An inertial platform fault location method based on redundancy technique

Abstract

The application provides an inertial platform fault positioning method based on redundancy technology, and is applied to a height measurement system of an inertial platform accelerometer height measurement and an altimeter combined measurement. When a sensor fails, low-pass filters of the accelerometer and the altimeter, instantaneous growth speed average data and average growth speed average data are designed, a mixed growth speed signal is solved, and an estimated signal is calculated according to average time stamp signals. Acceleration and altimeter estimated error data are further solved. Nonlinear filters are designed to obtain acceleration and altimeter estimated error nonlinear filter data. Finally, error threshold data are set to position fault positions and fault times.

Description

Technical Field

[0001] This invention relates to the field of inertial platform navigation and fault location, and more specifically, to an inertial platform fault location method based on redundancy technology. Background Technology

[0002] Redundancy technology refers to the use of redundant resources in a system and the effective management of these resources to improve the overall system reliability. In inertial navigation and altitude measurement, the altitude signal can generally be obtained by integrating the accelerometer of an inertial platform twice; however, accelerometer measurements are subject to noise interference and errors, resulting in drift and accumulated errors after integration. Altitude measurements using an altimeter, on the other hand, have random errors. Fusion of these two types of information can effectively form redundancy technology to improve the altitude information of the aircraft, with a reliability far exceeding that of single-device measurement schemes. However, once a fault occurs, it is necessary to locate the fault, determine the faulty measurement component, and identify the time of the fault. Traditional fault location methods require calculating residuals and setting expected and covariance data of the residuals based on empirical values. Obtaining this data is relatively complex, or it is difficult to accurately measure the expected and covariance data of the residuals for certain instrument faults. Based on the above background reasons, this invention improves a method for fault location judgment based on the filtering of estimation error data from multiple average growth rate predictions, which has good engineering application effects.

[0003] It should be noted that the information in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide an inertial platform fault location method based on redundancy technology, thereby overcoming the problem that the fault location judgment and solution of inertial platforms is too complicated due to the limitations and defects of related technologies.

[0005] According to one aspect of the present invention, a fault location method for an inertial platform based on redundancy technology is provided, comprising the following steps:

[0006] Step S10: For the altitude redundancy measurement system that uses a combination of inertial platform accelerometers and altimeters to measure the altitude of an aircraft based on redundancy technology, the output data of the platform's vertical accelerometer is selected. First, a low-pass filter is designed to filter out a small amount of glitch data during the measurement process to avoid misjudgment in fault diagnosis. After filtering, the rate data of the acceleration low-pass filter is obtained. Then, integration is performed to obtain the accelerometer low-pass data. Then, based on the acceleration data of the previous sampling period and the acceleration data at the current moment, the instantaneous acceleration growth rate data is calculated. Then, all the instantaneous acceleration growth rate data are accumulated to obtain the accumulated sum of instantaneous acceleration growth rates. Then, the average instantaneous acceleration growth rate data is calculated. Then, based on the acceleration data at the current moment and the initial acceleration data, the average acceleration growth rate data is calculated. Then, all the average acceleration growth rate data are accumulated to obtain the accumulated sum of average acceleration growth rates. Finally, the average acceleration growth rate data is calculated.

[0007] Step S20: The instantaneous acceleration growth rate average data and the average acceleration growth rate average data are superimposed and averaged to obtain the mixed acceleration growth rate signal; then, the platform vertical acceleration average data is obtained by accumulating and averaging the output data of the platform vertical accelerometer; and the average timestamp signal is obtained based on the usage period and data length; then, the acceleration estimation signal is calculated; then, the acceleration estimation error data is calculated based on the output data of the platform vertical accelerometer; then, a nonlinear filter is designed to calculate the acceleration estimation error derivative signal; and finally, integration is performed to obtain the acceleration estimation error nonlinear filter data.

[0008] In step S30, for the altitude redundancy measurement system that uses an inertial platform accelerometer and altimeter combination to measure the altitude of an aircraft based on redundancy technology, the altimeter output data is selected. First, an altimeter low-pass filter is designed to filter out a small amount of glitch data during the measurement process to avoid misjudgment in fault diagnosis. After filtering, the rate data of the altimeter low-pass filter is obtained. Then, integration is performed to obtain the altimeter low-pass data. Then, based on the altimeter data of the previous sampling period and the altimeter data at the current moment, the instantaneous growth rate data of the altimeter is calculated. Then, all the instantaneous growth rate data of the altimeter are accumulated to obtain the accumulated sum of the instantaneous growth rate data of the altimeter. Then, the average value data of the instantaneous growth rate of the altimeter is calculated. Then, based on the altimeter data at the current moment and the initial altimeter data, the average growth rate data of the altimeter is calculated. Then, all the average growth rate data of the altimeter are accumulated to obtain the accumulated sum of the average growth rate data of the altimeter. Finally, the average value data of the average growth rate of the altimeter is calculated.

[0009] In step S40, the average instantaneous growth rate data and the average growth rate data of the altimeter are superimposed and averaged to obtain the mixed growth rate signal of the altimeter; then, the altimeter output data are accumulated and averaged to obtain the average altimeter data; and the average timestamp signal is used; then the altimeter estimation signal is calculated; then the altimeter estimation error data is calculated based on the altimeter output data; then, a nonlinear height filter is designed to calculate the derivative signal of the altimeter estimation error; and finally, integration is performed to obtain the nonlinear filtered data of the altimeter estimation error.

[0010] Step S50: Based on the nonlinear filtering data of the acceleration estimation error, set the accelerometer error threshold parameter, and calculate the absolute value of the ratio of the difference between its data and the data of the previous three sampling periods to the data of the previous three sampling periods. If the absolute value of the data for three consecutive periods is greater than the accelerometer error threshold parameter, then the accelerometer of the inertial platform is identified as faulty. Based on the nonlinear filtering data of the altimeter estimation error, set the altimeter error threshold parameter, and calculate the absolute value of the ratio of the difference between its data and the data of the previous three sampling periods to the data of the previous three sampling periods. If the absolute value of the data for three consecutive periods is greater than the altimeter error threshold parameter, then the altimeter is identified as faulty.

[0011] In one exemplary embodiment of the present invention, the output data of the platform's vertical accelerometer is selected, a low-pass filter is designed, and the rate data of the accelerometer low-pass filter is obtained; integration is performed to obtain the accelerometer low-pass data; then, based on the acceleration data of the previous sampling period and the acceleration data at the current moment, the instantaneous acceleration growth rate data is calculated, and all the instantaneous acceleration growth rate data are accumulated to obtain the accumulated sum of instantaneous acceleration growth rates; then, the average instantaneous acceleration growth rate data is calculated; then, based on the acceleration data at the current moment and the initial acceleration data, the average acceleration growth rate data is calculated; then, all the average acceleration growth rate data are accumulated to obtain the accumulated sum of average acceleration growth rates, and then the average acceleration growth rate data is calculated, including:

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[0020] in For the output data of the platform's vertical accelerometer The value at time, The data acquisition period is a constant time parameter. is the time constant of the low-pass filter, and is a constant parameter; Rate data for the acceleration low-pass filter; For accelerometer low-pass data; For the instantaneous growth rate data of acceleration, This is the summation of the instantaneous growth rate of acceleration. This represents the average instantaneous growth rate of acceleration. The length of the data at the current moment is an integer; The length of the data up to the current time. This refers to the average growth rate of acceleration. The data is the sum of the average growth rate of the acceleration. This represents the average growth rate of the acceleration.

[0021] In one exemplary embodiment of the present invention, the instantaneous acceleration growth rate average data and the average acceleration growth rate average data are superimposed and averaged to obtain a mixed acceleration growth rate signal; then, the output data of the platform vertical accelerometer are accumulated and averaged to obtain the platform vertical acceleration average data; and the average timestamp signal is obtained based on the usage period and data length; then, the acceleration estimation signal is calculated; then, the acceleration estimation error data is calculated based on the output data of the platform vertical accelerometer; then, a nonlinear filter is designed to calculate the acceleration estimation error derivative signal; and finally, integration is performed to obtain the acceleration estimation error nonlinear filtered data, including:

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[0029] in This is an acceleration signal indicating the rate of mixed growth. This is the average vertical acceleration data for the platform; This is the average timestamp signal; For acceleration estimation signals; This is acceleration estimation error data; The derivative signal of the acceleration estimation error; , For constant parameter signals, The time constant signal of the nonlinear filter; Nonlinear filtering of acceleration estimation error data.

[0030] In one example embodiment of the present invention, the output data of the altimeter is selected, an altimeter low-pass filter is designed, and the rate data of the altimeter low-pass filter is obtained; then, integration is performed to obtain the altimeter low-pass data; then, based on the altimeter data of the previous sampling period and the altimeter data at the current time, the instantaneous growth rate data of the altimeter is calculated, and then all the instantaneous growth rate data of the altimeter are accumulated to obtain the accumulated sum of the instantaneous growth rates of the altimeter; then, the average instantaneous growth rate data of the altimeter is calculated; then, based on the altimeter data at the current time and the initial altimeter data, the average growth rate data of the altimeter is calculated; then, all the average growth rate data of the altimeter are accumulated to obtain the accumulated sum of the average growth rates of the altimeter, and then the average growth rate data of the altimeter is calculated, including:

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[0039] in For the output data of the altimeter in The value at time, is the time constant of the altimeter's low-pass filter, and is a constant parameter; This is the rate data for the low-pass filter of the altimeter; Low-pass data for altitude table; This is the instantaneous growth rate data of the altimeter. This is the summation of the instantaneous growth rate of the altimeter. This is the average instantaneous growth rate data of the altimeter. This is the average growth rate data for the altimeter; This is the sum of the average growth rate of the altitude data. This represents the average growth rate of the altitude.

[0040] In one exemplary embodiment of the present invention, the average instantaneous growth rate data of the altimeter is superimposed and averaged with the average growth rate data of the altimeter to obtain a mixed growth rate signal of the altimeter; then, the altimeter output data is accumulated and averaged to obtain the average altimeter data; and then, based on the average timestamp signal, the altimeter estimation signal is calculated; then, the altimeter estimation error data is calculated based on the altimeter output data; then, a nonlinear altimeter filter is designed to calculate the derivative signal of the altimeter estimation error; and finally, integration is performed to obtain the nonlinear filtered altimeter estimation error data, including:

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[0047] in This is a mixed growth rate signal from the altimeter; This is the average data from the altimeter; For acceleration estimation signals; This is the height gauge estimation error data; This is the derivative signal of the altimeter estimation error; , For constant parameter signals, The time constant signal of a highly nonlinear filter; Nonlinear filtering of altimeter estimation error data.

[0048] In one example embodiment of the present invention, based on the nonlinear filtering data of the acceleration estimation error, an accelerometer error threshold parameter is set, and the absolute value of the ratio of the difference between its data and the data of the previous three sampling periods to the data of the previous three sampling periods is calculated. If the absolute value of the data for three consecutive periods is greater than the accelerometer error threshold parameter, then an inertial platform accelerometer fault is identified. Based on the nonlinear filtering data of the altimeter estimation error, an altimeter error threshold parameter is set, and the absolute value of the ratio of the difference between its data and the data of the previous three sampling periods to the data of the previous three sampling periods is calculated. If the absolute value of the data for three consecutive periods is greater than the altimeter error threshold parameter, then an altimeter fault is identified, including:

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[0055] in This is the accelerometer error threshold parameter, which is a constant. This is the moment when the accelerometer malfunctioned; This is the altimeter error threshold parameter, which is a constant. This indicates the moment the altimeter malfunctioned.

[0056] Beneficial effects

[0057] This invention discloses a fault location method for inertial platforms based on redundancy technology, with the following four main innovations. First, it proposes using a low-pass filter to pre-filter the output data of the accelerometer and altimeter, thereby filtering out a small amount of glitch data during the measurement process and reducing the probability of misjudgment during fault diagnosis. Second, it obtains a mixed acceleration-altimeter acceleration signal by superimposing and averaging the instantaneous and average acceleration-altimeter growth rate values, and then uses this signal for prediction, greatly improving the accuracy of the prediction and reducing the sensitivity of the results to interference. Third, it uses the estimated acceleration and altimeter signals to solve for the estimation error data, further enhancing the basis for fault diagnosis; this method is very convenient to implement and has a relatively high fault location accuracy. Fourth, it designs a nonlinear filter to solve for the nonlinear filtered data of the accelerometer and altimeter estimation errors, further eliminating singular points in the data jumps caused by interference, effectively avoiding false alarms during fault location.

[0058] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0060] Figure 1 This is a flowchart of an inertial platform fault location method based on redundancy technology provided by the present invention;

[0061] Figure 2 This is the accelerometer low-pass data (unit: meters per second squared) provided by the method in the embodiments of the present invention.

[0062] Figure 3 The height data (unit: meters) is obtained by accelerometer measurement using the method provided in the embodiments of the present invention.

[0063] Figure 4 This is nonlinear filtered data of acceleration estimation error (unitless) provided by the method in the embodiments of the present invention.

[0064] Figure 5 This refers to the altimeter low-pass data (unit: meters) provided in the embodiments of the present invention.

[0065] Figure 6 This is the nonlinear filtering data for the height table estimation error provided in the embodiments of the present invention.

[0066] (No unit). Detailed Implementation

[0067] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make the invention more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention may be practiced with one or more of these specific details omitted, or other methods, components, apparatus, steps, etc., may be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the invention.

[0068] This invention provides a fault location method for inertial platforms based on redundancy technology. For a height measurement system using a combination of accelerometer and altimeter measurements on an inertial platform employing redundancy technology, when a sensor malfunctions, a low-pass filter is designed for both the accelerometer and altimeter, along with instantaneous and average growth rate data. This allows for the calculation of the mixed growth rate signal, and the estimation signal is then calculated based on the average timestamp signal. Further, the acceleration and altimeter estimation error data are calculated. A nonlinear filter is then designed to obtain nonlinear filtered data for the acceleration and altimeter estimation errors. Finally, an error threshold is set to locate the fault position and time.

[0069] The following will further explain and illustrate the inertial platform fault location method based on redundancy technology according to the present invention, with reference to the accompanying drawings. (Reference) Figure 1 As shown, this inertial platform fault location method based on redundancy technology may include the following steps:

[0070] Step S10: For the altitude redundancy measurement system that uses a combination of inertial platform accelerometers and altimeters to measure the altitude of an aircraft based on redundancy technology, the output data of the platform's vertical accelerometer is selected. First, a low-pass filter is designed to filter out a small amount of glitch data during the measurement process to avoid misjudgment in fault diagnosis. After filtering, the rate data of the acceleration low-pass filter is obtained. Then, integration is performed to obtain the accelerometer low-pass data. Then, based on the acceleration data of the previous sampling period and the acceleration data at the current moment, the instantaneous acceleration growth rate data is calculated. Then, all the instantaneous acceleration growth rate data are accumulated to obtain the accumulated sum of instantaneous acceleration growth rates. Then, the average instantaneous acceleration growth rate data is calculated. Then, based on the acceleration data at the current moment and the initial acceleration data, the average acceleration growth rate data is calculated. Then, all the average acceleration growth rate data are accumulated to obtain the accumulated sum of average acceleration growth rates. Finally, the average acceleration growth rate data is calculated.

[0071] Specifically, this can be broken down into the following three steps. First, select the output data from the platform's vertical accelerometer. Firstly, design a low-pass filter, and obtain the rate data from the low-pass filter after filtering. Then, perform integration to obtain the accelerometer low-pass data as follows:

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[0074] in For the output data of the platform's vertical accelerometer The value at time, The data acquisition period is a constant time parameter. is the time constant of the low-pass filter, and is a constant parameter; Rate data for the acceleration low-pass filter; This is low-pass data from the accelerometer.

[0075] The second step involves calculating the instantaneous acceleration growth rate based on the acceleration data from the previous sampling period and the current acceleration data. Then, all the instantaneous acceleration growth rate data are summed to obtain the cumulative instantaneous acceleration growth rate. Finally, the average instantaneous acceleration growth rate is calculated as follows:

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[0079] in For the instantaneous growth rate data of acceleration, This is the summation of the instantaneous growth rate of acceleration. This represents the average instantaneous growth rate of acceleration. The length of the data at the current moment is an integer; The length of the data up to the current time.

[0080] The third step is to calculate the average acceleration growth rate based on the current acceleration data and the initial acceleration data; then, sum all the average acceleration growth rate data to obtain the summed average acceleration growth rate data, and then calculate the average value of the average acceleration growth rate data as follows;

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[0084] in This refers to the average growth rate of acceleration. The data is the sum of the average growth rate of the acceleration. This represents the average growth rate of the acceleration.

[0085] Step S20: The instantaneous acceleration growth rate average data and the average acceleration growth rate average data are superimposed and averaged to obtain the mixed acceleration growth rate signal; then, the platform vertical acceleration average data is obtained by accumulating and averaging the output data of the platform vertical accelerometer; and the average timestamp signal is obtained based on the usage period and data length; then, the acceleration estimation signal is calculated; then, the acceleration estimation error data is calculated based on the output data of the platform vertical accelerometer; then, a nonlinear filter is designed to calculate the acceleration estimation error derivative signal; and finally, integration is performed to obtain the acceleration estimation error nonlinear filter data.

[0086] Specifically, this can be broken down into the following three steps. First, the average instantaneous acceleration growth rate data and the average acceleration growth rate data are superimposed and averaged to obtain a mixed acceleration growth rate signal. Then, the output data from the platform's vertical accelerometer are accumulated and averaged to obtain the platform's vertical acceleration average data. Finally, based on the sampling period and data length, the average timestamp signal is calculated as follows:

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[0090] in This is an acceleration signal indicating the rate of mixed growth. This is the average vertical acceleration data for the platform; This is the average timestamp signal.

[0091] The second step involves calculating the acceleration estimation signal based on the average vertical acceleration data and the average timestamp signal of the platform. Then, the acceleration estimation error data is calculated based on the output data of the platform's vertical accelerometer, as follows:

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[0094] in For acceleration estimation signals; This is the acceleration estimation error data.

[0095] The third step involves designing a nonlinear filter based on the acceleration estimation error data, calculating the derivative signal of the acceleration estimation error, and then integrating to obtain the nonlinear filtered data of the acceleration estimation error as follows:

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[0098] in The derivative signal of the acceleration estimation error; , For constant parameter signals, The time constant signal of the nonlinear filter; Nonlinear filtering of acceleration estimation error data.

[0099] In step S30, for the altitude redundancy measurement system that uses an inertial platform accelerometer and altimeter combination to measure the altitude of an aircraft based on redundancy technology, the altimeter output data is selected. First, an altimeter low-pass filter is designed to filter out a small amount of glitch data during the measurement process to avoid misjudgment in fault diagnosis. After filtering, the rate data of the altimeter low-pass filter is obtained. Then, integration is performed to obtain the altimeter low-pass data. Then, based on the altimeter data of the previous sampling period and the altimeter data at the current moment, the instantaneous growth rate data of the altimeter is calculated. Then, all the instantaneous growth rate data of the altimeter are accumulated to obtain the accumulated sum of the instantaneous growth rate data of the altimeter. Then, the average value data of the instantaneous growth rate of the altimeter is calculated. Then, based on the altimeter data at the current moment and the initial altimeter data, the average growth rate data of the altimeter is calculated. Then, all the average growth rate data of the altimeter are accumulated to obtain the accumulated sum of the average growth rate data of the altimeter. Finally, the average value data of the average growth rate of the altimeter is calculated.

[0100] Specifically, this can be broken down into the following three steps. First, based on the altimeter's output data, design an altimeter low-pass filter, and obtain the filter's rate data after filtering; then, perform integration to obtain the altimeter's low-pass data as follows:

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[0103] in For the output data of the altimeter in The value at time, is the time constant of the altimeter's low-pass filter, and is a constant parameter; This is the rate data for the low-pass filter of the altimeter; This is low-pass data for the altitude meter.

[0104] The second step involves calculating the instantaneous altitude increase rate based on the altimeter data from the previous sampling period and the current altimeter data. Then, all the instantaneous altitude increase rate data are summed to obtain the cumulative sum of the instantaneous altitude increase rates. Finally, the average instantaneous altitude increase rate is calculated as follows:

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[0108] in This is the instantaneous growth rate data of the altimeter. This is the summation of the instantaneous growth rate of the altimeter. This represents the average instantaneous growth rate of the altimeter.

[0109] The third step is to calculate the average altimeter growth rate based on the current altimeter data and the initial altimeter data. Then, the average altimeter growth rate is calculated by summing all the average altimeter growth rates, and finally, the average altimeter growth rate is calculated as follows:

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[0113] in This is the average growth rate data for the altimeter; This is the sum of the average growth rate of the altitude data. This represents the average growth rate of the altitude.

[0114] In step S40, the average instantaneous growth rate data and the average growth rate data of the altimeter are superimposed and averaged to obtain the mixed growth rate signal of the altimeter; then, the altimeter output data are accumulated and averaged to obtain the average altimeter data; and the average timestamp signal is used; then the altimeter estimation signal is calculated; then the altimeter estimation error data is calculated based on the altimeter output data; then, a nonlinear height filter is designed to calculate the derivative signal of the altimeter estimation error; and finally, integration is performed to obtain the nonlinear filtered data of the altimeter estimation error.

[0115] Specifically, this can be broken down into the following three steps. First, the average instantaneous growth rate of the altimeter is superimposed and averaged with the average growth rate of the altimeter to obtain a mixed altimeter growth rate signal; then, the altimeter output data is accumulated and averaged to obtain the average altimeter data as follows:

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[0118] in This is a mixed growth rate signal from the altimeter; This is the average data from the altimeter.

[0119] The second step involves accumulating and averaging the altimeter output data to obtain the altimeter average value; and then calculating the altimeter estimation signal based on the average timestamp signal; finally, the altimeter estimation error data is calculated based on the altimeter output data as follows:

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[0122] in For acceleration estimation signals; This is the height gauge estimation error data.

[0123] The third step is to design a nonlinear filter for altitude estimation and calculate the derivative signal of the altimeter estimation error; then, by integration, the nonlinear filtered data of the altimeter estimation error is obtained as follows:

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[0126] in This is the derivative signal of the altimeter estimation error; , For constant parameter signals, The time constant signal of a highly nonlinear filter; Nonlinear filtering of altimeter estimation error data.

[0127] Step S50: Based on the nonlinear filtering data of the acceleration estimation error, set the accelerometer error threshold parameter, and calculate the absolute value of the ratio of the difference between its data and the data of the previous three sampling periods to the data of the previous three sampling periods. If the absolute value of the data for three consecutive periods is greater than the accelerometer error threshold parameter, then the accelerometer of the inertial platform is identified as faulty. Based on the nonlinear filtering data of the altimeter estimation error, set the altimeter error threshold parameter, and calculate the absolute value of the ratio of the difference between its data and the data of the previous three sampling periods to the data of the previous three sampling periods. If the absolute value of the data for three consecutive periods is greater than the altimeter error threshold parameter, then the altimeter is identified as faulty.

[0128] Specifically, this can be broken down into the following two steps. First, based on the nonlinear filtering data of the acceleration estimation error, set the accelerometer error threshold parameter, and calculate the absolute value of the ratio of the difference between the data and the data from the previous three sampling periods to the data from the previous three sampling periods. If the absolute value of the data for three consecutive periods is greater than the accelerometer error threshold parameter, then the inertial platform accelerometer is identified as faulty as follows:

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[0132] in This is the accelerometer error threshold parameter, which is a constant. This is the moment when the accelerometer malfunctions.

[0133] The second step involves setting an altimeter error threshold parameter based on the aforementioned nonlinear filtering data of the altimeter estimation error. The absolute value of the ratio of the difference between this data and the data from the previous three sampling periods to the data from the previous three sampling periods is then calculated. If the absolute value of the data for three consecutive periods exceeds the altimeter error threshold parameter, the altimeter is identified as faulty as follows:

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[0137] in This is the altimeter error threshold parameter, which is a constant. This indicates the moment the altimeter malfunctioned.

[0138] Case Implementation and Computer Simulation Results Analysis

[0139] In step S10, when selecting The accelerometer low-pass data is obtained as follows: Figure 2 As shown; after two integrations, the height data measured by the accelerometer is obtained as follows. Figure 3 As shown.

[0140] In step S20, select , , The nonlinear filtering data of acceleration estimation error is obtained as follows: Figure 4 As shown.

[0141] In step S30, select The low-pass data obtained from the altitude meter is as follows: Figure 5 As shown.

[0142] In step S40, select , , The nonlinear filtering data for the altimeter estimation error is obtained as follows: Figure 6 As shown.

[0143] In step S50, set , ,Depend on Figure 4 It can be seen that the accelerometer malfunctioned in 7 seconds; from Figure 6It can be seen that the altimeter malfunctioned in 5 seconds.

[0144] Depend on Figure 4 It can be seen that, Figure 4 It can be seen that when the accelerometer malfunctions, the altimeter estimation error nonlinear filtering data will show a significant jump; moreover, the jump time is 7 seconds; similarly, from... Figure 6 It can be seen that the altimeter malfunction time is 5 seconds. It's worth noting that this experiment selected a jump fault. Even with a general fault in the instrument output, it can still be clearly distinguished from the nonlinear filtering data of the estimation error. The main principle is that the estimation error of the data before and after the fault will be significantly different, while under fault-free conditions, the estimation error is basically randomly distributed. Figure 6 The state shown in the first 5 seconds demonstrates that the inertial platform fault location technology provided by this invention has significant engineering and economic value.

Claims

1. A fault location method for an inertial platform based on redundancy technology, characterized by the following steps: Step S10: For the altitude redundancy measurement system that uses a combination of inertial platform accelerometer and altimeter to measure the altitude of an aircraft based on redundancy technology, select the output data of the platform vertical accelerometer, first design a low-pass filter to filter out a small amount of glitch data in the measurement process to avoid misjudgment of fault diagnosis, and obtain the rate data of the acceleration low-pass filter after filtering. Then, integration is performed to obtain the accelerometer low-pass data; then, based on the acceleration data of the previous sampling period and the acceleration data at the current moment, the instantaneous acceleration growth rate data is calculated; then, all the instantaneous acceleration growth rate data are accumulated to obtain the accumulated instantaneous acceleration growth rate data; then, the average instantaneous acceleration growth rate data is calculated; finally, based on the acceleration data at the current moment and the initial acceleration data, the average acceleration growth rate data is calculated. Then, all the average acceleration growth rate data are summed to obtain the sum of the average acceleration growth rate data, and then the average value of the average acceleration growth rate data is calculated. Step S20: The instantaneous acceleration growth rate average data and the average acceleration growth rate average data are superimposed and averaged to obtain the mixed acceleration growth rate signal; then the platform vertical acceleration average data is obtained by accumulating and averaging the output data of the platform vertical accelerometer; and the average timestamp signal is obtained according to the adoption period and data length; then the acceleration estimation signal is calculated. Then, the acceleration estimation error data is solved based on the output data of the platform's vertical accelerometer; Then, a nonlinear filter is designed to solve the derivative signal of the acceleration estimation error; then, integration is performed to obtain the nonlinear filtered data of the acceleration estimation error. In step S30, for the altitude redundancy measurement system that uses an inertial platform accelerometer and altimeter combination to measure the altitude of an aircraft based on redundancy technology, the altimeter output data is selected. First, an altimeter low-pass filter is designed to filter out a small amount of glitch data during the measurement process to avoid misjudgment in fault diagnosis. After filtering, the rate data of the altimeter low-pass filter is obtained. Then, integration is performed to obtain the altimeter low-pass data. Then, based on the altimeter data of the previous sampling period and the altimeter data at the current moment, the instantaneous growth rate of the altimeter is calculated. Then, all the instantaneous growth rate data of the altimeter are accumulated to obtain the accumulated sum of the instantaneous growth rates of the altimeter. Then, the average value of the instantaneous growth rate of the altimeter is calculated. Then, based on the altimeter data at the current moment and the initial altimeter data, the average growth rate of the altimeter is calculated. Then, all the average growth rate data of the altimeter are accumulated to obtain the accumulated sum of the average growth rates of the altimeter. Finally, the average value of the average growth rate of the altimeter is calculated as follows: In step S40, the average value of the instantaneous growth rate of the altimeter and the average value of the average growth rate of the altimeter are superimposed and averaged to obtain the mixed growth rate signal of the altimeter; then, the altimeter output data are accumulated and averaged to obtain the average altimeter data; and the average timestamp signal is used; then the altimeter estimation signal is calculated; then the altimeter estimation error data is calculated based on the altimeter output data; and finally, an altitude nonlinear filter is designed to calculate the derivative signal of the altimeter estimation error. Then, integration is performed to obtain the nonlinear filtered data of the altimeter estimation error; Step S50: Based on the nonlinear filtering data of the acceleration estimation error, set the accelerometer error threshold parameter, and calculate the absolute value of the ratio of the difference between its data and the data of the previous three sampling periods to the data of the previous three sampling periods. If the absolute value of the data for three consecutive periods is greater than the accelerometer error threshold parameter, then the accelerometer of the inertial platform is identified as faulty. Based on the nonlinear filtering data of the altimeter estimation error, set the altimeter error threshold parameter, and calculate the absolute value of the ratio of the difference between its data and the data of the previous three sampling periods to the data of the previous three sampling periods. If the absolute value of the data for three consecutive periods is greater than the altimeter error threshold parameter, then the altimeter is identified as faulty.

2. The inertial platform fault location method based on redundancy technology according to claim 1, characterized in that, The output data of the platform's vertical accelerometer is selected, and a low-pass filter is designed to obtain the rate data of the accelerometer low-pass filter. Then, integration is performed to obtain the accelerometer low-pass data; then, based on the acceleration data from the previous sampling period and the current acceleration data, the instantaneous acceleration growth rate is calculated, and all the instantaneous acceleration growth rate data are accumulated to obtain the accumulated sum of instantaneous acceleration growth rates; then, the average instantaneous acceleration growth rate is calculated; then, based on the current acceleration data and the initial acceleration data, the average acceleration growth rate is calculated; then, all the average acceleration growth rate data are accumulated to obtain the accumulated sum of average acceleration growth rates, and finally, the average acceleration growth rate is calculated as follows: ； ； ； ； ； ； ； ； in For the output data of the platform's vertical accelerometer The value at time, The data acquisition period is a constant time parameter. is the time constant of the low-pass filter, and is a constant parameter; Rate data for the acceleration low-pass filter; For accelerometer low-pass data; For the instantaneous growth rate data of acceleration, This is the summation of the instantaneous growth rate of acceleration. This represents the average instantaneous growth rate of acceleration. The length of the data at the current moment is an integer; The length of the data up to the current time. This refers to the average growth rate of acceleration. The data is the sum of the average growth rate of the acceleration. This represents the average growth rate of the acceleration.

3. The inertial platform fault location method based on redundancy technology according to claim 1, characterized in that, The instantaneous acceleration growth rate average data and the average acceleration growth rate average data are superimposed and averaged to obtain the mixed acceleration growth rate signal; then, the output data of the platform vertical accelerometer are accumulated and averaged to obtain the platform vertical acceleration average data; and the average timestamp signal is obtained according to the adoption period and data length; then, the acceleration estimation signal is calculated. Next, the acceleration estimation error data is calculated based on the output data of the platform's vertical accelerometer; then, a nonlinear filter is designed to solve for the derivative signal of the acceleration estimation error; finally, integration is performed to obtain the nonlinear filtered acceleration estimation error data as follows: ； ； ； ； ； ； ； in This is an acceleration signal indicating the rate of mixed growth. This is the average vertical acceleration data for the platform; This is the average timestamp signal; For acceleration estimation signals; This is acceleration estimation error data; The derivative signal of the acceleration estimation error; , For constant parameter signals, The time constant signal of the nonlinear filter; Nonlinear filtering of acceleration estimation error data.

4. The inertial platform fault location method based on redundancy technology according to claim 1, characterized in that, The altimeter output data is selected, and a low-pass filter for the altimeter is designed. The rate data of the low-pass filter is obtained after filtering. Then, integration is performed to obtain the altimeter low-pass data. Next, based on the altimeter data from the previous sampling period and the current altimeter data, the instantaneous altimeter growth rate is calculated. All instantaneous altimeter growth rate data are then accumulated to obtain the sum of the instantaneous altimeter growth rates. The average instantaneous altimeter growth rate is then calculated. Finally, based on the current altimeter data and the initial altimeter data, the average altimeter growth rate is calculated. All average altimeter growth rate data are then accumulated to obtain the sum of the average altimeter growth rates. The average altimeter growth rate is then calculated as follows: ； ； ； ； ； ； ； ； in For the output data of the altimeter in The value at time, is the time constant of the altimeter's low-pass filter, and is a constant parameter; This is the rate data for the low-pass filter of the altimeter; Low-pass data for altitude table; This is the instantaneous growth rate data of the altimeter. This is the summation of the instantaneous growth rate of the altimeter. This is the average instantaneous growth rate data of the altimeter. This is the average growth rate data for the altimeter; This is the sum of the average growth rate of the altitude data. This represents the average growth rate of the altitude.

5. The inertial platform fault location method based on redundancy technology according to claim 1, characterized in that, The altimeter's instantaneous growth rate average data and the average growth rate average data are superimposed and averaged to obtain a mixed altimeter growth rate signal. Then, the altimeter's output data is accumulated and averaged to obtain the average altimeter data. Based on the average timestamp signal, the altimeter estimation signal is calculated. The altimeter estimation error data is then calculated based on the altimeter's output data. A nonlinear altitude filter is designed to calculate the derivative signal of the altimeter estimation error. Integration yields the following nonlinear filtered altimeter estimation error data: ； ； ； ； ； ； in This is a mixed growth rate signal from the altimeter; This is the average data from the altimeter; For acceleration estimation signals; This is the height gauge estimation error data; This is the derivative signal of the altimeter estimation error; , For constant parameter signals, The time constant signal of a highly nonlinear filter; Nonlinear filtering of altimeter estimation error data.

6. The inertial platform fault location method based on redundancy technology according to claim 1, characterized in that, Based on the nonlinear filtering data of the acceleration estimation error, an accelerometer error threshold parameter is set. The absolute value of the ratio of the difference between the data and the data of the previous three sampling periods to the data of the previous three sampling periods is calculated. If the absolute value of the data for three consecutive periods is greater than the accelerometer error threshold parameter, the accelerometer is identified as faulty on the inertial platform. Based on the nonlinear filtering data of the altimeter estimation error, an altimeter error threshold parameter is set. The absolute value of the ratio of the difference between the data and the data of the previous three sampling periods to the data of the previous three sampling periods is calculated. If the absolute value of the data for three consecutive periods is greater than the altimeter error threshold parameter, the altimeter is identified as faulty as follows: ； ； ； ； ； ； in This is the accelerometer error threshold parameter, which is a constant. This is the moment when the accelerometer malfunctioned; This is the altimeter error threshold parameter, which is a constant. This indicates the moment the altimeter malfunctioned.

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