9 results about "Residual generator" patented technology

The invention discloses a fault detection method for a ship power condenser system based on multi-scale feature fusion, relates to the fault detection method for the ship power condenser system based on the multi-scale feature fusion, and belongs to the field of fault diagnosis and safety monitoring of ship power systems. The objective of the invention is to solve the problems of insufficient detection precision and high false alarm rate of a traditional fault diagnosis method. The method comprises the following steps: step 1, acquiring input and output information of a condenser system; 2, constructing a multi-scale feature extraction model; 3, obtaining a trained multi-scale feature extraction model and parameters; 4, inputting online data of the condenser system into the trained multi-scale feature extraction model, and outputting predicted values of the liquid level and the vacuum degree of the condenser by the trained multi-scale feature extraction model; the residual generator obtains an online prediction error signal based on the prediction value; and step 5, performing fault diagnosis based on the online prediction error.

This invention discloses a distributed fault detection method for large-scale industrial processes based on subspace identification. A fault detection unit is established on each subsystem of the large-scale system. Local residual generators are directly identified using process data from local and neighboring industrial subsystems, and the residual covariance matrix under healthy conditions is estimated. Actuator faults in neighboring industrial subsystems are decoupled, enabling the detection of faults in both local and neighboring industrial subsystems. Using this invention, a fault detection unit can be constructed within each industrial subsystem using only local and neighboring data, achieving high-performance fault detection for both local and neighboring industrial subsystems. This method boasts high detection rates, low false alarm rates, and low false negative rates, reducing computational and data transmission burdens and making it suitable for dynamic industrial processes.

The application discloses a satellite attitude control system event-triggered STF fault detection method, and belongs to the technical field of detection, which comprises the following steps: establishing a fault model of a satellite attitude control system; introducing an event-triggered mechanism; adopting an STF method to design an event-triggered fault detection filter; obtaining an estimation error dynamic equation and a residual signal through linearization processing of the fault detection filter, realizing complete decoupling of the residual signal and the event-triggered error; converting a design problem of a residual generator into solving a gain matrix of the fault detection filter; calculating a residual evaluation function and comparing the residual evaluation function with a threshold value to judge whether a fault occurs or not. The application utilizes the STF method to design the fault detection filter, can enable the system to keep the tracking ability to a sudden state when reaching a stable state, adopts a brand-new event-triggered framework, realizes complete decoupling of the residual signal and the event-triggered error, and effectively improves the resource utilization rate under the premise of guaranteeing the system performance.

The application relates to a reversible left manifold-based closed-loop system fault diagnosis method and system, which comprises the following steps: step 1, constructing a reversible left manifold according to a reversible neural network, and parameterizing a nonlinear closed-loop system; step 2, constructing a reversible residual generator of a nonlinear closed-loop control system according to the reversible neural network; and step 3, obtaining online data of the nonlinear closed-loop system, and performing online fault diagnosis based on a test statistic of the residual generator and a set diagnosis threshold. Compared with the prior art, the application has the advantages of low memory cost requirement and high fault diagnosis precision.

The application discloses a dynamic system data-driven fault detection method based on distributed typical correlation analysis, and comprises the following steps: in an offline process, historical data collected by sensors in each subsystem under normal working conditions is used to construct a centralized CCA residual generator; according to the network topology structure among the subsystems in the dynamic system, the weight coefficients of information transmission among the subsystems are determined based on an average consistency algorithm, an iteration matrix for solving CCA parameters of each subsystem is constructed, and the centralized CCA residual generator is converted into a distributed CCA residual generator; the CCA parameters of each subsystem are solved; in an online process, each subsystem uses the solved CCA parameters to construct a CCA-based residual generator, and the residual signals of each subsystem are distributedly fused based on the average consistency algorithm; finally, a control statistic is designed, a threshold is determined, and distributed optimal fault detection based on data driving in the dynamic system is carried out.

A kind of ship power condenser system fault detection method based on multiscale feature fusion, the present application relates to ship power condenser system fault detection method based on multiscale feature fusion, belongs to ship power system fault diagnosis and safety monitoring field.The present application aims to solve the problem of insufficient detection accuracy and high false alarm rate of traditional fault diagnosis method.Process is as follows:step one, obtain the input and output information of condenser system;Step two, construct multiscale feature extraction model;Step three, obtain trained multiscale feature extraction model and parameters;Step four, input the online data of condenser system into trained multiscale feature extraction model, and trained multiscale feature extraction model outputs the predicted value of condenser liquid level and vacuum degree;Residual generator obtains online prediction error signal based on predicted value;Step five, fault diagnosis based on online prediction error.

The invention provides a double-layer fault diagnosis method and system for an industrial interconnection system, and belongs to the technical field of fault diagnosis of the industrial interconnection system. The method comprises the following steps: firstly, based on a state space model of each subsystem in the industrial interconnection system, deducing input and output data of each subsystem of the industrial interconnection system, and realizing distributed identification of a subspace parameter matrix by using the input and output data of each subsystem so as to give full play to complementary advantages of mechanism modeling and data driving. Secondly, a Granger causality analysis method is used for calculating the coupling connection relation between the subsystems, and distributed optimization of a subspace parameter matrix is achieved in combination with an alternating direction multiplier method; and finally, distributed residual error generator design and residual error evaluation are carried out on the subsystem side, distributed fault detection of the industrial interconnection system is realized, residual error signals of the subsystems are transmitted to a centralized processing unit, and when a fault is detected, a fault source subsystem is found by calculating a comprehensive fault score of each subsystem.

The application discloses a neural network-based maglev train suspension system anti-disturbance control method and device, equipment and medium, and relates to the field of transportation, which comprises the following steps: determining a target space model, a target controlled object and a corresponding target nominal controller based on a target state variable and a suspension equilibrium point of a target maglev train, constructing a target loop through Youla parameterization and determining a corresponding target relationship matrix, and determining a corresponding target optimization function based on the target loop; training a parameter prediction model based on an original residual signal output by a residual generator in the target loop, the target relationship matrix and the target optimization function to obtain a target parameter prediction model, determining a prediction control parameter of a dynamic feedback gain based on a real-time residual signal by using the target parameter prediction model, and performing anti-disturbance control on the target maglev train based on the prediction control parameter. The application improves the wideband anti-disturbance capability of the maglev train control system.

The invention discloses a fault detection and detectability analysis method for a high-speed train traction motor, and belongs to the technical field of fault diagnos.The method comprises the steps that firstly, a T-S fuzzy dynamic modeling technology is adopted to construct a traction motor nominal system, and then a stable kernel characterization / stable image characterization and orthogonal projection residual error generator is established; effective detection of multiplicative faults is achieved, and robustness of control input changes is improved in combination with an adaptive threshold strategy. And then, further based on a traction motor fault system influenced by an intrinsic multiplicative fault under closed-loop control, analyzing residual dynamic characteristics by using time-varying K-Gap measurement, and proposing a detectability quantitative evaluation index, thereby forming a complete technical system from a detection method to detectability evaluation. According to the method, efficient multiplicative fault detection is achieved, theoretical analysis and quantitative basis is provided for detectable performance, and the method has remarkable advantages in the aspects of calculation efficiency and engineering applicability.