A data-driven fault isolation and estimation approach for unknown linear systems. (April 2023) - Ma, Zhen-Lei; Li, Xiao-Jian
Abstract: This paper considers the data-driven fault isolation and estimation problem for linear time-invariant systems with unknown dynamic matrices and multiple actuator faults. In most of existing fault isolation methods, how to accurately identify the types of faults has not been solved well when the system matrices are unknown. To deal with this problem, a neural network-based fault isolation method is proposed by analyzing and extracting features of different fault models in terms of constructing sparse vectors and function libraries using the available input–output data. Then, a fault estimator is designed to estimate the fault signals within the data-driven framework, where its parameters are computed by the system's Markov parameters and the identified types of faults. Finally, two examples are used to verify the advantages and effectiveness of the proposed fault isolation and estimation approach. Highlights: A data-driven fault diagnosis approach is developed for unknown linear systems. A neural network-based fault isolation method is used to isolate actuator faults. The faults features of different fault models are analyzed and extracted. A fault estimator is designed for both single and simultaneously occurring faults. H 2 index is used to attenuate the effects of noises on fault estimation error.