A self-testing method and self-testing device for an accelerometer

Abstract

The present invention relates to the technical field of accelerometers, and more specifically, to a self-testing method and device for an accelerometer. The method comprises generating a preset vibration signal through a vibration signal module within a sensor module, the vibration signal module including a drive circuit and a vibrator. The accelerometer collects vibration response data excited by the vibrator, and determines whether the accelerometer is operating normally based on a comparison result of the vibration response data with preset standard parameters. The present invention introduces a vibration signal module to generate a preset vibration signal, and combines the comparison of the accelerometer's dynamic response data with the preset standard parameters to achieve a more realistic and comprehensive self-testing solution, thereby improving the reliability of the accelerometer and user trust.

Description

Technical Field

[0001] The present invention relates to the technical field of accelerometers, and more particularly to a self-testing method and a self-testing device for an accelerometer. Background Art

[0002] Single-axis and multi-axis accelerometer chips available on the market can be divided into two categories: some with self-test capabilities and some without. The purpose of the self-test function is to check the accelerometer's operating status before use to ensure proper operation. If the test results are normal, the accelerometer can continue to be used; if abnormal, the accelerometer must be repaired or replaced to ensure measurement accuracy and reliability. Accelerometers without self-test capabilities lack effective status verification methods, making their output data more prone to errors, leading to misjudgments and affecting system performance.

[0003] Currently, accelerometers with self-test functions usually use a built-in self-test mechanism. The common practice is to stimulate the sensitive elements inside the accelerometer by applying an external DC signal, causing it to output a specific signal. The presence of this signal is then used to determine whether the accelerometer is functioning properly. However, this self-test method has significant drawbacks:

[0004] The self-test process relies entirely on internal circuits and theoretical signals. Users cannot intuitively confirm whether the self-test actually occurs, which makes the credibility of the results questionable.

[0005] DC signal excitation only simulates static conditions and fails to test the dynamic response capability of the accelerometer in a real vibration environment, thus masking potential faults (such as abnormal damping materials or frequency selectivity failure), thereby causing misjudgment.

[0006] Some common accelerometers on the market, such as the SCL3400, MXC6235xQ, and BMI260 six-axis sensors, lack self-test capabilities. Failures in these products are often difficult to detect early, increasing system risks. Therefore, a self-test method suitable for single-axis and multi-axis accelerometers is urgently needed that overcomes the shortcomings of existing technologies and provides a reliable and reliable detection method. Summary of the Invention

[0007] To this end, the purpose of the present invention is to provide a self-test method and self-test device for an accelerometer. By introducing a vibration signal module to generate a preset vibration signal, and combining the dynamic response data of the accelerometer with the comparison of preset standard parameters, a more realistic and comprehensive self-test solution is achieved, thereby improving the reliability of the accelerometer and user trust.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A self-test method for an accelerometer comprises the following steps:

[0010] S1. Generate a preset vibration signal through a vibration signal module inside the sensor module, wherein the vibration signal module includes a driving circuit and a vibrator.

[0011] S2, the accelerometer collects the vibration response data excited by the vibrator to obtain the vibration time domain signal, the sampling frequency of which is satisfy ,in is the highest frequency of the oscillator, is a coefficient greater than 2;

[0012] S3. Processing the vibration time domain signal to obtain characteristic parameters, including:

[0013] S31, low-pass filter the time domain signal, cutoff frequency > ;

[0014] S32, converting the filtered time domain signal into a frequency domain signal, and extracting at least one of the main frequency energy, harmonic component, and signal-to-noise ratio as a characteristic parameter;

[0015] S4. Determine the working status of the accelerometer based on the comparison result between the characteristic parameter and the preset standard parameter.

[0016] The present invention is further configured as follows: Step S4 specifically includes:

[0017] S41, dynamically adjusting the judgment threshold according to environmental parameters;

[0018] S42. When the characteristic parameter exceeds the judgment threshold for the first time, it is marked as the initial abnormality and triggers 3-5 repeated vibration tests, each vibration lasting 10-500ms;

[0019] S43, using the least squares method to fit the multiple test data, correcting the zero bias and sensitivity parameters of the accelerometer to obtain corrected calibration parameters;

[0020] S44, re-execute steps S1-S3 to perform a verification test based on the calibration parameters;

[0021] The present invention is further configured as follows: the sampling frequency The value range is 10Hz-20kHz;

[0022] The cut-off frequency =1.2× ;

[0023] When the characteristic parameter deviation of the verification test exceeds 80% of the judgment threshold, an unrecoverable fault alarm signal is generated and the self-test function is disabled.

[0024] The present invention is further configured as follows: Step S1 also includes detecting the actual vibration amplitude and frequency of the vibrator through a feedback loop, and adjusting the PWM duty cycle or voltage amplitude output by the driving circuit in real time, so that the frequency error of the actual vibration signal is less than 3% and the amplitude error is less than 5%.

[0025] The present invention is further configured as follows: in step S1, it also includes: performing a frequency scan on the vibrator in the range of 50Hz-5kHz, detecting the peak value of the response amplitude of the accelerometer to determine the resonant frequency, and using the resonant frequency as the main frequency of the subsequent vibration signal.

[0026] The present invention is further configured such that: the feedback loop includes:

[0027] The driving current phase is detected by a current sensor connected in series in the vibrator driving circuit. Combined with the vibration time domain signal output by the accelerometer, the actual vibration frequency is calculated and compared with the preset frequency to dynamically adjust the duty cycle of the driving signal.

[0028] The present invention is further configured to further include, after the frequency scan:

[0029] a. At resonant frequency As the center, the logarithmic frequency interval is 0.8 -1.2 Sine sweep excitation is performed within the range of Dynamic adjustment, where is the amplitude of the excitation signal; is the resonant frequency Corresponding to the peak driving voltage; is the frequency of the excitation signal;

[0030] b. Synchronously collect the differential output signal of the accelerometer and the phase difference of the driving current to generate the response amplitude curve A(f) after removing the carrier interference;

[0031] c. Perform Hartley transform on the A(f) curve and calculate the standard deviation of the frequency domain energy distribution as an indicator of smoothness;

[0032] d. Use the dynamic time warping algorithm to calculate the morphological similarity between the measured A(f) curve and the preset standard template curve ;

[0033] e. When satisfied > or < When , it is determined that there is an abnormality in the damping material of the accelerometer, where 1.2-1.8; 0.9-0.95; is the preset reference standard deviation.

[0034] The present invention is further configured to: after generating an unrecoverable fault alarm signal, encode the fault type as hardware damage or environmental interference overload, and write the fault information into a non-erasable storage area of ​​the accelerometer through an encrypted check code.

[0035] A self-test device for an accelerometer, comprising:

[0036] A rigid packaging shell, the interior of which is divided into a first cavity and a second cavity;

[0037] The vibrator and the driving circuit board are fixed in the first cavity;

[0038] The second cavity is isolated and installed with an accelerometer and a signal processing module through a metal shielding cover;

[0039] The driving circuit board includes an H-bridge circuit and a closed-loop control chip, and the signal processing module integrates an FFT analysis unit and a calibration database.

[0040] The present invention is further configured to include:

[0041] Wireless communication module, supporting receiving external trigger commands and uploading self-test results to the cloud server;

[0042] The multi-axis collaborative interface is configured to synchronously trigger the self-test of the three-axis accelerometers in pairs, and generate a global fault alarm signal when the self-test results of at least two axes are abnormal.

[0043] Compared with the shortcomings of the prior art, the beneficial effects of the present invention are:

[0044] A single vibrator within the sensor module generates a realistic, pre-set vibration signal, replacing traditional DC excitation. This allows users to sense the vibration and visually verify the authenticity of the self-test. Test results are based on actual dynamic responses, rather than theoretical assumptions, significantly improving reliability and user confidence.

[0045] The vibrator output is calibrated in real time through closed-loop feedback (frequency error <3%, amplitude error <5%), combined with wideband scanning and resonant frequency detection to ensure that the vibration signal accurately matches the accelerometer characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the process of the present invention;

[0047] Figure 2 This is a timing diagram of the vibration signal startup of the present invention;

[0048] Figure 3 This is a graph of the output value of the accelerometer collected when there is no vibration signal in the first embodiment of the present invention;

[0049] Figure 4 This is a graph of the output value of the accelerometer collected when there is a vibration signal in the first embodiment of the present invention;

[0050] Figure 5 This is a graph of the output value of the accelerometer collected when there is no vibration signal in the second embodiment of the present invention;

[0051] Figure 6 This is a graph of the output value of the accelerometer collected when there is no vibration signal in the second embodiment of the present invention. DETAILED DESCRIPTION

[0052] Reference Figures 1 to 6 An embodiment of a self-test method for an accelerometer of the present invention is further described, specifically comprising:

[0053] Step 1: Preset vibration signal generation and resonant frequency scanning

[0054] The core of this step is to use the vibration signal module integrated in the sensor module to generate a precise and controllable preset vibration signal, and determine the resonant frequency of the oscillator through a feedback loop and frequency scanning, laying the foundation for subsequent precise excitation and performance evaluation.

[0055] A preset vibration signal is generated by a vibration signal module inside the sensor module, and the vibration signal module includes a driving circuit and a vibrator.

[0056] The drive circuit utilizes a full-bridge (H-bridge) circuit design, utilizing a closed-loop control chip to precisely drive the oscillator. The H-bridge circuit provides bidirectional drive capability, improving the oscillator's drive efficiency and response speed. The closed-loop control chip integrates a PWM (pulse width modulation) controller, which precisely adjusts the duty cycle of the output PWM signal, thereby controlling the effective value of the drive voltage. Furthermore, the drive circuit incorporates overcurrent protection, overvoltage protection, and short-circuit protection circuits to ensure module safety under abnormal operating conditions.

[0057] The vibrator is the actuator of the vibration signal and is a piezoelectric ceramic vibrator or an electromagnetic vibrator. Piezoelectric ceramic vibrators use the piezoelectric effect to generate mechanical vibrations by applying voltage. They have the advantages of fast response speed, small size, and low power consumption, and are suitable for generating high-frequency vibration signals. Electromagnetic vibrators use electromagnetic force to drive magnets or coils to generate vibrations. They can produce a large vibration amplitude and are suitable for simulating low-frequency and large-amplitude vibration signals. The vibrator is made of materials with a high elastic modulus and a low damping coefficient to improve vibration efficiency and quality factor. The vibrator is fixed in the first cavity by a precision bonding process or a threaded connection to ensure that the vibration signal can be effectively transmitted to the accelerometer.

[0058] In order to ensure the accuracy and stability of the vibration signal, the vibration signal module integrates a feedback loop.

[0059] In the feedback loop, the driving current phase and amplitude are detected by the current sensor connected in series in the vibrator driving circuit, and the vibration time domain signal collected by the accelerometer itself is used as part of the feedback signal. The closed-loop control chip in the signal processing module is responsible for receiving the feedback signals from the current sensor and accelerometer and performing calculations.

[0060] Frequency / amplitude adjustment algorithm:

[0061] By applying Fourier transform or zero-crossing detection algorithms to the phase changes of the driving current detected by the current sensor, the frequency information of the driving current is extracted and used as a reference for the actual vibration frequency of the vibrator. Simultaneously, the main frequency component of the vibration time-domain signal output by the accelerometer is extracted using a fast Fourier transform (FFT) or spectrum analysis algorithm to further verify and correct the calculated actual vibration frequency.

[0062] The vibration time-domain signal output by the accelerometer directly reflects the vibration amplitude of the vibrator. The actual vibration amplitude of the vibrator can be obtained by performing peak detection, root mean square (RMS) calculation, or integration on the time-domain signal output by the accelerometer.

[0063] The closed-loop control chip compares the calculated actual vibration frequency and amplitude with the preset frequency and amplitude to calculate the frequency and amplitude errors. If the frequency error exceeds 3% or the amplitude error exceeds 5%, the closed-loop control chip adjusts the PWM duty cycle or voltage amplitude of the drive circuit's output in real time. This adjustment strategy employs a PID control algorithm to precisely control the PWM duty cycle or voltage amplitude based on the magnitude and trend of the error. Ultimately, the frequency error of the actual vibration signal is less than 3%, and the amplitude error is less than 5%, ensuring that the vibration signal's accuracy meets self-test requirements.

[0064] Before the formal self-test, the vibrator needs to be frequency-sweeped to determine its resonant frequency. The drive circuit excites the vibrator logarithmically within the frequency range of 50Hz to 5kHz, detecting the peak value of the accelerometer's response amplitude to determine the resonant frequency, which is used as the dominant frequency of the subsequent vibration signal.

[0065] During the frequency sweep, the signal processing module collects the accelerometer's output signal in real time and calculates the response amplitude, which can be the peak-to-peak value, RMS value, or spectrum amplitude of the accelerometer's output signal.

[0066] The signal processing module analyzes the accelerometer response amplitude collected during the frequency sweep and searches for the peak value of the response amplitude. The frequency corresponding to the peak value is the resonant frequency of the oscillator. The resonant frequency represents the frequency point at which the vibrator has the highest vibration efficiency at a specific frequency. Using this frequency as the main frequency of the subsequent vibration signal can improve the utilization rate of vibration energy and the self-detection sensitivity.

[0067] After determining the resonant frequency, a further frequency sweep test was performed to more closely evaluate the accelerometer's performance:

[0068] a. At resonant frequency As the center, the logarithmic frequency interval is 0.8 -1.2 Sine frequency sweep excitation is performed within a frequency range, which covers the frequency response characteristics near the resonant frequency. This can more comprehensively evaluate the performance of the accelerometer near the resonant frequency. In addition, the use of logarithmic frequency interval points for frequency sweep makes the frequency points denser near the resonant frequency, which can more finely capture the details of the frequency response curve.

[0069] The excitation signal amplitude is Dynamic adjustment, where The amplitude of the excitation signal refers to the peak value of the driving voltage output by the driving circuit to the vibrator; is the resonant frequency Corresponding to the peak driving voltage; The frequency of the excitation signal refers to the frequency of the current sweep point; 0.2 is the scaling factor, which is used to control the magnitude of the dynamic adjustment of the amplitude.

[0070] This formula shows that at the resonant frequency Nearby, the excitation signal amplitude is close to With the excitation frequency Deviation from resonant frequency , the excitation signal amplitude V gradually increases. This dynamic adjustment strategy aims to compensate for the reduced vibration efficiency of the vibrator at non-resonant frequencies, ensuring that the accelerometer can obtain a relatively constant excitation intensity throughout the entire frequency sweep range, thereby improving the signal-to-noise ratio and reliability of the frequency response curve A(f).

[0071] b. Generation of response amplitude curve A(f):

[0072] The signal processing module synchronously acquires the accelerometer's differential output signal and the drive current phase difference signal. Accelerometers typically use differential output, which effectively suppresses common-mode noise and improves the signal's anti-interference capability. Acquiring the accelerometer's differential output signal yields a purer vibration response signal. A current sensor detects the drive current signal, and phase information is extracted using a phase demodulation circuit or digital signal processing algorithm. The drive current phase difference signal reflects the phase relationship between the vibrator's drive signal and the vibration response, helping to analyze the vibrator's dynamic characteristics.

[0073] Response amplitude curve A(f) after carrier interference removal: Because the excitation signal is a sinusoidal wave, the accelerometer's output signal and drive current signal both contain carrier components. To obtain the true vibration response amplitude, carrier interference removal is necessary. Methods for removing carrier interference include synchronous demodulation, envelope detection, and digital filtering. A quadrature demodulation algorithm can be used to demodulate the accelerometer output signal and drive current signal to baseband using two reference signals that are co-frequency, in-phase, and orthogonal to the excitation signal, thereby removing carrier interference.

[0074] After removing the carrier interference, the response amplitude corresponding to each frequency point is obtained. These response amplitudes are arranged in frequency order to generate the response amplitude curve A(f). The A(f) curve reflects the sensitivity characteristics of the accelerometer at different frequencies.

[0075] c. Perform a Hartley transform on the A(f) curve: The signal processing module performs a Hartley transform on the generated response amplitude curve A(f). The Hartley transform is a real-domain integral transform, similar to the Fourier transform, but its result is a real number, making it more computationally efficient and particularly suitable for spectral analysis of real signals.

[0076] Calculate the standard deviation of the frequency domain energy distribution As an indicator of smoothness; standard deviation Characterizes the degree of discreteness of the frequency domain energy distribution. For an ideal accelerometer, its frequency response curve A(f) should be smooth and the frequency domain energy distribution should be concentrated.

[0077] d. The signal processing module uses the dynamic time warping (DTW) algorithm to calculate the morphological similarity between the measured A(f) curve and the preset standard template curve The DTW algorithm can effectively compare the shape similarity between the measured A(f) curve and the ideal frequency response curve. Even if the measured curve has a slight offset or stretch on the frequency axis, its morphological similarity can be accurately evaluated.

[0078] The preset standard template curve represents the shape of an ideal accelerometer's frequency response curve. This curve can be obtained through theoretical simulation, experimental calibration, or historical data statistics. The standard template curve should be smooth, single-peaked, and symmetrical, reflecting the frequency response characteristics of the accelerometer under normal operating conditions.

[0079] Output of the DTW algorithm is a morphological similarity index, and its value range is usually from 0 to 1. The closer the value is to 1, the more similar the shape of the measured A(f) curve is to the preset standard template curve, and the closer the performance of the accelerometer is to the ideal state.

[0080] e. Smoothness index obtained by calculation and morphological similarity , and the preset threshold 、 and reference standard deviation , make fault determination. > or < The accelerometer is judged to be faulty.

[0081] > Indicates that the smoothness of the measured A(f) curve is too low and exceeds the reference standard deviation of times, indicating that the curve has obvious fluctuations or noise, and the accelerometer performance is disturbed or abnormal. is the smoothness threshold coefficient, which ranges from 1.2 to 1.8 and can be adjusted according to the actual application scenario and smoothness requirements.

[0082] < Indicates that the morphological similarity between the measured A(f) curve and the preset standard template curve is too low, below the threshold , indicating that the curve shape is significantly distorted, the frequency response characteristics of the accelerometer deviate from the normal range, and the performance fails. is the similarity threshold coefficient, which ranges from 0.9 to 0.95 and can be adjusted according to the requirements for curve morphology similarity.

[0083] is the preset reference standard deviation, representing the smoothness standard of the normal accelerometer frequency response curve. It can be obtained by statistically analyzing the frequency response curves of a large number of normal accelerometers.

[0084] Step 2 and Step 3: Vibration time domain signal acquisition and parameter extraction:

[0085] This step mainly evaluates the performance of the accelerometer under normal working conditions. By collecting the vibration time domain signal and extracting key parameters such as main frequency energy, harmonic components and signal-to-noise ratio, it is further verified whether the performance of the accelerometer meets expectations. The vibration time domain signal is obtained, and its sampling frequency satisfy ,in is the highest frequency of the oscillator, The coefficient is greater than 2, which is an engineering extension based on the Nyquist sampling theorem.

[0086] Driven by a self-test device, the accelerometer continuously collects vibration time-domain signals. The sampling frequency is set within the 10Hz-20kHz range, depending on the maximum frequency of the oscillator and the required signal bandwidth. The sampling frequency must meet the Nyquist sampling theorem to ensure undistorted signals.

[0087] The signal processing module performs low-pass filtering on the collected time domain signal. The low-pass filter is used to filter out high-frequency noise and interference and improve the signal-to-noise ratio of the signal. > ,Right now =1.2× , which is 1.2 times the maximum frequency of the oscillator under normal operation. If the maximum frequency of the oscillator is 5kHz, the low-pass filter cutoff frequency can be set to 6kHz. The low-pass filter can be a digital filter.

[0088] The signal processing module converts the filtered time domain signal into a frequency domain signal using a fast Fourier transform (FFT) algorithm, and extracts at least one of the main frequency energy, harmonic components and signal-to-noise ratio parameters.

[0089] Parameter extraction:

[0090] Dominant frequency energy: In frequency domain signals, the square of the spectral amplitude corresponding to the dominant frequency represents the dominant frequency energy. This reflects the concentration of the primary energy in the vibration signal. Normally, the dominant frequency energy accounts for the vast majority of the total energy.

[0091] Harmonic components: Harmonic components refer to spectral components at frequencies that are integer multiples of the main frequency. The amplitude of the harmonic components reflects the degree of nonlinear distortion of the vibration signal. Under normal circumstances, the harmonic components should be as small as possible.

[0092] Signal-to-noise ratio (SNR): The SNR is the ratio of the main frequency energy to the noise energy. It reflects the purity of the signal; higher SNR values ​​indicate better signal quality. Noise energy can be estimated as the sum of the energy of the spectral components of the frequency domain signal, excluding the main frequency and its harmonics.

[0093] Step 4: Dynamic threshold adjustment and fault alarm

[0094] This step realizes the intelligence and reliability of the self-test device. By dynamically adjusting the judgment threshold to adapt to changes in environmental parameters, and adopting multiple repeated tests and parameter correction mechanisms, the false positive rate is reduced, ultimately achieving accurate fault alarms and fault information recording.

[0095] The signal processing module dynamically adjusts the judgment threshold according to environmental parameters, such as temperature, humidity, air pressure, and external vibration interference intensity.

[0096] Dynamic threshold adjustment mechanism: A mapping relationship model between environmental parameters and judgment thresholds is pre-established. For example, if rising temperature causes the accelerometer's zero bias to increase, the judgment threshold for the zero bias deviation should be appropriately relaxed. If the intensity of external vibration interference decreases the signal-to-noise ratio, the signal-to-noise ratio threshold should be appropriately relaxed. The mapping relationship model can be obtained through experimental calibration, data analysis, or machine learning algorithms and stored in a calibration database. The signal processing module dynamically adjusts the judgment threshold based on the real-time collected environmental parameters and the mapping relationship model. This dynamic threshold adjustment mechanism can improve the adaptability and accuracy of the self-test device under different environmental conditions and reduce the impact of environmental factors on the self-test results.

[0097] Initial anomaly marking and repeated vibration testing: The signal processing module compares the main frequency energy, signal-to-noise ratio, or harmonic components of the initially collected vibration response data with preset standard parameters. If the deviation exceeds the judgment threshold, it is marked as an initial anomaly and triggers 3-5 repeated vibration tests, each lasting 10-500ms. To reduce misjudgments caused by accidental errors or transient interference, 3-5 repeated vibration tests are triggered after the initial anomaly occurs. The number of repeated tests can be adjusted based on the required false positive rate. Each vibration lasts 10-500ms to ensure sufficient time to collect a stable vibration response signal. Repeated vibration testing verifies the authenticity of the initial anomaly and improves the reliability of fault diagnosis.

[0098] The signal processing module uses the least squares method to fit multiple test data, corrects the zero bias and sensitivity parameters of the accelerometer, and obtains the corrected calibration parameters;

[0099] Least Squares Fitting Parameter Correction: The signal processing module uses the least squares method to fit the vibration response data (e.g., accelerometer output signal, drive current signal, etc.) collected from repeated vibration tests. The goal of the fitting is to correct the accelerometer's zero bias and sensitivity parameters.

[0100] Bias correction: The constant offset (i.e., bias) in the accelerometer output signal is estimated through a fitting algorithm and subtracted from the accelerometer output signal to eliminate the bias error.

[0101] Sensitivity correction: The sensitivity coefficient of the accelerometer is estimated through a fitting algorithm, and the accelerometer output signal is scaled according to the sensitivity coefficient to calibrate the sensitivity error.

[0102] Obtaining the corrected calibration parameters: After the least squares fitting is completed, the corrected zero bias and sensitivity parameters are obtained. These parameters will be used as new calibration parameters for subsequent accelerometer data processing and self-test verification.

[0103] Based on the corrected calibration parameters, repeat steps 1-3 to perform a verification test. The purpose of the verification test is to verify the effect of the parameter correction and confirm whether the corrected accelerometer performance has returned to normal.

[0104] Verification test deviation threshold: The deviation threshold of the verification test is set to 80% of the judgment threshold, which is stricter than the initial abnormality judgment threshold, further reducing the false positive rate and ensuring the accuracy of unrecoverable fault alarms.

[0105] Irrecoverable Fault Alarm Signal Generation: When the signal processing module determines a permanent accelerometer fault, it generates an irrecoverable fault alarm signal. This alarm signal can be output via digital signal output, analog voltage output, or wireless communication, providing fault notification and processing to external systems or users.

[0106] Disable the self-test function: After an unrecoverable fault alarm is generated, the self-test function is disabled. Disabling the self-test function prevents the faulty accelerometer from continuously performing self-tests, which could waste system resources or cause erroneous output. This can be achieved by setting a software flag or disconnecting the hardware circuit.

[0107] Fault type coding and fault information storage: After the unrecoverable fault alarm signal is generated, the fault type is encoded as hardware damage or environmental interference overload, and the fault information is written into the non-erasable storage area of ​​the accelerometer through an encrypted check code.

[0108] Fault type coding: The fault type coding is "hardware damage" or "environmental interference overload".

[0109] Hardware damage: This refers to a malfunction of the accelerometer's internal hardware, such as MEMS sensor failure, circuit chip damage, or component aging. Hardware damage is usually irreversible and requires replacement of the accelerometer.

[0110] Environmental interference overload: This refers to the accelerometer being disturbed by environmental factors outside its operating range, such as excessive external vibration, high / low temperatures, and excessive electromagnetic interference. Environmental interference overload is usually recoverable; when environmental conditions return to normal, the accelerometer performance returns to normal.

[0111] Encryption check code: Use encryption check code (such as CRC check code, MD5 hash value, etc.) to encrypt and check fault information to prevent fault information from being tampered with or misread, and ensure the integrity and reliability of fault information.

[0112] Non-erasable storage area: The fault type code and encrypted checksum are written to the accelerometer's non-erasable storage area (e.g., a specific area of ​​OTPROM or FlashROM). This non-erasable storage area maintains data during power outages and cannot be modified by the user. It permanently records fault information, facilitating subsequent fault tracing and quality analysis.

[0113] A self-test device for an accelerometer includes a first cavity and a second cavity inside a rigid packaging shell; a vibrator and a driving circuit board are fixed in the first cavity; and an accelerometer and a signal processing module are installed in the second cavity in isolation through a metal shielding cover.

[0114] Signal processing module composition: The signal processing module integrates FFT analysis unit and calibration database.

[0115] FFT analysis unit: A hardware FFT accelerator chip or a software FFT algorithm library (such as the CMSIS-FFT library or the FFTW library). The FFT analysis unit is responsible for converting time-domain signals into frequency-domain signals and performing spectrum analysis and parameter extraction.

[0116] Calibration database: Non-volatile memory (such as FlashROM or EEPROM) is used to store calibration data, preset standard parameters, judgment threshold models, fault type coding tables, encryption verification algorithms, and other data. The calibration database can be pre-written with factory calibration data and dynamically updated with calibration parameters and fault information during the self-test process.

[0117] Wireless communication module: An integrated wireless communication module (such as a Wi-Fi module, Bluetooth module, LoRa module, or NB-IoT module) supports receiving external trigger commands and uploading self-test results to a cloud server.

[0118] Receiving external trigger commands: The wireless communication module listens for trigger commands from an external system or cloud server. Trigger commands can be periodic self-test commands, remote manual trigger commands, or event-triggered commands (for example, device startup, abnormal environmental parameters, etc.). After decryption and verification, the received command is passed to the signal processing module to initiate the self-test process.

[0119] Uploading self-test results to the cloud server: After the self-test is complete, the signal processing module uploads the results (including performance parameters, fault status, fault type code, calibration parameters, etc.) to the cloud server via the wireless communication module. The uploaded data uses an encrypted transmission protocol (such as TLS / SSL) to ensure data security. The cloud server can store, analyze, visualize, and generate alerts for the received self-test results, allowing users to remotely monitor the accelerometer's operating status and health.

[0120] Multi-axis collaborative interface: Configure a multi-axis collaborative interface (such as an SPI interface, an I2C interface, or a synchronous trigger signal line) to synchronously trigger the pairwise self-test of the three-axis accelerometers and generate a global fault alarm signal when the self-test results of at least two axes are abnormal.

[0121] Synchronous Triggering of Pairwise Triple-Axis Accelerometer Self-Tests: The self-test mechanism supports simultaneous self-tests on any two axes of a triaxial accelerometer. For example, the X-axis and Y-axis, the Y-axis and Z-axis, and the Z-axis and X-axis can all be tested simultaneously. Synchronous self-tests improve efficiency and reduce the impact of inter-axis interference. Through the multi-axis collaborative interface, external systems can simultaneously trigger multiple accelerometers to perform self-tests, enabling distributed, multi-point accelerometer health monitoring.

[0122] Global fault alarm signal generation: The signal processing module comprehensively analyzes the self-test results of multiple axes. If the self-test results of at least two axes are abnormal (for example, the smoothness index Exceeding the standard or morphological similarity If the accelerometer is below a threshold or the parameter deviation exceeds a judgment threshold, a global fault alarm signal is generated. This global fault alarm signal can be output via a digital signal output, analog voltage output, or wireless communication module, indicating to the external system that the entire accelerometer system is faulty and requires maintenance or replacement.

[0123] When the sensor performs self-test function, it will give a self-test signal. After receiving this signal, the signal processing module will first pre-sample the accelerometer data for a period of time, which is generally 10-10000ms. Figure 2 As shown: The signal processing module starts the oscillator and waits for about 15 milliseconds.

[0124] like Figure 3 and Figure 4 As shown, the collected accelerometer data is compared with the previously collected data. At this time, the signal processing module calculates through the algorithm that the accelerometer is working normally and sends a correct self-test signal.

[0125] like Figure 5 and Figure 6As shown, the signal processing module uses an algorithm to calculate that the accelerometer is not working properly and sends out an erroneous self-test signal. This self-test signal can truly judge the quality of the accelerometer, making it easier and more secure for customers to use.

[0126] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A self-test method for an accelerometer, characterized in that: The following steps are involved: S1. Generate a preset vibration signal through a vibration signal module within the sensor module. The vibration signal module includes a drive circuit and a vibrator. The drive circuit performs a frequency sweep on the vibrator and detects a peak value of the response amplitude of the accelerometer to determine a resonant frequency, which is used as the main frequency of the vibration signal. S2, the accelerometer collects the vibration response data excited by the vibrator to obtain the vibration time domain signal, the sampling frequency of which is satisfy ,in is the highest frequency of the oscillator, is a coefficient greater than 2; S3. Processing the vibration time domain signal to obtain characteristic parameters, including: S31, low-pass filter the time domain signal, cutoff frequency > ; S32, converting the filtered time domain signal into a frequency domain signal, and extracting at least one of the main frequency energy, harmonic component, and signal-to-noise ratio as a characteristic parameter; S4. judging the working state of the accelerometer based on the comparison result of the characteristic parameter and the preset standard parameter; Step S4 specifically includes: S41, dynamically adjusting the judgment threshold according to environmental parameters; S42. When the characteristic parameter exceeds the judgment threshold for the first time, it is marked as a first abnormality and triggers 3-5 repeated vibration tests, each vibration lasting 10-500ms; S43, using the least squares method to fit the multiple test data, correcting the zero bias and sensitivity parameters of the accelerometer to obtain corrected calibration parameters; S44. Based on the calibration parameters, re-execute steps S1-S3 to perform a verification test.

2. The self-test method of an accelerometer according to claim 1, characterized in that: The sampling frequency The value range is 10Hz-20kHz; The cut-off frequency =1.2× ; When the characteristic parameter deviation of the verification test exceeds 80% of the judgment threshold, an unrecoverable fault alarm signal is generated and the self-test function is disabled.

3. The self-test method of an accelerometer according to claim 2, characterized in that: The step S1 also includes detecting the actual vibration amplitude and frequency of the vibrator through a feedback loop, and adjusting the PWM duty cycle or voltage amplitude output by the driving circuit in real time so that the frequency error of the actual vibration signal is less than 3% and the amplitude error is less than 5%.

4. The self-test method of an accelerometer according to claim 3, characterized in that: The step S1 includes performing a frequency sweep on the vibrator within the range of 50 Hz-5 kHz.

5. The self-test method of an accelerometer according to claim 3, characterized in that: The feedback loop includes: The driving current phase is detected by a current sensor connected in series in the vibrator driving circuit. Combined with the vibration time domain signal output by the accelerometer, the actual vibration frequency is calculated and compared with the preset frequency to dynamically adjust the duty cycle of the driving signal.

6. The self-test method of an accelerometer according to claim 4, characterized in that: After the frequency scan further includes: a. At resonant frequency As the center, the logarithmic frequency interval is 0.8 -1.2 Sine sweep excitation is performed within the range of Dynamic adjustment, where is the amplitude of the excitation signal; is the resonant frequency Corresponding to the peak driving voltage; is the frequency of the excitation signal; b. Synchronously collect the differential output signal of the accelerometer and the phase difference of the driving current to generate the response amplitude curve A(f) after removing the carrier interference; c. Perform Hartley transform on the A(f) curve and calculate the standard deviation of the frequency domain energy distribution as an indicator of smoothness; d. Use the dynamic time warping algorithm to calculate the morphological similarity between the measured A(f) curve and the preset standard template curve ; e. When satisfied > or < When , it is determined that there is an abnormality in the damping material of the accelerometer, where 1.2-1.8; 0.9-0.95; is the preset reference standard deviation.

7. The self-test method of an accelerometer according to claim 3, characterized in that: After an unrecoverable fault alarm signal is generated, the fault type is encoded as hardware damage or environmental interference overload, and the fault information is written into a non-erasable storage area of ​​the accelerometer through an encrypted check code.

8. A self-testing device for an accelerometer for implementing the method according to any one of claims 1 to 7, characterized in that: include: A rigid packaging shell, the interior of which is divided into a first cavity and a second cavity; The vibrator and the driving circuit board are fixed in the first cavity; The second cavity is isolated and installed with an accelerometer and a signal processing module through a metal shielding cover; The driving circuit board includes an H-bridge circuit and a closed-loop control chip, and the signal processing module integrates an FFT analysis unit and a calibration database.

9. The self-test device of an accelerometer according to claim 8, characterized in that: Also includes: Wireless communication module, supporting receiving external trigger commands and uploading self-test results to the cloud server; The multi-axis collaborative interface is configured to synchronously trigger the self-test of the three-axis accelerometers in pairs, and generate a global fault alarm signal when the self-test results of at least two axes are abnormal.

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