CN214225212U

CN214225212U - Fault detection system for aircraft anti-stagnation brake sensor

Info

Publication number: CN214225212U
Application number: CN202022892659.6U
Authority: CN
Inventors: 游国栋; 董立佳
Original assignee: Tianjin University of Science and Technology
Current assignee: Tianjin University of Science and Technology
Priority date: 2020-12-07
Filing date: 2020-12-07
Publication date: 2021-09-17
Anticipated expiration: 2030-12-07

Abstract

The utility model belongs to the technical field of the aircraft brake and specifically relates to an aircraft prevents braking sensor fault detection system that delays, characterized by include fault detection module, logic processing module, aircraft direct current power supply system, prevent controller, cockpit indicating system that delays. The output end of the fault detection module is connected with the logic processing module and the anti-stagnation controller; the output end of the logic processing module is connected with the cockpit indication system and the anti-stagnation controller; the airplane direct current power supply system supplies power to the cockpit indication system and the anti-stagnation controller. The utility model provides a brake sensor fault detection system can in time detect out brake sensor's trouble, then carries out fault isolation to reduce the trouble of sensor and prevent stagnant braking system's influence to the high efficiency.

Description

Fault detection system for aircraft anti-stagnation brake sensor

Technical Field

The utility model relates to an aircraft prevents brake sensor fault detection system that delays belongs to aircraft brake field, mainly is a detecting system based on kalman method.

Background

The main purpose of using the anti-drag brake for the airplane is to fully utilize the maximum combination coefficient provided by the runway to ensure that the landed airplane can safely reduce the speed to the dynamic taxiing stage at the shortest distance. The modern aircraft has larger and larger take-off and landing weight and faster flight speed, so higher requirements on an anti-stagnation brake system are also provided. The anti-stagnation brake system of the airplane is a nonlinear system, has complex dynamic characteristics, has a plurality of factors influencing the performance of the airplane, and is very difficult to realize efficient control. The braking of the airplane is mainly the interaction process of the airplane tire and the ground, and the airplane brakes by means of the binding force between the airplane tire and the ground. Under the condition of certain airplane quality, the bonding coefficient mu is a factor influencing the bonding force. Combined with the coefficient mu and the influence of multiple factors on audience. The most important factor is the estimation of the wheel speed, and the wheel speed sensor has important significance for estimating the wheel speed, so that the wheel speed sensor needs to be subjected to fault detection.

SUMMERY OF THE UTILITY MODEL

The utility model discloses an overcome prior art not enough, improved detection method. Existing wheel speed sensor failure detection has good detection capability for single sensor failure, but is overwhelmed with failure detection constraints for multiple sensors. In order to solve the technical problem, the utility model provides a basic technical scheme does:

a fault detection system for an aircraft anti-stagnation brake sensor is characterized by comprising a fault detection module, a logic processing module, an aircraft direct-current power supply system, an anti-stagnation controller and a cockpit indication system. The output end of the fault detection module is connected with the logic processing module and the anti-stagnation controller; the output end of the logic processing module is connected with the cockpit indication system and the anti-stagnation controller; the airplane direct current power supply system supplies power to the cockpit indication system and the anti-stagnation controller.

The fault detection module mainly comprises a Kalman filter and a residual weighted least square calculator, wherein the Kalman filter estimates the measured value of the ith wheel speed sensor by using the arithmetic mean value of the rest 3 wheel speed sensors, and the ith wheel speed sensor is used as the detection object of the filter and outputs 4 estimated values. The residual weight calculator calculates the residual weight to obtain a value.

When the ith wheel speed sensor fails, the filtering residual sequence of the sensor must obey zero mean Gaussian distribution, the residual weighted least square of the filter must be smaller than the threshold of chi-square distribution with the degree of freedom (n-1) corresponding to the given false alarm rate, and the residual weighted least square of other filters must be larger than the threshold of chi-square distribution with the degree of freedom (n-1) corresponding to the given false alarm rate. At this time, it is determined that the ith sensor has failed.

The logic processing module is used for converting the fault signal into a logic signal and carrying out logic processing.

And the airplane direct current power supply system supplies power to the cockpit indication system and the anti-stagnation controller.

The anti-stagnation controller shields the signal of the failed wheel speed sensor processed by the logic processing module and carries out anti-stagnation control through the signal of the wheel speed sensor without failure.

Cockpit indication systems provide indications to drivers and maintenance personnel.

The utility model has the advantages that:

1. the fault of the wheel speed sensor is random, so that the possibility that a plurality of sensor faults exist simultaneously exists at a certain probability, and the fault detection system of the sensor can accurately detect the fault occurrence source. When the ith wheel speed sensor fails, the filtering residual sequence of the sensor must obey zero mean Gaussian distribution, the residual weighted least square of the filter must be smaller than the threshold of chi-square distribution with the degree of freedom (n-1) corresponding to the given false alarm rate, and the residual weighted least square of other filters must be larger than the threshold of chi-square distribution with the degree of freedom (n-1) corresponding to the given false alarm rate. At this time, it is determined that the ith sensor has failed.

2. The Kalman filtering method is adopted, the Kalman method occupies small memory, and the method is an optimal estimation theory of a classical linear system. The state of the dynamic system can be estimated from a series of data in the presence of measurement noise with known measurement variance. Because the method is convenient for realizing computer programming and can update and process the data acquired on site in real time, Kalman filtering is the most widely applied filtering method at present and is better applied to the fields of communication, navigation, guidance, control and the like.

Drawings

FIG. 1 is a control schematic block diagram of an aircraft anti-stick brake sensor fault detection system;

FIG. 2 is a simplified block diagram of an aircraft anti-stick brake sensor fault detection system;

Detailed Description

A fault detection system for an aircraft anti-stagnation brake sensor is characterized by comprising a fault detection module, a logic processing module, an aircraft direct-current power supply system, an anti-stagnation controller and a cockpit indication system. The output end of the fault detection module is connected with the logic processing module and the anti-stagnation controller, the output end of the logic processing module is connected with the cockpit indication system and the anti-stagnation controller, and the aircraft direct-current power supply system supplies power to the cockpit indication system and the anti-stagnation controller.

The fault detection module mainly comprises a Kalman filter and a residual weighted least square calculator, the Kalman filter is the measurement value of the ith wheel speed sensor estimated by the arithmetic mean value of the rest 3 wheel speed sensors, and the ith wheel speed sensor is used as the detection object of the filter and outputs 4 estimation values. The residual weight calculator calculates the residual weight to obtain a value.

When the ith wheel speed sensor fails, the filtering residual sequence of the sensor must obey zero mean Gaussian distribution, the residual weighted least square of the filter must be smaller than the threshold of chi-square distribution with the degree of freedom (n-1) corresponding to the given false alarm rate, and the residual weighted least square of other filters must be larger than the threshold of chi-square distribution with the degree of freedom (n-1) corresponding to the given false alarm rate. At this time, the ith sensor failure is determined.

The principle of the Kalman filter for fault diagnosis is that the filter is used for estimating the measurement value of a sensor without fault, a residual error is made with the actual measurement value of the sensor, and the fault is judged by comparing the residual error with a threshold value. The improved diagnosis method is that m filters are used for corresponding m sensors to be detected, each filter utilizes the arithmetic mean value of the measured values of the sensors except the ith sensor to estimate the ith sensor, then the estimated value and the measured value of the ith sensor are made into residual errors, a residual error sequence is bound to obey Gaussian distribution, weighted least square calculation is carried out on the residual error sequence of the ith sensor, and the calculated value of the weighted least square of the residual errors obeys the chi-square distribution with the degree of freedom of (n-1). Given the false alarm rate a, calculating the threshold value on chi-square distribution, the calculated value of the residual weighted least square of the faulty sensor is certainly smaller than the threshold value, and the calculated value of the residual weighted least square of the sensor without the fault is certainly larger than the threshold value, thereby well solving the problem of fault diagnosis of a large number of sensor faults and a short time.

Variations and modifications to the above-described embodiments may occur to those skilled in the art, in light of the above teachings and teachings. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the present invention should fall within the protection scope of the claims of the present invention. Furthermore, although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

1. A fault detection system of an aircraft anti-stagnation brake sensor is characterized by comprising a fault detection module, a logic processing module, an aircraft direct current power supply system, an anti-stagnation controller and a cockpit indication system, wherein the output end of the fault detection module is connected with the logic processing module and the anti-stagnation controller; the output end of the logic processing module is connected with the cockpit indication system and the anti-stagnation controller; the airplane direct current power supply system supplies power to the cockpit indication system and the anti-stagnation controller; a Kalman filter in the fault detection module carries out filtering estimation on the measured value of the ith wheel speed sensor by using the arithmetic mean value of the rest 3 wheel speed sensors, 4 estimated values are output, a residual error weighting calculator in the fault detection module carries out residual error weighting calculation on a least square value, a residual error sequence is bound to Gaussian distribution, the residual error weighting least square value is compared with a threshold value of a false alarm rate a of chi-square distribution with the degree of freedom (n-1), whether the sensor is in fault or not is judged, wherein i is the other one of the 4 wheel speed sensors which is not subjected to 3 arithmetic mean, and n is the number 4 of the wheel speed sensors.