Detection method for judging fault of inertial measurement unit

Abstract

The invention belongs to the technical field of inertial navigation, and particularly relates to a detection method for judging a fault of an inertial measurement unit, which comprises the following steps: acquiring fiber-optic gyroscope data and accelerometer data of the inertial measurement unit; analyzing and judging the data of each axis fiber-optic gyroscope, counting a numerical value of which the output change of adjacent sampling intervals is 0, and if a preset first condition is met, judging that the axis fiber-optic gyroscope has a fault; if the X-axis fiber-optic gyroscope, the Y-axis fiber-optic gyroscope and the Z-axis fiber-optic gyroscope all have faults, judging that the gyroscope main circuit board has Analyzing and judging the data of each axis accelerometer, counting the duration when the output value of each axis accelerometer is 0, and if the preset duration is reached, judging that the axis accelerometer has a fault; and if the X-axis accelerometer, the Y-axis accelerometer and the Z-axis accelerometer all fail, the I / F conversion module is judged to fail. According to the method, accurate identification and rapid positioning of various potential and complex fault modes can be realized through deep analysis and comprehensive diagnosis of a single IMU component under the condition of not depending on an additional inertial navigation inertial device.

Description

Technical Field

[0001] The invention belongs to the technical field of inertial navigation, and in particular relates to a detection method for judging a fault of an inertial measurement unit. Background Art

[0002] When discussing the stability and reliability of inertial navigation systems, the inertial measurement unit (IMU), as its core component, plays a vital role. IMU is a device that integrates precision technology and is specifically used to measure the attitude angle (or angular rate) and acceleration of objects (such as smart cars, ships, drones, etc.) in three-axis directions. This information is essential for achieving precise navigation. Specifically, the IMU is equipped with three fiber optic gyroscopes and three accelerometers. These sensors can sensitively capture the angular velocity changes and linear motion information of the carrier relative to the inertial space, providing indispensable raw data for subsequent navigation calculations.

[0003] However, in practical applications, IMU failure often becomes a key factor affecting the normal operation and accuracy of the inertial navigation system. Once the IMU fails, the performance of the inertial navigation system will be greatly reduced, and problems such as abnormal operation and accuracy deviation from the preset range may occur, which in turn affects the stability and reliability of the entire navigation system.

[0004] Faced with this challenge, the industry has actively tried an effective monitoring method: real-time monitoring of the two sets of inertial navigation inertial devices and their key signals. The core of this method is to preliminarily determine whether there is an abnormality in the IMU by continuously observing whether the gyroscope, accelerometer and temperature signals fluctuate within the normal range. Specifically, abnormal signals from gyroscopes and accelerometers may indicate problems such as mechanical wear, circuit failure or signal interference inside the sensor, while abnormal temperature signals may indicate failure of the thermal management system or degradation of sensor performance due to overheating.

[0005] This method does improve the sensitivity of fault detection to a certain extent, allowing some obvious faults to be discovered and handled in a timely manner. However, with the in-depth application of technology and the accumulation of practical experience, its limitations are becoming increasingly prominent.

[0006] First, this method mainly relies on direct monitoring of gyroscope and accelerometer signals, which limits the comprehensiveness of its fault detection to a certain extent. Because the failure of IMU may not only be limited to the sensor itself, but may also involve multiple levels such as signal processing circuits and data transmission channels. For these potential or complex failure modes, it is often difficult to capture enough abnormal information through simple signal comparison, making it impossible to accurately judge the fault.

[0007] Secondly, this method is also insufficient in fault location. Due to the lack of in-depth understanding of the interaction between the components inside the IMU, when multiple sensors are abnormal at the same time, this method often has difficulty in accurately determining which component or link has a problem. This not only increases the difficulty of troubleshooting, but may also lead to misdiagnosis or missed diagnosis, which in turn affects the stability and reliability of the entire navigation system.

[0008] Therefore, the industry has begun to realize that it is far from enough to rely solely on direct monitoring of gyroscope and accelerometer signals. In order to accurately identify and quickly locate IMU faults, a more comprehensive and in-depth detection method must be adopted. This method should be able to comprehensively consider the interaction between the components inside the IMU and the various abnormal manifestations that may be caused by faults, so as to accurately capture and quickly respond to potential and complex fault modes.

[0009] In summary, how to accurately identify and quickly locate various potential and complex fault modes through in-depth analysis and comprehensive diagnosis of a single IMU component without relying on additional inertial navigation devices has become a technical problem that needs to be solved urgently. Summary of the invention

[0010] In view of the above-mentioned deficiencies in the prior art, the present invention provides a detection method for determining inertial measurement unit faults, which can accurately identify and quickly locate various potential and complex fault modes through in-depth analysis and comprehensive diagnosis of a single IMU component without relying on additional inertial navigation devices.

[0011] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0012] A method for determining an inertial measurement unit fault comprises the following steps:

[0013] Obtain fiber optic gyro data and accelerometer data from the inertial measurement unit;

[0014] Analyze the data of the gyroscope of each axis; analyze and judge the data of the fiber optic gyroscope of each axis, and count the values ​​of the output changes of adjacent sampling intervals to 0. If the value of the output change to 0 meets the preset first condition, it is determined that the fiber optic gyroscope of the axis has a fault;

[0015] If the X, Y, and Z three-axis fiber optic gyroscopes are all faulty, it is determined that the gyroscope main circuit board is faulty;

[0016] Parse the data of each accelerometer; analyze and judge the data of the accelerometer of each axis, and count the duration of its output value being 0. If the duration reaches the preset duration, it is determined that the accelerometer of the axis is faulty;

[0017] If the X, Y, and Z axis accelerometers are all faulty, it is determined that the I / F conversion module is faulty;

[0018] The faults identified in the above steps are counted and fault warnings are issued.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. This method first obtains the fiber optic gyro data and accelerometer data of the IMU, and parses the data of each axis separately. Through in-depth analysis of the data, it is possible to capture subtle changes in the fiber optic gyro and accelerometer under normal working conditions, as well as abnormal performance under fault conditions. For the fiber optic gyroscope, this method counts the values ​​of the interval gyro output change to 0, and compares it with the preset first condition, so as to accurately identify whether there is a fault. Similarly, for the accelerometer, the method counts the duration of the output value of 0, and compares it with the preset duration to determine whether there is a fault. This method based on data statistical analysis improves the accuracy of fault identification.

[0021] 2. This method can quickly locate faults. Once a fault is identified, the method can quickly locate the specific hardware component. For example, if the X, Y, and Z three-axis fiber optic gyroscopes are all faulty, it is judged that the gyroscope main circuit board is faulty; if the three-axis accelerometers are all faulty, it is judged that the I / F conversion module is faulty; the fiber optic gyroscope of a certain axis is faulty, and the accelerometer of a certain axis is faulty. This ability to quickly locate faults helps to take appropriate maintenance or replacement measures in a timely manner to avoid the fault from causing greater impact on the system.

[0022] 3. This method does not rely on additional devices. The entire detection process is completely dependent on the IMU's own data, without the need for additional inertial navigation inertial device support. This not only reduces the cost of detection, but also improves the flexibility and convenience of detection.

[0023] In summary, this method can accurately identify and quickly locate various potential and complex fault modes through in-depth analysis and comprehensive diagnosis of a single IMU component without relying on additional inertial navigation devices.

[0024] Preferably, when analyzing and judging the data of the fiber optic gyroscope of each axis, if its output exceeds a preset first value, it is also determined that the fiber optic gyroscope of this axis has a fault.

[0025] Such a setting, through the newly added judgment conditions, enables the method to cope with more types of fault modes, such as abnormal sensitivity of the fiber optic gyroscope, zero drift, excessive noise, etc., which may cause the output value to exceed the preset first value. By comprehensively judging the output value of 0 and the output exceeding the preset first value, the method can more comprehensively identify the potential faults of the fiber optic gyroscope and improve the reliability of fault detection. In addition, the introduction of this judgment condition does not increase additional hardware dependence or complex calculations, and maintains the simplicity and efficiency of the detection process. Therefore, this method not only improves the accuracy of fault detection, but also maintains good practicality and economy.

[0026] By introducing the preset first value as another criterion for determining whether the fiber optic gyroscope is faulty, the method further enhances the comprehensiveness and accuracy of fault detection, and provides a more reliable and efficient means for fiber optic gyroscope fault detection.

[0027] Preferably, the preset first value is ±800000000.

[0028] In this way, setting ±800000000 as the threshold can take into account the normal working range and performance parameters of the fiber optic gyroscope, ensuring that under normal circumstances, the output data of the fiber optic gyroscope will not exceed this range. In addition, normal fluctuations or noise interference (such as environmental noise, temperature changes, mechanical vibrations, etc.) can be filtered out to a certain extent to avoid false alarms caused by these factors.

[0029] Preferably, the preset first condition is that, within a preset comparison time, the proportion of the output change value of the fiber optic gyroscope of the axis being 0 is greater than a preset ratio.

[0030] This setting, by setting a comparison time and a preset ratio, can more sensitively capture the abnormal performance of the fiber optic gyroscope in a faulty state. Even if the output data of the fiber optic gyroscope does not fluctuate much within the overall range, as long as the proportion of its output change value of 0 within a specific time exceeds the preset ratio, the system can issue a fault warning in time, avoiding the situation of missing detection due to small data fluctuations.

[0031] In addition, by setting a reasonable comparison time and preset ratio, this method can still maintain the accuracy and stability of fiber optic gyroscope fault detection in complex and changeable application environments (such as environmental noise, temperature changes, mechanical vibrations, etc.). In addition, the setting of the judgment condition fully considers the normal working characteristics of the fiber optic gyroscope and the volatility of the output data, avoiding false alarms caused by normal data fluctuations. At the same time, by setting a reasonable preset ratio, it can also reduce the underreporting caused by the unclear fault performance to a certain extent.

[0032] Preferably, the preset comparison time is greater than or equal to 8 seconds; and the preset ratio is greater than or equal to 85%.

[0033] Such a setting, 1. Long-term observation reduces accidental errors. Setting the comparison time to be greater than or equal to 8 seconds means that the system will observe and analyze the output data of the fiber optic gyroscope for a longer period of time. The advantage of doing this is that it can effectively reduce misjudgments caused by short-term or accidental external interference (such as short-term mechanical vibration, electromagnetic interference, etc.), making fault detection more robust and reliable. 2. High proportion requirements ensure fault significance. The preset ratio is greater than or equal to 85%, which means that during the comparison period, the situation where the output change value of the fiber optic gyroscope is 0 needs to occupy most of the time. Such a high proportion requirement ensures that only when the output data of the fiber optic gyroscope is almost unchanged for a long time will it be judged as a fault, thereby avoiding false alarms caused by small fluctuations in the data.

[0034] Preferably, when analyzing and judging the data of the accelerometer of each axis, if the pulse output of the accelerometer of this axis exceeds a preset threshold pulse number, it is also determined that the accelerometer of this axis is faulty.

[0035] With this setup, the pulse output-based fault detection method can detect problems faster and reduce time delays in the fault detection process compared to traditional methods based on periodic detection or data analysis. Since the threshold is set based on the normal operating range of the accelerometer, the system will only determine a fault when the pulse output deviates significantly from the normal range. This helps avoid misjudgments caused by data fluctuations or noise interference.

[0036] Preferably, the preset threshold pulse number is ±2500.

[0037] With such settings, reasonable threshold settings can reduce false alarms caused by data fluctuations or noise interference to a certain extent. When the pulse output of the accelerometer fluctuates within the range of ±2500, the system will not judge it as a fault, thus ensuring the stability and reliability of the system. In addition, the working environment of the accelerometer may be complex and changeable, such as temperature changes, mechanical vibrations, etc. By setting the threshold of ±2500, the system can adapt to these environmental changes to a certain extent and maintain the accuracy and stability of accelerometer fault detection.

[0038] Preferably, the preset duration is greater than or equal to 100 seconds.

[0039] With this setting, by setting a preset duration greater than or equal to 100 seconds, the system can continuously monitor the data of the accelerometers of each axis for a long time. The advantage of this is that it can capture the stable performance of the accelerometer over a long period of time and reduce misjudgments caused by short-term fluctuations or accidental factors. In addition, long-term monitoring helps the system eliminate various possible external interferences, such as short-term mechanical vibrations, electromagnetic noise, etc. These interferences may affect the accelerometer readings in a short period of time, but will not have a significant impact on the overall performance over a long period of time. Therefore, through long-term monitoring, the system can more accurately identify real faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0041] Figure 1 A flowchart of the method is shown in FIG.

[0042] Figure 2 Flow chart of data analysis and judgment of fiber optic gyroscopes of each axis in the embodiment;

[0043] Figure 3 Flow chart of data analysis and judgment of accelerometers of each axis in the embodiment. DETAILED DESCRIPTION

[0044] The following is a further detailed description through specific implementation methods:

[0045] Example:

[0046] like Figure 1 As shown, this embodiment discloses a detection method for determining an inertial measurement unit fault, comprising the following steps:

[0047] Obtain fiber optic gyro data and accelerometer data from the inertial measurement unit;

[0048] Analyze the gyroscope data of each axis; Figure 2 As shown, the data of the fiber optic gyroscope of each axis is analyzed and judged, and the value of the output change of adjacent sampling intervals is counted. If the value of the output change of 0 meets the preset first condition, it is determined that the fiber optic gyroscope of this axis has a fault.

[0049] In specific implementation, the preset first condition is that within the preset comparison time, the proportion of the output change value of the fiber optic gyroscope of this axis being 0 is greater than the preset ratio. Among them, the preset comparison time is greater than or equal to 8 seconds; the preset ratio is greater than or equal to 85%. Setting the comparison time to be greater than or equal to 8 seconds means that the system will observe and analyze the output data of the fiber optic gyroscope for a longer period of time. The advantage of doing so is that it can effectively reduce misjudgments caused by short-term or accidental external interference (such as short-term mechanical vibration, electromagnetic interference, etc.), making fault detection more robust and reliable. The preset ratio is greater than or equal to 85%, which means that within the comparison time, the situation where the output change value of the fiber optic gyroscope is 0 needs to occupy most of the time. Such a high proportion requirement ensures that only when the output data of the fiber optic gyroscope is almost unchanged for a long time will it be judged as a fault, thereby avoiding false alarms caused by small fluctuations in the data.

[0050] In this way, by setting a comparison time and a preset ratio, the abnormal performance of the fiber optic gyroscope under a fault state can be captured more sensitively. Even if the output data of the fiber optic gyroscope does not fluctuate much within the overall range, as long as the proportion of its output change value of 0 within a specific time exceeds the preset ratio, the system can issue a fault warning in time, avoiding the situation of missed detection due to small data fluctuations. In addition, by setting a reasonable comparison time and preset ratio, this method can still maintain the accuracy and stability of fiber optic gyroscope fault detection in complex and changeable application environments (such as environmental noise, temperature changes, mechanical vibrations, etc.). In addition, the setting of the judgment condition fully considers the normal working characteristics of the fiber optic gyroscope and the volatility of the output data, avoiding false alarms caused by normal data fluctuations. At the same time, by setting a reasonable preset ratio, it can also reduce the missed alarm caused by the unclear fault performance to a certain extent.

[0051] During specific implementation, when analyzing and judging the data of the fiber optic gyroscope of each axis, if its output exceeds the preset first value, it is also determined that the fiber optic gyroscope of this axis has a fault. Among them, the preset first value is ±800000000. Setting ±800000000 as the threshold value can take into account the normal working range and performance parameters of the fiber optic gyroscope to ensure that under normal circumstances, the output data of the fiber optic gyroscope will not exceed this range. In addition, normal fluctuations or noise interference (such as environmental noise, temperature changes, mechanical vibrations, etc.) can also be filtered out to a certain extent to avoid false alarms caused by these factors.

[0052] In this way, the newly added judgment conditions enable the method to cope with more types of fault modes, such as abnormal sensitivity, zero drift, excessive noise, etc. of the fiber optic gyroscope, which may cause the output value to exceed the preset first value. By comprehensively judging the output value of 0 and the output exceeding the preset first value, the method can more comprehensively identify the potential faults of the fiber optic gyroscope and improve the reliability of fault detection. In addition, the introduction of the judgment condition does not increase additional hardware dependence or complex calculations, and maintains the simplicity and efficiency of the detection process. Therefore, the method not only improves the accuracy of fault detection, but also maintains good practicality and economy. By introducing the preset first value as another criterion for judging whether the fiber optic gyroscope is faulty, the method further enhances the comprehensiveness and accuracy of fault detection, and provides a more reliable and efficient means for fault detection of the fiber optic gyroscope.

[0053] If the X, Y, and Z axes of the fiber optic gyroscope are all faulty, it is determined that the gyroscope main circuit board is faulty.

[0054] Analyze the data of each accelerometer; such as Figure 3 As shown, the data of the accelerometer of each axis is analyzed and judged, and the duration of the output value of each axis being 0 is counted. If the duration reaches a preset duration, it is determined that the accelerometer of the axis is faulty.

[0055] Wherein, the preset duration is greater than or equal to 100 seconds. By setting the preset duration greater than or equal to 100 seconds, the system can continuously monitor the data of the accelerometers of each axis for a long time. The advantage of this is that the stable performance of the accelerometer over a long period of time can be captured, reducing misjudgments caused by short-term fluctuations or accidental factors. In addition, long-term monitoring helps the system eliminate various possible external interferences, such as short-term mechanical vibrations, electromagnetic noise, etc. These interferences may affect the readings of the accelerometer in a short period of time, but will not have a significant impact on the overall performance over a long period of time. Therefore, through long-term monitoring, the system can more accurately identify real faults.

[0056] In specific implementation, when analyzing and judging the data of the accelerometers of each axis, if the pulse output of the accelerometer of that axis exceeds the preset threshold pulse number, it is also determined that the accelerometer of that axis is faulty. In this way, compared with the traditional method based on periodic detection or data analysis, the fault detection method based on pulse output can find problems more quickly and reduce the time delay in the fault detection process. Since the threshold is set according to the normal working range of the accelerometer, the system will only determine it as a fault when the pulse output deviates significantly from the normal range. This helps to avoid misjudgments caused by data fluctuations or noise interference.

[0057] Among them, the preset threshold pulse number is ±2500. Reasonable threshold setting can reduce false alarms caused by data fluctuations or noise interference to a certain extent. When the pulse output of the accelerometer fluctuates within the range of ±2500, the system will not judge it as a fault, thereby ensuring the stability and reliability of the system. In addition, the working environment of the accelerometer may be complex and changeable, such as temperature changes, mechanical vibrations, etc. By setting a threshold of ±2500, the system can adapt to these environmental changes to a certain extent and maintain the accuracy and stability of accelerometer fault detection.

[0058] If the X, Y, and Z axis accelerometers are all faulty, it is determined that the I / F conversion module is faulty.

[0059] The faults identified in the above steps are counted and fault warnings are issued.

[0060] In order to facilitate those skilled in the art to better understand the specific method for judging the failure of each component in the present method, a statistical description is given in Table 1. Among them, the preset comparison time is 10 seconds; the preset ratio is 90%; and the preset duration is 120 seconds.

[0061] Table 1

[0062]

[0063] This method first obtains the fiber optic gyroscope data and accelerometer data of the IMU, and analyzes the data of each axis respectively. Through in-depth analysis of the data, it is possible to capture the subtle changes of the fiber optic gyroscope and accelerometer under normal working conditions, as well as the abnormal performance under fault conditions. For the fiber optic gyroscope, this method counts the values ​​of the interval gyroscope output change to 0, and compares it with the preset first condition, so as to accurately identify whether there is a fault. Similarly, for the accelerometer, the method counts the duration of the output value of 0 and compares it with the preset duration to determine whether there is a fault. This method based on data statistical analysis improves the accuracy of fault identification. In addition, this method can quickly locate the fault. Once the fault is identified, the method can quickly locate the specific hardware component. For example, if the X, Y, and Z three-axis fiber optic gyroscopes are all faulty, it is judged that the gyroscope main circuit board is faulty; if the three-axis accelerometers are all faulty, it is judged that the I / F conversion module is faulty; the fault of a certain axis fiber gyroscope, the fault of a certain axis accelerometer. This ability to quickly locate faults helps to take corresponding maintenance or replacement measures in time to avoid greater impact of the fault on the system. In addition, this method does not rely on additional devices. The entire detection process is completely dependent on the data of the IMU itself, without the need for additional inertial navigation inertial device support. This not only reduces the detection cost, but also improves the flexibility and convenience of detection.

[0064] This method can accurately identify and quickly locate various potential and complex fault modes through in-depth analysis and comprehensive diagnosis of a single IMU component without relying on additional inertial navigation devices.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for determining an inertial measurement unit fault, characterized in that: The following steps are involved: Obtain fiber optic gyro data and accelerometer data from the inertial measurement unit; Analyze the data of the gyroscope of each axis; analyze and judge the data of the fiber optic gyroscope of each axis, and count the values ​​of the output changes of adjacent sampling intervals to 0. If the value of the output change to 0 meets the preset first condition, it is determined that the fiber optic gyroscope of the axis has a fault; If the X, Y, and Z three-axis fiber optic gyroscopes are all faulty, it is determined that the gyroscope main circuit board is faulty; Analyze the data of each accelerometer; analyze and judge the data of the accelerometer of each axis, and count the duration of its output value being 0. If the duration reaches the preset duration, it is determined that the accelerometer of the axis is faulty; If the X, Y, and Z axis accelerometers are all faulty, it is determined that the I / F conversion module is faulty; The faults identified in the above steps are counted and fault warnings are issued.

2. The method for determining an inertial measurement unit fault as claimed in claim 1, wherein: When analyzing and judging the data of the fiber optic gyroscope of each axis, if its output exceeds a preset first value, it is also determined that the fiber optic gyroscope of this axis has a fault.

3. The method for determining an inertial measurement unit fault as claimed in claim 2, wherein: The preset first value is ±800000000.

4. The method for determining an inertial measurement unit fault according to claim 1, wherein: The preset first condition is that within a preset comparison time, the proportion of the output change value of the fiber optic gyroscope of the axis being 0 is greater than a preset ratio.

5. The method for determining an inertial measurement unit fault as claimed in claim 4, characterized in that: The preset comparison time is greater than or equal to 8 seconds; the preset ratio is greater than or equal to 85%.

6. The method for determining an inertial measurement unit fault as claimed in claim 1, wherein: When analyzing and judging the data of the accelerometer of each axis, if the pulse output of the accelerometer of this axis exceeds the preset threshold pulse number, it is also determined that the accelerometer of this axis is faulty.

7. The method for determining an inertial measurement unit fault as claimed in claim 6, characterized in that: The preset threshold pulse number is ±2500.

8. The method for determining an inertial measurement unit fault as claimed in claim 1, wherein: The preset duration is greater than or equal to 100 seconds.

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