34 results about "Actuator fault detection" patented technology

A failure detecting device of a fault detection system includes a detection time calculator, which calculates a stroke time T3 required for a piston to travel between one end and another end of an actuator based on detection signals from a first sensor and a second sensor, a statistical processor that performs a predetermined statistical calculation with respect to the stroke time T3, and a fault response detector, which detects whether or not a fault of the actuator has occurred based on a processing result of the statistical processor.

The invention discloses a fault detection, diagnosis and performance evaluation method for a redundant aileron actuator. According to the method, fault detection, diagnosis, evaluation and real-time detection of the actuator are performed by means of an input order signal, an output displacement signal, a force motor current signal and aerodynamic loading data of the actuator; the fault detection is realized by a two-stage neural network, a first neural network is used as a system observer and is matched with actual output to acquire a residual error, and a second neural network outputs a self-adaptive threshold value synchronously; the fault detection is realized by the system observer and a force motor current observer; a time domain feature is extracted from a residual error signal and output to a self-organizing mapping neural network, and a minimum quantization error is acquired and normalized to a health degree, so that the actuator performance is evaluated; and on the basis of fault detection, the aerodynamic loading data is introduced, by means of a specific input order spectrum, the system observer and the neural network with the self-adaptive threshold value are trained, and the real-time fault detection is realized.

The invention discloses an aircraft actuator fault detection and diagnosis method based on a depth random forest algorithm. The method includes: firstly, summarizing the fault mode of an aircraft actuator; establishing an RBF neural network, and collecting the input and output data of the aircraft actuator under the normal working condition to serve as training data, and training the parameters inthe neural network model to obtain analysis redundancy of the monitored actuator; analyzing the residual data of the output signals by collecting the output of the actual actuator and the neural network model, and after the feature extraction, inputting the feature data set into a trained depth random forest multi-classifier for fault mode recognition. According to the invention, the complex nonlinear input and output relation of the aircraft actuator can be accurately simulated by the neural network, the fault mode is accurately recognized by a depth random forest strong classifier, and moreover, the method has the advantages of parallel calculation and high running speed, can be integrated into a flight management computer of an aircraft, realizes the online real-time monitoring, and the accuracy and the efficiency of fault diagnosis of the aircraft actuator are improved.

The invention proposes a fault detection method for an interval observer aiming at an unmanned aerial vehicle formation system. It belongs to the technical field of safety and reliability. First, an interval observer is established based on known bounded disturbances and relative output errors when the UAV formation is in a fault-free state. The residual obtained by outputting the estimated error is used to detect actuator faults. The difference from traditional fault detection lies in that this fault detection method based on interval observer does not need threshold generator and residual evaluation function. The invention mainly solves the fault detection problem of the unmanned aerial vehicle formation actuator, has low conservatism and strong adaptability, and can well meet the requirements of actuator fault detection.