Calibration method of MEMS acceleration sensor module
By employing a dual calibration method to detect the horizontal and tilt angles of the MEMS accelerometer, the problems of sensor initial state dependence and installation position offset were resolved, enabling precise compensation and safety alarms, and improving the sensor's accuracy and stability.
Patent Information
- Application Number
- CN202511151484.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-18
- Publication Date
- 2025-11-07
AI Technical Summary
Existing initial state calibration methods for MEMS accelerometers rely on the sensor's initial state, leading to inaccurate compensation values, reduced measurement range, and data distortion. They cannot effectively control the effects of gravitational acceleration, and cannot be accurately calibrated when the sensor's installation position is offset, posing a safety risk.
A dual calibration method of horizontal calibration and tilt angle detection is adopted. The sensor body is calibrated by calibrating the positioning fixture of the water platform with a level, the offset value is recorded and the horizontal state is calibrated. After installation, the tilt angle is checked every time it is powered on, the tilt angle is calculated and compensated within the preset range, and the angle is alarmed or fed back to the host computer.
Effectively control compensation values, limit component tolerances, improve the accuracy of secondary development functions, avoid safety risks, ensure that sensors alarm under abnormal conditions, and improve data accuracy and range stability.
Smart Images
Figure CN120908482A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of MEMS acceleration sensor, and particularly relates to a calibration method of a MEMS acceleration sensor module. BACKGROUND
[0002] At present, MEMS acceleration sensors are applied in various industries, and the calibration method of the MEMS acceleration sensor generally adopts initial state calibration. When powered on, the initial value is read as a compensation value, the three-axis acceleration values read by the MEMS chip are directly compensated to zero, and the subsequent algorithm development and calculation all use the data after zero compensation. The calibration method has the following two disadvantages in application: The initial state calibration when powered on depends on the initial state of the acceleration sensor (that is, the accuracy of the acceleration sensor itself and the level of the installation position), if the initial state of the acceleration sensor is too large, the compensation value will be large, and the actual range will be forced to be small due to the zero compensation (the original +2g range can only measure +1.5g after compensation), and there is a risk of serious data distortion and failure. In addition, the data called by the initial compensation is affected by the gravity acceleration, which cannot be controlled. The accuracy and completion of the secondary development function are affected.
[0003] 2. With the aging of the washing machine equipment, the loosening of the washing machine tension spring and other conditions, the initial position of the sensor before power-on will be offset, at this time, the initial position of the tilt state is used to calibrate the sensor, and accurate data cannot be obtained, which may cause more serious safety risks. The sensor module data used by the upper computer is based on the normal state test development, and the data of the abnormal aging state may cause the upper computer to misjudge. The sensor itself cannot judge whether the installation position before power-on is in the normal state, so it cannot alarm the upper computer. SUMMARY
[0004] In view of the problems in the prior art, the purpose of the present application is to provide a calibration method of a MEMS acceleration sensor module, which can effectively control and alarm the compensation value of calibration, and improve the accuracy of secondary development functions based on acceleration.
[0005] To achieve the above purpose, the technical scheme adopted by the present application is: A calibration method of a MEMS acceleration sensor module, the calibration method comprising the following steps: Step 1, calibrate the acceleration sensor module body: add a horizontal calibration process in the production test process of the acceleration sensor module; specifically as follows: Step 1.1, use the level to calibrate the water platform positioning tool, adjust to the absolute horizontal position perpendicular to the direction of gravitational acceleration; then install the acceleration sensor module on the water platform positioning tool; Step 1.2, collect three-axis acceleration data output by the MEMS chip, record the offset value, and judge whether the offset value is normal; if the offset value is not normal, it is judged to be defective; if the offset value is normal, the offset value range of the horizontal state is recorded to calibrate the horizontal state; Step 2, install the acceleration sensor module on the washing machine, and check and calibrate the inclination every time it is powered on; the specific steps are as follows: Step 2.1, after the acceleration sensor module is powered on, collect three-axis acceleration data output by the MEMS chip, judge whether the installation position is horizontal according to the offset value of the horizontal state, if the installation position is horizontal, continue to read the three-axis acceleration data for subsequent function algorithm; if the installation position is not horizontal, calculate the inclination angle; Step 2.2, calculate the inclination angle by using the trigonometric function; Step 2.3, judge whether the inclination angle is within the preset range, if not, alarm to the host computer, and inform that the inclination position is too large at this time, and the installation position needs to be checked; if it is within the preset range, the inclination angle is fed back to the host computer; the collected acceleration original data is used for secondary compensation of the inclination angle, and then provided to the subsequent function algorithm.
[0006] In step 1.2, the collected three-axis acceleration data is recorded after filtering processing; in step 2.2, the collected three-axis acceleration data is filtered, and then the inclination angle is calculated.
[0007] The filtering processing is as follows: (a) collect M groups of three-axis acceleration data output by the MEMS chip, and read one group at an interval threshold time; (b) sort the M groups of three-axis acceleration data from large to small; remove the largest N groups of data and the smallest N groups of data after sorting, take the average value of the middle M-2N groups of three-axis acceleration data, and record it as a group of average values, wherein M>2N; (c) repeat the above (a)-(b), take K groups of average values; (d) sort the K groups of average values from large to small, remove the largest J groups of data and the smallest J groups of data after sorting, take the average value of the middle K-2J groups of data as the final filtered three-axis acceleration data.
[0008] In step 2.2, the inclination angle is calculated as follows: Calculate the angle between the Z-axis and the direction of gravity , the formula is as follows: ; wherein, is the X-axis acceleration, is the Y-axis acceleration, is the Z-axis acceleration; the complementary angle of the tilt angle of the XY plane is calculated, and the tilt angle can be obtained by subtracting the complementary angle from 90 degrees , and the formula is as follows: ; the final tilt angle , and the average value of the results of the two calculation methods is taken: .
[0009] After the above scheme is adopted, the present application has corresponding restrictions and control on the compensation value through horizontal calibration and tilt angle detection calibration, and the slight tolerance of the device or component itself can be limited in the corresponding range during production. The compensation for the tilt can also be controlled and warned. Due to the zero compensation, the range loss problem can also be controlled within a reasonable range by limiting the angle. After two calibrations, the calculated tilt angle can truly reflect the tilt angle relative to the direction of gravity, and the interference of the gravitational acceleration has clear angle data control, which is more suitable for actual application and improves the precision of the secondary development function based on acceleration. BRIEF DESCRIPTION OF DRAWINGS
[0010] Figure 1 is a method flowchart of the present application; Figure 2 is a flowchart of three-axis acceleration data filtering processing. DETAILED DESCRIPTION
[0011] The present application discloses a calibration method of a MEMS acceleration sensor module, which solves the problem of precision deviation and range loss caused by the influence of gravitational acceleration on a MEMS acceleration sensor chip or a sensor module based on a MEMS acceleration chip. In the past design, the above errors are ignored.
[0012] However, with the development of science and technology and the continuous iteration of semiconductor technology, people's performance requirements for electronic products are constantly improving. The precision of the commonly used low-cost MEMS chip has reached 0.061 mg / LSB, and the influence of gravitational acceleration caused by several degrees of tilt will cause several hundred to several thousand numerical deviations on the original data, so corresponding optimization and control are needed.
[0013] As shown in Figure 1 , the calibration method of the present application comprises the following steps: Step 1, calibrate the acceleration sensor module body: add a horizontal calibration process in the production and testing process of the acceleration sensor module. Specifically as follows: Step 1.1: Use a level to calibrate the water platform positioning fixture and adjust it to be absolutely horizontal, perpendicular to the direction of gravitational acceleration; then install the acceleration sensor module on the water platform positioning fixture.
[0014] Once the accelerometer module is assembled, all inherent external deviation factors are present (such as chip consistency deviation, PCB levelness deviation, PCBA mounting levelness deviation, PCBA and housing mounting levelness deviation, and housing mounting surface levelness deviation). The accelerometer module is then installed on a horizontal platform positioning fixture for calibration, compensating for any deviations in the accelerometer module itself.
[0015] After compensating the data read during calibration, the deviation compensation caused by the sensor's tolerance can be set to 0. At this point, the sensor's compensation value is constant and will not increase continuously as the equipment ages or the module's installation position shifts.
[0016] Step 1.2: Collect the triaxial acceleration data output by the MEMS chip, record the offset value, and determine whether the offset value is normal; if the offset value is abnormal, it is rejected and judged as defective; if the offset value is normal, record the offset value range of the horizontal state to calibrate the horizontal state.
[0017] For accelerometers that are deemed faulty, it can be further determined whether the fault lies in the chip or in the assembly, so that improvements can be made in the future.
[0018] Theoretically, on a stationary water platform, only the Z-axis experiences a gravitational acceleration of 1G, while the X and Y axes show no acceleration data. However, due to installation errors and other factors, the actual sensor parameters cannot perfectly match the XYZ coordinate system: the Z-axis is in the same direction as the gravitational acceleration, while the XY plane is perpendicular to the direction of the gravitational acceleration. In this case, the XY axes experience a slight acceleration due to gravity, and this value represents the actual offset.
[0019] After horizontal calibration, the horizontal position of the accelerometer module is determined. That is, when the accelerometer module is installed in a horizontal position, the acceleration value detected perpendicular to the direction of gravity (Z-axis) is 1G, while the acceleration experienced on the other two axes (X-axis and Y-axis) is 0.
[0020] Step 2: Install the accelerometer sensor module on the washing machine and perform a tilt check and calibration every time it is powered on. Details are as follows: Step 2.1: After the accelerometer sensor module is powered on, it collects the triaxial acceleration data output by the MEMS chip. Based on the horizontal offset value, it determines whether the installation position is horizontal. If the installation position is horizontal, it continues to read the triaxial acceleration data for subsequent functional algorithms; if the installation position is not horizontal, it calculates the tilt angle.
[0021] The triaxial acceleration data and the numerical judgment of the horizontal state recorded in step 1.2 are used to determine whether it is horizontal. If it is determined to be horizontal, it is determined to be a horizontal state, and the next step is entered. Other functions are calculated and executed, and the feedback tilt angle is set to 0 degrees by default. If it is determined not to be horizontal, it is determined to be a tilt state, and the tilt angle is calculated according to the 3-axis data, and the corresponding tilt angle is fed back.
[0022] Step 2.2, the tilt angle is calculated by trigonometric function.
[0023] The tilt angle is calculated as follows: 1. Calculate the angle between the Z-axis and the direction of gravity , the formula is as follows:
[0024] where, is the X-axis acceleration, is the Y-axis acceleration, is the Z-axis acceleration.
[0025] 2. Calculate the complementary angle of the tilt angle of the XY plane, 90 degrees minus the complementary angle to get the tilt angle , the formula is as follows:
[0026] 3. The final tilt angle is the average of the results of the two calculation methods:
[0027] The final value , filter the error produced when calculating the trigonometric function, for example: when calculating the division, the calculation result is an infinitely small number, when rounding or other compensation is taken, the error is produced.
[0028] Step 2.3, determine whether the tilt angle is within the preset range, if not, alarm to the host computer, indicating that the tilt position is too large at this time, and the installation position needs to be checked; if it is within the preset range, the tilt angle is fed back to the host computer; the collected acceleration raw data is zeroed and compensated, and provided to the subsequent function algorithm.
[0029] In step 1.2, the triaxial acceleration data collected after filtering is recorded as an offset value. Similarly, in step 2.2, the triaxial acceleration data collected after filtering is then calculated to obtain the tilt angle.
[0030] As shown in Figure 2 , the filtering process is as follows: (a) Collecting triaxial acceleration data outputted by M groups (for example, 100 groups) of MEMS chips, and reading a group of data at an interval threshold time (for example, 150 us). The data is collected at intervals, so that the continuous error caused by interference can be avoided.
[0031] (b) Sorting the M groups of triaxial acceleration data from large to small; removing the largest N (for example, 10 groups) of data and the smallest N groups of data after sorting, and taking the average of the middle M-2N groups of triaxial acceleration data, and recording as a group of average values, wherein M>2N. By taking the middle value, the peak value and the low value are filtered, and the data fluctuation error is filtered.
[0032] (c) Repeating the above (a)-(b) to take K groups (for example, 30 groups) of average values.
[0033] (d) Sorting the K groups of average values from large to small, removing the largest J groups (for example, 3 groups) of data and the smallest J groups of data after sorting, and taking the average of the middle K-2J groups of data as the final filtered triaxial acceleration data.
[0034] The average values of the K groups are taken, and then the sorting, middle value and average value operations are performed again. The values of each time segment are filtered again. This operation supplements the fluctuation deviation value of the periodic fluctuation of the data to the final value, and is more consistent with the actual initial value of the acceleration sensor module.
[0035] In summary, the errors of the chip itself, the errors of the module patch assembly, and the errors of the installation and use are optimized during horizontal calibration, so that the abnormality caused by the chip error and the assembly error can be accurately screened out in advance. The slight tolerance of the device or component itself can be recorded and controlled during the first calibration of the product, so that the material quality and the quality of the raw product can be effectively controlled and improved.
[0036] The present application has corresponding restrictions and controls on the compensation value through horizontal calibration and inclination angle detection calibration, and the slight tolerance of the device or component itself can be limited within a corresponding range during production. The compensation for inclination can also be controlled and warned. Due to zero compensation, the range loss problem can also be controlled within a reasonable range through angle limitation.
[0037] After two calibrations, the calculated inclination angle can truly reflect the inclination angle relative to the direction of gravity, and the interference with the gravitational acceleration can be clearly controlled with angle data, which is more suitable for practical applications and improves the accuracy of secondary development functions based on acceleration.
[0038] In the application of a washing machine, the method of the present application can effectively solve the following problems: If the MEMS sensor is affected by temperature, electromagnetic interference and the like, and the internal MEMS structure fails, the power-on position detection data will immediately feedback an alarm to avoid continued use and safety problems.
[0039] If the drum tension spring is aged and loose, and the drum is tilted, which will affect the movement balance, the sensor can detect the abnormality at the moment of power-on before the drum rotates, and the upper computer is alarmed in advance, so that the upper computer no longer drives the drum to rotate. Safety accidents are avoided.
[0040] If the sensor lock is skewed or the screw is not fixed in place during the assembly and production of the washing machine, the sensor module can detect the abnormality during the power-on self-checking and report an error.
[0041] If the wiring is aged or the joint contact is poor during the operation of the washing machine, that is, the power is turned on again after power failure due to poor contact during drum movement, the sensor module will alarm and feedback when detecting the position at power-on (at this time in the movement state), avoiding accidents such as drum collision caused by incorrect initial position judgment.
[0042] The precision of the MEMS acceleration value is improved, and the precision of the secondary development functions based on the MEMS acceleration sensor, such as displacement detection, resonance judgment, and weight judgment, is improved.
[0043] The above is only an embodiment of the present application, and does not limit the technical scope of the present application in any way. Therefore, any slight modification, equivalent change and modification of the above embodiment according to the technical essence of the present application still falls within the scope of the technical solution of the present application.
Claims
1. A method of calibrating a MEMS acceleration sensor module, characterized by, The calibration method comprises the following steps: Step 1, carry out acceleration sensor module body calibration: add a horizontal calibration process in the acceleration sensor module production test process; the specific steps are as follows: Step 1.1, use a level to calibrate the water platform positioning tool, adjust to an absolute horizontal position perpendicular to the direction of gravitational acceleration; then install the acceleration sensor module on the water platform positioning tool; Step 1.2, collect three-axis acceleration data output by the MEMS chip, record the offset value, and determine whether the offset value is normal; if the offset value is not normal, it is judged to be defective; if the offset value is normal, record the offset value range of the horizontal state, and use it to calibrate the horizontal state; Step 2, install the acceleration sensor module on the washing machine, and check and calibrate the inclination every time it is powered on; the specific steps are as follows: Step 2.1, after the acceleration sensor module is powered on, collect three-axis acceleration data output by the MEMS chip, and determine whether the installation position is horizontal according to the horizontal state offset value; if the installation position is horizontal, continue to read the three-axis acceleration data for subsequent function algorithm; if the installation position is not horizontal, calculate the inclination angle; Step 2.2, calculate the inclination angle by using trigonometric functions; Step 2.3, determine whether the inclination angle is within the preset range; if it is not within the preset range, alarm the host computer to inform that the inclination position is too large at this time and the installation position needs to be checked; if it is within the preset range, feedback the inclination angle to the host computer; after the collected acceleration raw data is compensated for the inclination angle, it is provided to the subsequent function algorithm.
2. The method of calibrating a MEMS acceleration sensor module of claim 1, wherein, In step 1.2, the collected three-axis acceleration data is recorded after filtering processing; in step 2.2, the collected three-axis acceleration data is filtered and then the inclination angle is calculated.
3. The method of calibrating a MEMS acceleration sensor module according to claim 2, wherein, The filtering processing is as follows: (a) collect M groups of three-axis acceleration data output by the MEMS chip, read one group at an interval threshold time; (b) sort the M groups of three-axis acceleration data from large to small; remove the largest N groups of data and the smallest N groups of data after sorting, take the middle M-2N groups of three-axis acceleration data to calculate the average value, and record it as a group of average values, wherein M>2N; (c) repeat the above (a)-(b) to take K groups of average values; (d) sort the K groups of average values from large to small, remove the largest J groups of data and the smallest J groups of data after sorting, take the average value of the middle K-2J groups of data as the final filtered three-axis acceleration data.
4. The method of claim 1, wherein, In step 2.2, the inclination angle is calculated as follows: The angle between the Z axis and the direction of gravity is calculated and is given by the formula: ； wherein, is the X-axis acceleration, is the Y-axis acceleration, is the Z-axis acceleration; The complementary angle of the tilt angle of the XY plane is calculated, and the tilt angle can be obtained by subtracting the complementary angle from 90 degrees , and the formula is as follows: ； Final tilt angle The average of the two results is taken: .