8 results about "Failure detection and isolation" patented technology

The invention relates to the technical field of inverter control, and provides an MBD-based reinforcement learning inverter control algorithm optimization method, which comprises the steps of S1, establishing a state space model of an inverter, designing a baseline controller of double-loop control, and constructing an MBD simulation platform containing multiple load models; s2, defining a state space of the 12-dimensional state vector, designing a continuous action space, and constructing an adaptive multi-target reward function; s3, training a control parameter optimization strategy by adopting an Actor-Critic algorithm architecture in combination with an experience playback mechanism and a hybrid exploration strategy; s4, establishing a security constraint mechanism of three-layer security protection, designing a fault detection and isolation strategy, realizing a closed-loop online learning strategy of pre-training-transfer learning-online fine tuning, and meanwhile, adopting a self-adaptive updating mechanism; and S5, verifying the optimization effect of the control algorithm through quantitative index evaluation, an experimental verification scheme and a real-time performance requirement test. And the upgrade of inverter control from model driving to data and model cooperative driving is realized.

The invention discloses an intelligent fault diagnosis method and device for a complex aerospace system based on correlation modeling, and relates to the technical field of aerospace system fault diagnosis, and the method comprises the steps: constructing a testability model of the aerospace system; the distinguishing capability of different faults is simplified, and test points are optimized; automatically generating a fault diagnosis strategy based on the optimized testability model; performing simulation evaluation on the fault diagnosis strategy, and counting a fault detection rate and a fault isolation rate; and performing iterative optimization by judging whether the fault detection rate and the fault isolation rate meet preset requirements or not. According to the method, the technical problems of low diagnosis efficiency and insufficient fault detection and isolation accuracy caused by dependence on artificial experience and incomplete static fault tree coverage of complex spaceflight system fault diagnosis in the prior art are solved, intelligentization and precision of complex spaceflight system fault diagnosis are realized, and the fault diagnosis efficiency is improved. And the fault detection rate and the isolation rate are improved.

The invention discloses a fault comprehensive diagnosis logic generation method considering multiple working conditions, and belongs to the field of fault diagnosis. The method specifically comprises the following steps: for equipment S with a fault to be detected, extracting a fault mode of each key component and obtaining associated state monitoring parameters; then, dividing the operation state of the equipment S into a plurality of typical working condition sets; for each working condition, setting a fault criterion according to a fault mode and a state monitoring parameter; thirdly, further decomposing the complex fault criterion into a plurality of mutually independent test units; when the test unit triggers an alarm, it is indicated that the equipment S breaks down, and detection is carried out. And finally, constructing fault isolation logic by using the test number of the test unit and combining the mapped fault criterion, and matching the fault isolation logic with the characteristic mode of each fault mode so as to judge the specific fault and realize fault isolation. And finally generating comprehensive diagnosis logic of the fault. According to the invention, fault detection and isolation of a complex system under various operation conditions are realized.

This application discloses a method, device, and medium for fault detection and isolation of complex spacecraft equipment, relating to the field of fault identification. The method includes: inputting the current operating status data of the complex spacecraft equipment into an operating status prediction model for prediction to obtain the future operating status data distribution; the operating status prediction model is obtained by training a variational Koopman anomaly detector using a time-series training sample set of the complex spacecraft equipment; and calculating the prediction error matrix and the corresponding T based on the current operating status data and the future operating status data distribution. 2 The system calculates the values ​​and SPE statistics, and then determines the operating state of complex spacecraft equipment based on preset limits. When the operating state indicates a fault, it calculates the deviation score of each operating state variable and isolates the fault to determine the fault variables of the operating state. This application realizes effective fault detection and fault variable screening for complex spacecraft equipment.

The invention relates to the technical field of electrical variable test and analysis, in particular to a distribution box fault diagnosis and prediction method based on an AI algorithm. In order to solve the limitation of a traditional power distribution network fault diagnosis method in a complex and changeable environment, the method combines high-frequency signal processing and an artificial intelligence algorithm, and an efficient fault detection and isolation scheme is provided. The method comprises the following steps: 1, high-frequency signal injection and collection: injecting a preset high-frequency signal at a key node of a power distribution network and collecting a reflection signal; 2, data feature extraction and transformation: performing feature extraction on the signals by using short-time Fourier transformation and wavelet transformation; 3, fault classification and diagnosis: identifying fault types and predicting trends through a convolutional neural network and a long short-term memory network; 4, graph theory path simulation analysis: optimizing a fault isolation path through a graph theory model; the simulation module is used for simulating and optimizing system response and accuracy through a computer. And when the simulation of the system is insufficient, the performance is optimized by further adjusting the AI algorithm and signal processing. According to the solution, the accuracy and adaptability of fault processing are improved, and stable operation of the power distribution system is ensured.

The application provides a horizontal flight direction fault-tolerant control and reconstruction method for an aircraft under rudder surface failure, designs a horizontal flight direction fault-tolerant controller, calculates an optimal control law through a nominal model of the aircraft and a rudder surface failure model, and stabilizes the aircraft under external disturbance. Then, a quadruple neural network is used to estimate the optimal control law for all possible failure degrees. When the rudder surface failure occurs, the aircraft can be stably operated. The application provides a horizontal flight direction fault-tolerant control and reconstruction system for an aircraft under rudder surface failure, which is not limited in generality, based on a fault detection and isolation module, a switching mechanism and a flight control system, introduces a horizontal flight direction fault-tolerant controller, and stabilizes the aircraft under external disturbance.

The application discloses an airborne multi-source fusion navigation dynamic integrity monitoring and alarming method and device and a medium, relates to the field of multi-source fusion navigation dynamic integrity monitoring and alarming, and comprises the following steps: firstly, calculating the separation combination of a multi-source fusion navigation system under a plurality of fault hypotheses; secondly, performing navigation source fault detection and isolation by using a PCA multivariate statistical analysis method according to KF-LS form solutions of the separation combinations; then, verifying the fault navigation source recovery by using a w-detection method for the isolated navigation source, and dynamically regulating and controlling effective navigation source information; finally, calculating the PCA principal component contribution rate under each fault hypothesis to be monitored based on an integrity risk dynamic allocation criterion, dynamically calculating the protection level, and realizing integrity monitoring and alarming. The application can realize dynamic integrity monitoring of airborne multi-source fusion navigation, and can generate an alarm prompt by calculating the system protection level in real time.

This invention discloses a multi-timescale fault detection and isolation method for integrated navigation systems. Targeting integrated navigation systems employing a federated filtering structure, the method acquires state estimation information for each local filtering branch, constructs a residual quantity reflecting changes in consistency between branches, and normalizes this residual by combining it with corresponding uncertainty information to obtain a standardized deviation. Normal fluctuations are suppressed through soft thresholding, and non-negative effective deviations are extracted. Based on the effective deviations, fast-timescale and slow-timescale statistics are constructed respectively, and the impact of operational condition changes on the detection results is mitigated through baseline updates and platform removal processing. Finally, fault detection and isolation conclusions are output based on the joint decision results of the fast and slow-timescale statistics. This invention can balance rapid response to abrupt faults with cumulative perception of gradual faults, and is applicable to local branch fault detection and isolation in multi-source integrated navigation systems.