Aiming at the problem of unknown model accuracy,high sample representativeness requirements,and the loss of time series features in traditional model-based and data-driven fault diagnosis for intelligent ship sensors,...Aiming at the problem of unknown model accuracy,high sample representativeness requirements,and the loss of time series features in traditional model-based and data-driven fault diagnosis for intelligent ship sensors,this paper proposes a hybrid fault diagnosis method that combines both approaches.A nonlinear passive observer is constructed to generate system residual signals at first.Then,a convolutional neural network and a gated recurrent unit neural network are used to extract local and time series features,respectively.Moreover,the self-attention mechanism is introduced to further distinguish the important relationship between different time points of the signal.Finally,the fault diagnosis is realized through the classifier.Experimental results based on an intelligent ship model show that the diagnosis rate increased by 7.4%compared to models without an observer.Compared to traditional machine learning and deep learning methods,the proposed model achieves a diagnostic accuracy of over 99%,demonstrating superior performance.展开更多
In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. Fi...In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.展开更多
A robust polynomial observer is designed based on passive synchronization of a given class of fractional-order Colpitts(FOC)systems with mismatched uncertainties and disturbances.The primary objective of the proposed ...A robust polynomial observer is designed based on passive synchronization of a given class of fractional-order Colpitts(FOC)systems with mismatched uncertainties and disturbances.The primary objective of the proposed observer is to minimize the effects of unknown bounded disturbances on the estimation of errors.A more practicable output-feedback passive controller is proposed using an adaptive polynomial state observer.The distributed approach of a continuous frequency of the FOC is considered to analyze the stability of the observer.Then we derive some stringent conditions for the robust passive synchronization using Finsler’s lemma based on the fractional Lyapunov stability theory.It is shown that the proposed method not only guarantees the asymptotic stability of the controller but also allows the derived adaptation law to remove the uncertainties within the nonlinear plant’s dynamics.The entire system using passivity is implemented with details in PSpice to demonstrate the feasibility of the proposed control scheme.The results of this research are illustrated using computer simulations for the control problem of the fractional-order chaotic Colpitts system.The proposed approach depicts an efficient and systematic control procedure for a large class of nonlinear systems with the fractional derivative.展开更多
基金supported by the National Science Foundation of China under(Grant No.52201373)the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City(Grant No.2021CXLH0016)the Fundamental Research Funds for the Central Universities(Grant No.WUT.2024IVA018).
文摘Aiming at the problem of unknown model accuracy,high sample representativeness requirements,and the loss of time series features in traditional model-based and data-driven fault diagnosis for intelligent ship sensors,this paper proposes a hybrid fault diagnosis method that combines both approaches.A nonlinear passive observer is constructed to generate system residual signals at first.Then,a convolutional neural network and a gated recurrent unit neural network are used to extract local and time series features,respectively.Moreover,the self-attention mechanism is introduced to further distinguish the important relationship between different time points of the signal.Finally,the fault diagnosis is realized through the classifier.Experimental results based on an intelligent ship model show that the diagnosis rate increased by 7.4%compared to models without an observer.Compared to traditional machine learning and deep learning methods,the proposed model achieves a diagnostic accuracy of over 99%,demonstrating superior performance.
基金co-supported by Science and Technology on Avionics Integration Laboratory and the Aeronautical Science Foundation of China(No.20105584004)
文摘In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.
文摘A robust polynomial observer is designed based on passive synchronization of a given class of fractional-order Colpitts(FOC)systems with mismatched uncertainties and disturbances.The primary objective of the proposed observer is to minimize the effects of unknown bounded disturbances on the estimation of errors.A more practicable output-feedback passive controller is proposed using an adaptive polynomial state observer.The distributed approach of a continuous frequency of the FOC is considered to analyze the stability of the observer.Then we derive some stringent conditions for the robust passive synchronization using Finsler’s lemma based on the fractional Lyapunov stability theory.It is shown that the proposed method not only guarantees the asymptotic stability of the controller but also allows the derived adaptation law to remove the uncertainties within the nonlinear plant’s dynamics.The entire system using passivity is implemented with details in PSpice to demonstrate the feasibility of the proposed control scheme.The results of this research are illustrated using computer simulations for the control problem of the fractional-order chaotic Colpitts system.The proposed approach depicts an efficient and systematic control procedure for a large class of nonlinear systems with the fractional derivative.
