
TI Distributed Fault Detection for Large-Scale and Interconnected Systems
PD 11
PY 2024
DI 10.17185/duepublico/81272
LA en
AB To perform more flexible and sophisticated tasks and increase the quality of industrial products, large-scale and interconnected systems are becoming a pervasive component of modern industrial processes over the past decades. Motivated by the requirements of the safety and reliability issues of such systems, advanced distributed fault detection (FD) methods have attracted considerable attention. This dissertation is dedicated to the design issues of distributed FD in large-scale and interconnected systems equipped with sensor networks. In the first part of the work, the average consensus algorithm is applied for large-scale systems to realize the centralized optimal FD method in a distributed manner. This distributed realization shows a similar result as the original centralized one. In the second part, a combination of distributed observer and post-filter is applied for FD in large-scale and interconnected systems. In this framework, each local observer uses only its local and neighbours' information to estimate its local state. Thereafter, the estimation result passes through the post-filter to perform the corresponding FD. In the third part, a distributed approach for FD in interconnected systems under the influence of random noises and transmission time of information is introduced. In the latter method, prediction, filtering, and smoothing procedures are used to improve the accuracy of FD by means of reducing the variance matrix of the estimation error. Subsequently, the estimation result is used to generate residual signals and detect faults. Finally, benchmark studies are demonstrated to show the effectiveness of the proposed FD approaches.
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