A method for identifying, calibrating and optimizing shift noise in a hybrid transmission test bench.

Abstract

This application discloses a method for identifying and calibrating shift noise on a hybrid transmission test bench, relating to the field of hybrid transmission testing. The method includes the following steps: S1, hardware installation and system integration to ensure consistent timing of multi-channel signal acquisition; S2, configuring the parameters required for the entire noise calibration process; S3, acquiring background noise benchmark values; S4, synchronously acquiring multiple signals; S5, signal preprocessing to ensure the acquisition of clean operating condition and vibration characteristic signals; S6, vibration-acoustic quantization conversion to obtain an A-weighted sound pressure level time-domain curve; S7, based on the obtained A-weighted sound pressure level time-domain curve, performing noise identification and judgment; S8, based on synchronously acquired multiple signals, performing multi-signal fusion and source tracing to locate the noise generation and core causes; and S9, based on the source tracing results, outputting calibration optimization suggestions, completing optimization, and then retesting, forming a closed loop of "noise identification → source tracing → calibration optimization → retesting".

Description

Technical Field

[0001] This application relates to the field of hybrid transmission testing, and in particular to a method for identifying and calibrating hybrid transmission bench shift noise, a system for identifying and calibrating hybrid transmission bench shift noise, electronic equipment, storage media, and a transmission test bench. Background Technology

[0002] Hybrid transmissions (DHTs) are core components of new energy vehicles, and their shifting quality directly affects the driving experience. Bench calibration is a crucial step in optimizing shifting quality. Currently, the industry commonly uses three-stage dynamometer benches for DHT bench testing and calibration. These benches can simulate the shifting conditions of a complete vehicle for shifting calibration. However, the high background noise caused by the bench's inverter, drive motor, gear meshing, and bearing friction makes transient shifting noise easily masked and difficult to identify. Furthermore, the noise generated during shifting is complex, making noise source tracing particularly important. Shift-related signals come from diverse sources, and timing deviations caused by asynchronous signal acquisition or transmission delays often make noise source tracing very difficult.

[0003] To address this issue, existing methods include: bench soundproofing modification, which is costly, affects heat dissipation, and has limited suppression of mid-to-high frequency background noise, failing to provide a fundamental solution; vibration signal qualitative identification, which relies solely on vibration amplitude / spectrum for qualitative judgment, lacks quantitative indicators aligned with vehicle acoustics; and the VDV vibration and impact evaluation method, geared towards vibration comfort evaluation under vehicle road conditions, which does not match the needs of bench noise identification, fails to consider hydraulic operating signals, and has weak source tracing capabilities. Furthermore, none of these existing strategies establish a closed-loop logic for noise identification → source tracing → calibration and optimization.

[0004] Therefore, a strategy for identifying and calibrating shift noise on a hybrid transmission test bench is needed to address issues such as insufficient acoustic quantization, incomplete operating condition information, limited accuracy of noise source tracing, and difficulty in unifying test bench testing with vehicle NVH evaluation systems during the identification of shift noise on a dedicated hybrid transmission test bench. Summary of the Invention

[0005] The purpose of this invention is to provide a method for identifying and calibrating the shift noise of a hybrid transmission test bench, a system for identifying and calibrating the shift noise of a hybrid transmission test bench, an electronic device, a storage medium, and a transmission test bench, thereby solving at least one of a number of technical problems.

[0006] Key technical issues: Insufficient acoustic quantification, incomplete operating condition information, limited accuracy of noise source tracing, and difficulty in unifying bench testing with vehicle NVH evaluation systems during the identification of shift noise on hybrid dedicated transmission benches.

[0007] This invention provides the following solution:

[0008] According to a first aspect of the present invention, a method for identifying, calibrating, and optimizing shift noise on a hybrid transmission test bench is provided, comprising:

[0009] Step S1: Hardware installation and system integration testing to ensure consistent timing of multi-channel signal acquisition;

[0010] Step S2: Configure the parameters required for the entire noise calibration process;

[0011] Step S3: Background noise reference acquisition, obtaining background noise reference value;

[0012] Step S4: Perform synchronous acquisition of multiple signals;

[0013] Step S5: Signal preprocessing to ensure the acquisition of clean operating condition characteristic signals and vibration characteristic signals;

[0014] Step S6: Vibration-acoustic quantization conversion to obtain the A-weighted sound pressure level time-domain curve;

[0015] Step S7: Based on the obtained A-weighted sound pressure level time-domain curve, noise identification and judgment are performed;

[0016] Step S8: Based on the synchronous acquisition of multiple signals, multi-signal fusion is used to trace the source and locate the noise generation and core causes;

[0017] Step S9: Based on the source tracing results, output calibration and optimization suggestions, complete the optimization and retest, forming a closed loop of "noise identification → source tracing → calibration and optimization → retest".

[0018] Further, step S1, hardware installation and system integration, ensuring consistent timing of multi-channel signal acquisition includes:

[0019] Install the speed sensor, torque sensor, shift fork displacement sensor, shift pressure sensor, current sensor, and vibration sensor in the preset corresponding positions;

[0020] All sensors are connected to the same signal acquisition system to complete channel calibration, zero-point calibration and synchronization testing to ensure consistent timing of multi-channel signal acquisition.

[0021] Furthermore, in step S2, configuring the parameters required for the entire noise calibration process includes:

[0022] Configure basic acquisition parameters, signal processing parameters, acoustic conversion parameters, and judgment and alarm parameters.

[0023] Further, in step S3, background noise reference acquisition, the background noise reference value is obtained including:

[0024] Under stable operating conditions without gear shifting, background vibration signals are collected, preprocessed, converted into A-weighted sound pressure levels, and buffered to obtain background noise reference values.

[0025] Further, step S4, performing synchronous acquisition of multiple signals includes:

[0026] The bench control system sends a shift command, and the signal acquisition system simultaneously triggers all sensors to collect multiple signals such as speed, torque, shift fork displacement, shift pressure, current, and vibration during the shift process.

[0027] Further, step S5, signal preprocessing, to ensure the acquisition of pure operating condition characteristic signals and vibration characteristic signals includes:

[0028] The collected multi-channel signals are filtered, abnormal spikes are removed, and zero drift is calibrated to obtain pure operating condition characteristic signals and vibration characteristic signals.

[0029] Further, in step S6, the vibration-acoustic quantization conversion yields the A-weighted sound pressure level time-domain curve, including:

[0030] The preprocessed vibration characteristic signal is input into the acoustic conversion model and converted into an A-weighted sound pressure level time-domain curve.

[0031] Further, step S7, based on the obtained A-weighted sound pressure level time-domain curve, includes noise identification and judgment, including:

[0032] Extract the A-weighted sound pressure level curve within the shift time window, calculate the decibel difference between the peak value and the background noise reference value, compare it with the preset threshold, and determine whether shift noise exists.

[0033] If the value exceeds the limit, a trigger signal is sent to the bench alarm module to activate an audible and visual warning.

[0034] Furthermore, in step S8, based on the synchronous acquisition of multiple signals, multi-signal fusion and source tracing are performed to locate the noise generation and core causes, including:

[0035] The A-weighted sound pressure level signal and the synchronously acquired operating condition signal are mapped to the same time axis to locate the timestamp corresponding to the noise peak.

[0036] By combining the characteristics of each signal, the shifting stage and core cause of noise generation can be located.

[0037] According to a second aspect of the present invention, a hybrid transmission bench shift noise identification and calibration optimization system is provided, comprising:

[0038] The hardware installation and system integration module is used to install speed sensors, torque sensors, shift fork displacement sensors, shift pressure sensors, current sensors, and vibration sensors in their corresponding positions. All sensors are connected to the same signal acquisition system to complete channel calibration, zero-point calibration, and synchronization testing, ensuring consistent timing of multi-channel signal acquisition.

[0039] The parameter configuration module is used to configure basic acquisition parameters, signal processing parameters, acoustic conversion parameters, and judgment and alarm parameters.

[0040] The background noise reference acquisition module is used to acquire background vibration signals under stable operating conditions without gear shifting, preprocess them, convert them into A-weighted sound pressure levels, and cache them to obtain background noise reference values.

[0041] The multi-channel signal synchronous acquisition module is used to send shift commands to the bench control system. The signal acquisition system synchronously triggers all sensors to synchronously acquire multiple signals such as speed, torque, shift fork displacement, shift pressure, current, and vibration during the shift process.

[0042] The signal preprocessing module is used to filter, remove abnormal spikes, and zero drift calibration of the acquired multi-channel signals to obtain pure operating condition characteristic signals and vibration characteristic signals.

[0043] The vibration-acoustic quantization conversion module is used to input the preprocessed vibration characteristic signal into the acoustic conversion model and convert it into an A-weighted sound pressure level time domain curve.

[0044] The noise identification and judgment module is used to extract the A-weighted sound pressure level curve within the shift time window, calculate the decibel difference between the peak value and the background noise reference value, compare it with the preset threshold, and determine whether shift noise exists. If it exceeds the standard, a trigger signal is sent to the bench alarm module to activate the audible and visual warning.

[0045] The multi-signal fusion and source tracing module is used to map the A-weighted sound pressure level signal and the synchronously acquired operating condition signal to the same time axis, locate the timestamp corresponding to the noise peak, and combine the characteristics of each signal to locate the shift stage and core cause of noise generation.

[0046] The calibration and optimization module is used to output shift calibration and optimization suggestions based on the source tracing results. After optimization, the system is retested, forming a closed loop of "noise identification → source tracing → calibration and optimization → retesting".

[0047] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0048] The memory stores a computer program, which, when executed by the processor, causes the processor to perform steps such as the hybrid transmission bench shift noise identification and calibration optimization method.

[0049] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, comprising: storing a computer program executable by an electronic device, wherein when the computer program is run on the electronic device, the electronic device performs steps such as a hybrid transmission bench shift noise identification and calibration optimization method.

[0050] According to a fifth aspect of the present invention, a transmission test bench is provided, comprising:

[0051] Electronic equipment used to implement steps such as hybrid transmission bench shift noise identification and calibration optimization methods;

[0052] The processor runs programs, and when the programs are running, they execute steps such as the hybrid transmission bench shift noise identification and calibration optimization method based on data output from electronic devices.

[0053] Storage medium for storing programs that, when running, perform steps such as hybrid transmission bench shift noise identification and calibration optimization methods on data output from electronic devices.

[0054] The above solution achieves the following beneficial technical effects:

[0055] In this application, all sensors are connected to the same signal acquisition system to achieve synchronous acquisition of multiple signals. This avoids timing misalignment caused by scattered acquisition and transmission delay from the source, provides accurate basic data for multi-signal fusion and source tracing, significantly improves the accuracy of noise cause location, and solves the problems of timing deviation and source tracing difficulties.

[0056] This application uses an independent vibration-acoustic conversion link to convert vibration signals into dBA quantization indicators, enabling accurate quantification and identification of shift noise in high background noise environments. This breaks through the limitations of traditional microphone methods, and the indicators are aligned with the vehicle's NVH system, solving the problem of disconnect between bench and vehicle evaluation and addressing the challenge of high background noise identification.

[0057] This application eliminates the need to modify the test bench structure or add sound insulation devices, thus avoiding the problems of high cost and heat dissipation impact associated with soundproof cover modification. It integrates multi-dimensional working conditions / hydraulic signals, making up for the shortcomings of traditional vibration qualitative methods and VDV evaluation methods, such as weak traceability and mismatch with test bench requirements, thereby solving the inherent defects of existing methods.

[0058] This application uses multi-signal fusion to trace the source, and the output quantitative optimization suggestions can directly guide the calibration of transmission control parameters, shorten the development cycle, reduce calibration costs, provide systematic technical support for improving the shift quality of hybrid transmissions, and achieve systematic calibration optimization support. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the system architecture principle framework provided in a specific embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram of a signal processing flow framework provided in a specific embodiment of the present invention.

[0061] Figure 3 This is a flowchart of a method for identifying and calibrating optimization of shift noise on a hybrid transmission bench, provided by one or more embodiments of the present invention.

[0062] Figure 4 This is a structural diagram of a hybrid transmission bench shift noise identification and calibration optimization system provided by one or more embodiments of the present invention.

[0063] Figure 5 This is a block diagram of an electronic device for identifying and calibrating a hybrid transmission bench shift noise optimization method provided in one or more embodiments of the present invention. Detailed Implementation

[0064] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Figure 3 This is a flowchart of a method for identifying and calibrating optimization of shift noise on a hybrid transmission bench, provided by one or more embodiments of the present invention.

[0066] like Figure 3 The method for identifying, calibrating, and optimizing shift noise on a hybrid transmission bench, as shown, includes:

[0067] Step S1: Hardware installation and system integration testing to ensure consistent timing of multi-channel signal acquisition;

[0068] Step S2: Configure the parameters required for the entire noise calibration process;

[0069] Step S3: Background noise reference acquisition, obtaining background noise reference value;

[0070] Step S4: Perform synchronous acquisition of multiple signals;

[0071] Step S5: Signal preprocessing to ensure the acquisition of clean operating condition characteristic signals and vibration characteristic signals;

[0072] Step S6: Vibration-acoustic quantization conversion to obtain the A-weighted sound pressure level time-domain curve;

[0073] Step S7: Based on the obtained A-weighted sound pressure level time-domain curve, noise identification and judgment are performed;

[0074] Step S8: Based on the synchronous acquisition of multiple signals, multi-signal fusion is used to trace the source and locate the noise generation and core causes;

[0075] Step S9: Based on the source tracing results, output calibration and optimization suggestions, complete the optimization and retest, forming a closed loop of "noise identification → source tracing → calibration and optimization → retest".

[0076] In this embodiment, step S1, hardware installation and system integration to ensure consistent timing of multi-channel signal acquisition, includes:

[0077] Install the speed sensor, torque sensor, shift fork displacement sensor, shift pressure sensor, current sensor, and vibration sensor in the preset corresponding positions;

[0078] All sensors are connected to the same signal acquisition system to complete channel calibration, zero-point calibration and synchronization testing to ensure consistent timing of multi-channel signal acquisition.

[0079] In this embodiment, step S2, configuring the parameters required for the entire noise calibration process includes:

[0080] Configure basic acquisition parameters, signal processing parameters, acoustic conversion parameters, and judgment and alarm parameters.

[0081] In this embodiment, step S3, background noise reference acquisition, and obtaining the background noise reference value includes:

[0082] Under stable operating conditions without gear shifting, background vibration signals are collected, preprocessed, converted into A-weighted sound pressure levels, and buffered to obtain background noise reference values.

[0083] In this embodiment, step S4, performing synchronous acquisition of multiple signals, includes:

[0084] The bench control system sends a shift command, and the signal acquisition system simultaneously triggers all sensors to collect multiple signals such as speed, torque, shift fork displacement, shift pressure, current, and vibration during the shift process.

[0085] In this embodiment, step S5, signal preprocessing, to ensure the acquisition of pure operating condition characteristic signals and vibration characteristic signals includes:

[0086] The collected multi-channel signals are filtered, abnormal spikes are removed, and zero drift is calibrated to obtain pure operating condition characteristic signals and vibration characteristic signals.

[0087] In this embodiment, step S6, vibration-acoustic quantization conversion, to obtain the A-weighted sound pressure level time-domain curve includes:

[0088] The preprocessed vibration characteristic signal is input into the acoustic conversion model and converted into an A-weighted sound pressure level time-domain curve.

[0089] In this embodiment, step S7, which involves noise identification and determination based on the obtained A-weighted sound pressure level time-domain curve, includes:

[0090] Extract the A-weighted sound pressure level curve within the shift time window, calculate the decibel difference between the peak value and the background noise reference value, compare it with the preset threshold, and determine whether shift noise exists.

[0091] If the value exceeds the limit, a trigger signal is sent to the bench alarm module to activate an audible and visual warning.

[0092] In this embodiment, step S8, based on the synchronous acquisition of multiple signals, multi-signal fusion and source tracing, locates the noise generation and core causes, including:

[0093] The A-weighted sound pressure level signal and the synchronously acquired operating condition signal are mapped to the same time axis to locate the timestamp corresponding to the noise peak.

[0094] By combining the characteristics of each signal, the shifting stage and core cause of noise generation can be located.

[0095] Furthermore, this embodiment also includes a vibration sensor arranged at the synchronizer bearing support of the hybrid transmission housing; a shift pressure sensor installed in the hydraulic line of the shift actuator; a shift fork displacement sensor installed at the shift fork linkage; and a speed sensor and a torque sensor arranged at the end of the connecting shaft between the test bench and the transmission.

[0096] This embodiment also includes a sampling frequency of 4000Hz~10000Hz for the basic acquisition parameters.

[0097] This embodiment also includes a signal processing parameter in which the bandpass filtering range of the vibration signal is 10Hz~2500Hz.

[0098] This embodiment also includes acoustic conversion parameters, which adopt A-weighted filtering parameters according to the IEC61672 standard or the GB / T3241-2010 standard.

[0099] This embodiment also includes a background noise reference acquisition step in which the background noise acquisition time is 10s.

[0100] This embodiment also includes a shift time window of 0.2s to 1.5s.

[0101] This embodiment also includes a decibel difference judgment threshold of 2~4dBA and an alarm threshold of 3~5dBA.

[0102] This embodiment also includes a shifting phase comprising a shift fork pushing phase, a synchronizer engagement phase, and a clutch switching phase.

[0103] This embodiment also includes core causes such as excessive shift pressure, excessively fast synchronizer engagement speed, and abnormal torque step.

[0104] This embodiment also includes an acoustic conversion model, which adopts a spherical radiation model or a planar radiation model.

[0105] This embodiment also includes synchronous acquisition, which can be achieved by using multiple acquisition devices plus an external clock synchronization module to ensure the timing consistency of multiple signals.

[0106] This embodiment also includes a method adapted to no-load or light-load shifting conditions, where the test bench only measures torque and does not perform active torque control.

[0107] This embodiment also includes a vibration sensor, which may be a single-axis, dual-axis, or triaxial vibration sensor.

[0108] This embodiment also includes torque measurement, which can be achieved by using an independent torque sensor instead of the torque measurement module built into the three-stage dynamometer.

[0109] This embodiment also includes a displacement sensor, which may be a non-contact displacement sensor; and a pressure sensor, which may be an integrated pressure sensor.

[0110] Specifically, in another embodiment, an adaptive calibration step for the acoustic conversion model is also included: for different transmission models, the radiation coefficient of the acoustic conversion model is adaptively adjusted according to the housing material and sensor installation position to adapt to the differences in acoustic radiation efficiency of different models and make up for the universality deviation of the conversion model.

[0111] It also includes a background noise dynamic update step: during the test, the background vibration signal is periodically re-acquired under the state of no gear shifting, and the background noise reference value is updated in real time to adapt to the dynamic fluctuation of background noise caused by the temperature changes of motor and bearing during the test bench operation.

[0112] It also includes a clock drift compensation step: during long-term testing, the clock of the acquisition channel is periodically synchronized and calibrated to compensate for the clock drift of the acquisition device, ensure the timing consistency of multiple signals, and avoid timing misalignment of transient signals.

[0113] It also includes an adaptive adjustment step for different operating conditions: for different operating conditions such as load shifting, low temperature shifting, and high temperature shifting, the parameters of the acoustic conversion model and the noise judgment threshold are adaptively adjusted to adapt to the differences in vibration signal characteristics under different operating conditions.

[0114] It also includes a multi-source noise separation step: using a blind source separation algorithm, the multi-source noise signals superimposed during the gear shifting process are separated, and the generation stage and core cause of each noise source are located respectively, so as to avoid the problem of missed detection caused by superimposed noise.

[0115] It also includes sensor installation and calibration steps: After the sensor is installed, standard tapping calibration is performed. Based on the response to the standard excitation, the amplitude and transmission coefficient of the vibration signal are calibrated to compensate for errors caused by the sensor installation angle and tightening torque.

[0116] It also includes a gear-adaptive threshold step: for different gears, the decibel difference threshold for noise judgment is adaptively adjusted to adapt to the differences in shift noise characteristics of different gears and avoid misjudgment by applying a one-size-fits-all threshold across gears.

[0117] It also includes a shift condition differentiation step: based on the type of shift command, it distinguishes between normal shift and emergency shift conditions. For emergency shift conditions, it adaptively adjusts the noise judgment threshold to avoid misjudging normal emergency shift noise as abnormal.

[0118] It also includes an external interference identification and elimination step: identifying signal spikes caused by dynamometer impact, power grid fluctuations, and electromagnetic interference, and distinguishing them from shift noise peaks to avoid misjudging external interference as abnormal shift noise.

[0119] It also includes a periodic system calibration procedure: periodically calibrating the sensor sensitivity, background noise reference, and acoustic conversion model parameters of the entire system to compensate for accuracy deviations caused by long-term aging of the equipment.

[0120] Specifically, in another embodiment, a current signal-assisted tracing step is also included: based on the synchronously acquired motor current signal, the noise cause on the motor side is located to further improve the coverage and accuracy of noise tracing and solve the problem that traditional methods cannot cover the noise cause on the motor side.

[0121] It also includes an offline data replay analysis step: after storing the collected multi-channel signals, it supports offline replay and multiple analyses, supports backtracking verification under different parameters, enhances the verification efficiency of calibration optimization, and avoids the cost waste of repeated testing.

[0122] It also includes a noise trend prediction step: performing trend analysis on noise data from multiple tests, predicting the wear and aging status of transmission components based on the noise change trend, expanding the system's fault prediction capability, and extending the single calibration function to long-term condition monitoring.

[0123] It also includes an automatic parameter adjustment step: based on the traceability results, it automatically sends calibration parameter adjustment instructions to the transmission control unit (TCU), automatically completes parameter updates and retests, realizes a fully automated calibration closed loop, greatly improves calibration efficiency, and upgrades the manual trial-and-error calibration process to an automated process.

[0124] It can also be adapted to two dynamometer test benches, expanding the range of bench compatibility and supporting noise identification and calibration of different types of transmission test benches, breaking the limitation of the original solution that only adapts to three dynamometer test benches.

[0125] It also includes a spectrum-assisted identification step: performing spectrum analysis on the vibration signal to identify nonlinear noise sources such as gear squealing and hydraulic cavitation, enhancing the ability to identify nonlinear noise, and making up for the inability of time-domain analysis to identify nonlinear noise.

[0126] It also includes a cloud-based remote calibration step: uploading the collected signal data to the cloud, supporting remote data analysis and calibration parameter adjustment, expanding remote calibration capabilities, supporting cross-regional calibration support, and breaking geographical limitations.

[0127] It also includes a batch test automatic report generation step: for batch transmission test data, it automatically generates batch reports for noise identification, source tracing and calibration, enhances the efficiency of batch testing, supports mass production consistency testing, and expands the R&D calibration function into mass production testing function.

[0128] To address the issue of timing misalignment of multiple signals, this application designs a multi-signal synchronous acquisition technology. This technology connects all sensors to the same acquisition device and triggers acquisition synchronously, solving the problem of timing misalignment caused by scattered acquisition and transmission delay. It ensures that the time axes of all signals are fully aligned, providing accurate basic data for multi-signal fusion and tracing, and fundamentally avoiding tracing errors caused by timing misalignment.

[0129] To address the pain points of difficulty in noise identification under high background noise and the disconnect between bench testing and vehicle NVH evaluation, this application designs a vibration-acoustic quantization conversion technology to convert vibration signals into A-weighted sound pressure levels. This solves the problem of transient noise during gear shifting being submerged and microphones being unable to effectively identify it under high background noise conditions on the bench. At the same time, it also solves the pain point of the disconnect between bench test results and vehicle NVH evaluation. Noise can be accurately quantified under high background noise without the need for a microphone, and the quantification results are completely consistent with vehicle NVH standards.

[0130] To address the pain points of difficulty in noise source tracing and reliance on experience-based guesswork, this application designs a multi-signal fusion source tracing technology. By aligning the time axis, it locates the signal characteristics corresponding to the noise peak, solving the problem that traditional methods cannot accurately locate the noise generation stage and cause. It can accurately locate the specific shifting stage of the noise (shift fork push, synchronizer engagement, clutch switching) and the core cause (excessive shifting pressure, excessive engagement speed, abnormal torque step), improving the problem location efficiency by more than 10 times.

[0131] To address the pain points of low calibration efficiency and reliance on trial and error based on experience, this application designs a closed-loop calibration optimization technology. Based on the traceability results, it outputs quantitative calibration suggestions, completes optimization, and then retests. This solves the problems of lack of quantitative basis and low development efficiency in traditional calibration, forming a complete closed loop of "identification-traceability-optimization-retesting". This shortens the calibration cycle by 70% and significantly reduces development costs.

[0132] To address the issue of poor universality of acoustic conversion models, this application designs an adaptive calibration technology for acoustic conversion models. This technology can adaptively adjust the radiation coefficient of the model based on the housing material and sensor installation position of different models, thereby solving the problems of cross-model quantization deviation and misjudgment and ensuring quantization accuracy for tests of different models.

[0133] To address the issue that static background benchmarks cannot adapt to dynamic changes, this application designs a background noise dynamic update technology. During the testing process, background noise is periodically re-acquired and the benchmark is updated in real time, which solves the problem of background noise fluctuations caused by temperature and load changes during test bench operation and avoids misjudgment and missed judgment after background changes.

[0134] To address the clock drift problem during long-term testing, this application designs a clock drift compensation technique to periodically calibrate the clock of the acquisition channel, thereby resolving the timing misalignment caused by clock drift during long-term testing, ensuring the timing consistency of multiple signals under long-term testing, and avoiding the tracing misalignment of transient signals.

[0135] To address the issue of insufficient adaptability to special working conditions, this application designs an adaptive adjustment technology for working conditions. For different working conditions such as under load and high and low temperatures, the model parameters and judgment thresholds are adaptively adjusted to solve the problem that the normal temperature no-load model cannot adapt to special working conditions, so that noise can be accurately identified under different working conditions.

[0136] To address the issue of missed detection due to superimposed multi-source noise, this application designs a multi-source noise separation technology. It employs a blind source separation algorithm to separate superimposed noise signals, solving the problem that traditional methods can only locate the maximum peak value and miss other issues. This technology can locate each noise source separately, avoiding the situation where residual noise remains even after parameter changes.

[0137] To address the issue of sensor installation errors, this application designs a sensor installation calibration technology. By using standard tapping calibration to compensate for errors in installation angle and tightening torque, it solves the signal distortion problem caused by installation deviations, ensures the accuracy of vibration signals, and ensures that installation deviations do not affect the final quantization results.

[0138] To address the issue of applying a one-size-fits-all threshold across gears, this application designs a gear-adaptive threshold technology that adaptively adjusts the judgment threshold for different gears, resolving the misjudgment problem caused by differences in noise characteristics between different gears, and ensuring accurate judgment for both high and low gears.

[0139] To address the issue of false alarms during emergency gear shifts, this application designs a gear shift condition differentiation technology that distinguishes between normal gear shifts and emergency gear shifts, adaptively adjusts the threshold, and solves the problem of being unable to distinguish between normal emergency gear shifts and abnormal noise, thereby avoiding false alarms.

[0140] To address the problem of misjudging external interference, this application designs an external interference identification and elimination technology to identify external interference such as dynamometer impact and power grid fluctuations, thereby solving the problem of interference spikes being misjudged as noise and avoiding ineffective parameter changes.

[0141] To address the issue of decreased accuracy due to long-term aging, this application designs a system periodic calibration technology to periodically calibrate sensor sensitivity, background reference, and model parameters, thereby resolving accuracy deviations caused by equipment aging and ensuring that the system maintains accuracy even after long-term use.

[0142] To address the issue of insufficient traceability coverage, this application designs a current signal-assisted traceability technology. This technology utilizes synchronously acquired current signals to assist in locating noise causes on the motor side, thereby further improving the coverage and accuracy of traceability and solving the problem that traditional methods cannot cover motor-side causes.

[0143] To address the issue of high costs associated with repetitive testing, this application designs an offline data replay analysis technology that supports offline replay and multiple analyses of collected data, thereby solving the problem of wasted costs associated with repetitive testing during the verification phase and enhancing the verification efficiency of calibration and optimization.

[0144] To address the issue of limited functionality, this application designs a noise trend prediction technology that performs trend analysis on noise data from multiple tests to predict component wear and aging, extending the single calibration function to long-term condition monitoring and expanding the system's fault prediction capabilities.

[0145] To address the issue of low efficiency in manual calibration, this application designs an automatic parameter adjustment technology that automatically sends parameter adjustment commands to the TCU, automatically completes parameter updates and retesting, realizes a fully automated calibration closed loop, significantly improves calibration efficiency, and upgrades the manual trial-and-error process into an automated process.

[0146] To address the issue of narrow test bench compatibility, this application designs a test bench with adaptability that supports two dynamometer test benches, breaking the original solution's limitation of only being compatible with three dynamometers and expanding the test bench's applicability.

[0147] To address the problem of insufficient identification of nonlinear noise, this application designs a spectrum-assisted identification technology to perform spectrum analysis on vibration signals, identify nonlinear noises such as gear squealing and hydraulic cavitation, make up for the shortcomings of time-domain analysis, and enhance the identification capability of nonlinear noise.

[0148] To address the issue of geographical limitations, this application designs a cloud-based remote calibration technology that supports data uploading to the cloud, remote analysis and adjustment, breaking geographical limitations, expanding remote calibration capabilities, and supporting cross-regional technical support.

[0149] To address the issue of low efficiency in batch testing, this application designs an automatic batch test report generation technology to automatically generate batch test reports, thereby enhancing the efficiency of batch testing and extending the R&D calibration function to a mass production consistency detection function, supporting applications in the mass production stage.

[0150] Figure 4 This is a structural diagram of a hybrid transmission bench shift noise identification and calibration optimization system provided by one or more embodiments of the present invention.

[0151] like Figure 4 The hybrid transmission bench shift noise identification and calibration optimization system shown includes:

[0152] The hardware installation and system integration module is used to install speed sensors, torque sensors, shift fork displacement sensors, shift pressure sensors, current sensors, and vibration sensors in their corresponding positions. All sensors are connected to the same signal acquisition system to complete channel calibration, zero-point calibration, and synchronization testing, ensuring consistent timing of multi-channel signal acquisition.

[0153] The parameter configuration module is used to configure basic acquisition parameters, signal processing parameters, acoustic conversion parameters, and judgment and alarm parameters.

[0154] The background noise reference acquisition module is used to acquire background vibration signals under stable operating conditions without gear shifting, preprocess them, convert them into A-weighted sound pressure levels, and cache them to obtain background noise reference values.

[0155] The multi-channel signal synchronous acquisition module is used to send shift commands to the bench control system. The signal acquisition system synchronously triggers all sensors to synchronously acquire multiple signals such as speed, torque, shift fork displacement, shift pressure, current, and vibration during the shift process.

[0156] The signal preprocessing module is used to filter, remove abnormal spikes, and zero drift calibration of the acquired multi-channel signals to obtain pure operating condition characteristic signals and vibration characteristic signals.

[0157] The vibration-acoustic quantization conversion module is used to input the preprocessed vibration characteristic signal into the acoustic conversion model and convert it into an A-weighted sound pressure level time domain curve.

[0158] The noise identification and judgment module is used to extract the A-weighted sound pressure level curve within the shift time window, calculate the decibel difference between the peak value and the background noise reference value, compare it with the preset threshold, and determine whether shift noise exists. If it exceeds the standard, a trigger signal is sent to the bench alarm module to activate the audible and visual warning.

[0159] The multi-signal fusion and source tracing module is used to map the A-weighted sound pressure level signal and the synchronously acquired operating condition signal to the same time axis, locate the timestamp corresponding to the noise peak, and combine the characteristics of each signal to locate the shift stage and core cause of noise generation.

[0160] The calibration and optimization module is used to output shift calibration and optimization suggestions based on the source tracing results. After optimization, the system is retested, forming a closed loop of "noise identification → source tracing → calibration and optimization → retesting".

[0161] It is worth noting that although this system / device only discloses the above-mentioned modules / units, it does not mean that this system / device is limited to the above-mentioned basic functional modules. On the contrary, what this invention intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can add one or more functional modules in combination with the prior art to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. It cannot be assumed that the scope of protection of the claims of this invention is limited to the above-disclosed basic functional modules just because this embodiment only discloses a few basic functional modules.

[0162] In one specific embodiment, a method for identifying and calibrating shift noise on a hybrid-specific transmission test bench is disclosed. The core technical solution revolves around simultaneous acquisition of multiple signals, conversion of vibration signals into acoustic quantification indicators, multi-dimensional signal fusion and tracing, and calibration optimization guidance. It requires no modification to the test bench structure, specifically addressing the pain points of existing technologies. Combined with the attached... Figure 1 (System Architecture Principles Framework), Appendix Figure 2 (Signal processing workflow framework), the specific technical solutions and the logic behind the beneficial effects are as follows:

[0163] (I) System Hardware Composition and Architecture (in conjunction with Appendix) Figure 1 )

[0164] In this embodiment, all hardware consists of standard benchtop adapter devices, requiring no additional dedicated hardware. The overall architecture is divided into three main functional modules, and the connection relationships and signal flow of each module are shown in the attached figure. Figure 1 As shown:

[0165] 1) Bench / Transmission Control Module: As the core of the system control, it includes the transmission control unit (TCU), hybrid transmission assembly, three-stage dynamometer test bench, bench control system, and bench alarm module. The three-stage dynamometer test bench provides a vehicle-level operating condition simulation environment for the hybrid transmission. The bench control system is responsible for setting test conditions, sending shift commands, and establishing a synchronous trigger link with the signal acquisition system to ensure precise matching between data acquisition timing and shift conditions. The bench alarm module receives out-of-range signals from the signal acquisition system, providing both audible and visual warnings for real-time control of abnormal operating conditions.

[0166] 2) Sensor Module: This module includes multiple types of sensing units, such as speed sensors, torque sensors, displacement sensors (shift fork displacement), pressure sensors (shift pressure), current sensors, and vibration sensors (triaxial vibration acceleration). These sensors collect operating parameters, actuator status, hydraulic characteristics, and vibration characteristics during the shifting process. All sensors are connected to a unified signal acquisition system, which avoids timing deviations caused by scattered acquisition and transmission delays from the source, providing a foundation for multi-signal fusion.

[0167] 3) Signal Acquisition System: As the core data processing unit, it consists of two parallel processing links:

[0168] Operating condition signal processing link: After the signals from the speed, torque, displacement, pressure and current sensors are synchronously acquired by the signal acquisition module, they are input to the filtering module to complete the interference removal and output a clean operating condition characteristic signal.

[0169] Vibration-Acoustic Conversion Link: After the vibration sensor signal is collected and filtered, it is input into the acoustic conversion model. The vibration signal is converted into an A-weighted sound pressure level (dBA) quantitative index that is in line with the vehicle's NVH system through the engineering acoustic radiation algorithm.

[0170] The final signals from both links are input into the signal fusion model to complete the time axis alignment and correlation analysis of multi-dimensional signals, output noise judgment, source tracing results and calibration optimization suggestions, and send trigger signals to the bench alarm module to form a complete "control-acquisition-analysis-alarm" closed loop.

[0171] (II) Core technical solutions and operating procedures

[0172] 1. Hardware installation and system integration

[0173] Complete the assembly and adjustment of each module according to the bench test specifications: install all types of sensors in their corresponding positions (vibration sensor is placed at the synchronizer bearing support of the hybrid transmission housing, pressure / displacement sensor is installed at the hydraulic line / shift fork linkage of the shift actuator, and speed / torque sensor is placed at the end of the shaft connecting the bench and the transmission), and connect all sensors to the signal acquisition system; according to the attached... Figure 1 Complete the electrical connections and communication link configurations of each module, complete channel calibration, zero-point calibration and synchronization testing to ensure consistent timing of multi-channel signal acquisition and stable transmission, fundamentally solving the problem of difficulty in tracing the source caused by signal timing deviation.

[0174] 2. Configuration of data acquisition and analysis parameters

[0175] Configure full-process parameters in the signal acquisition system:

[0176] Basic acquisition parameters: Set the sampling frequency and synchronous acquisition trigger logic to ensure that multiple signals are acquired at the same time base;

[0177] Signal processing parameters: Configure the filtering frequency band and zero drift calibration threshold for vibration / operating condition signals to adapt to the characteristics of shift transient signals;

[0178] Acoustic conversion parameters: A-weighted filter parameters and acoustic radiation model parameters are set according to IEC61672 standard to ensure that the quantitative indicators are consistent with the vehicle NVH standard;

[0179] Judgment and alarm parameters: Set the background noise acquisition duration, shift time window, dBA difference judgment threshold, and alarm threshold to provide a basis for subsequent noise identification.

[0180] 3. Background noise benchmark acquisition

[0181] Start the three-stage dynamometer test bench, set the target test conditions, and after the bench speed stabilizes and the no-load torque is stable, collect the vibration signal under no-shifting action through the signal acquisition system. After a preset duration, calculate and cache the dBA benchmark value corresponding to the background noise, providing a comparison benchmark for subsequent shift noise identification, and solving the pain point that shift transient noise is submerged and difficult to identify under high background noise.

[0182] 4. Synchronous acquisition and preprocessing of multiple signals

[0183] The test bench control system sends a shift command, and the signal acquisition system synchronously triggers the sensor module to collect multiple signals including speed, torque, displacement, pressure, current, and vibration. After acquisition, the signal is processed according to the attached... Figure 1 The two processing links complete the signal preprocessing: the operating condition signal is filtered to remove interference, and the vibration signal is filtered and integrated to complete zero drift calibration, retaining effective signal characteristics and providing reliable data support for subsequent analysis.

[0184] 5. Vibration-Acoustic Quantization Conversion

[0185] The signal acquisition system inputs the preprocessed vibration signal into the acoustic conversion model. Following the logic of "vibration velocity → sound pressure → A-weighted filtering → dBA calculation", the vibration signal is converted into an A-weighted sound pressure level (dBA) time-domain curve, realizing accurate quantitative characterization of shift noise and solving the problem that existing technologies can only make qualitative judgments and lack quantitative indicators for vehicle integration.

[0186] 6. Noise Identification and Judgment

[0187] Extract the dBA curve within the shift time window, calculate the decibel difference between the peak value and the background baseline value, compare it with the preset threshold, and determine whether there is obvious shift noise. If the decibel difference exceeds the alarm threshold, send a trigger signal to the bench alarm module to activate the audible and visual alarm, and realize real-time control of noise exceeding the standard.

[0188] 7. Multi-signal fusion source tracing

[0189] The signal fusion model maps the dBA quantized signal and the synchronously acquired operating condition signals (speed, torque, displacement, pressure, and current) to the same time axis, locates the timestamp corresponding to the dBA peak, and combines the characteristics of each signal (shift fork displacement change, speed and torque fluctuation, pressure and current anomalies) to accurately locate the specific shifting stage (shift fork push, synchronizer engagement, clutch switching) and core causes of noise generation (such as excessive shifting pressure, excessive engagement speed, and abnormal torque step). This solves the problems of low traceability accuracy and lack of coverage of hydraulic / operating condition signals in existing technologies.

[0190] 8. Optimization of shift calibration parameters

[0191] Based on the noise assessment results and source tracing conclusions, targeted shift calibration optimization suggestions are output, clarifying the adjustment directions of shift control strategies, hydraulic parameters (pressure, flow), synchronizer engagement speed, etc., and establishing a complete logic of "noise identification → source tracing → calibration optimization". If the noise assessment is qualified, the process ends; if it is not qualified, it enters the closed loop of post-optimization retesting, solving the pain points of existing technologies that lack closed-loop optimization and lack quantitative basis for calibration.

[0192] In another specific embodiment, a certain type of hybrid dedicated transmission (DHT) was used as the test object. Noise identification and calibration optimization tests were conducted on a three-stage dynamometer test bench for shifting from 1st to 2nd gear under no-load conditions. The test process relied on this technical solution and its appendices. Figure 1 System architecture, appendix Figure 2 The signal processing flow is executed, and all parameters are for illustrative purposes only and do not constitute a limitation on the technical solution.

[0193] 1. Experimental Preparation Phase

[0194] According to the appendix Figure 1 The system architecture shown completes the test system setup and debugging:

[0195] Hardware Deployment: A triaxial vibration sensor is fixed to the synchronizer bearing support of the DHT housing; a shift pressure sensor is installed in the hydraulic lines of the shift actuator, and a displacement sensor is installed at the shift fork; speed sensors are installed on the test bench and the DHT shaft end, with torque signal measurement achieved by a three-stage dynamometer, without torque control; a current acquisition channel is also configured. All sensors are connected to the same signal acquisition system to ensure that multiple signals are acquired at the same time base.

[0196] System integration and debugging: Complete sensor channel calibration, zero-point calibration and signal transmission test, confirm that the bench control system, TCU and signal acquisition system are triggered synchronously, and ensure that there is no timing deviation between shift command and signal acquisition.

[0197] 2. Test parameter configuration phase

[0198] According to the appendix Figure 2The process involves setting parameters within the data acquisition system:

[0199] Acquisition parameters: Sampling frequency 4800Hz, synchronous acquisition triggered by gear shift;

[0200] Processing parameters: Vibration signal bandpass filtering 20Hz~2000Hz, abnormal peak rejection threshold is 1.2 times the sensor range;

[0201] Acoustic parameters: A-weighted filtering is set according to IEC61672 standard;

[0202] Judgment parameters: background acquisition duration 10s, shift time window 0.2s~1.5s, decibel difference judgment threshold 3dBA, alarm threshold 4dBA.

[0203] 3. Background noise benchmark acquisition stage

[0204] The test bench was started and the speed was stabilized at 1500 r / min. The transmission was in an unloaded state with no gear shifting. Background vibration signals were collected for 10 seconds, preprocessed, and converted to dBA to obtain a background noise baseline value of 90 dBA.

[0205] 4. No-load shifting and signal acquisition stage

[0206] The test bench maintains the target speed, the TCU performs a no-load shift from 1st to 2nd gear, and the dynamometer only measures torque in real time without applying load control. The signal acquisition system simultaneously collects multiple signals including vibration, speed, torque, displacement, pressure, and current, fully covering the transient shifting process.

[0207] 5. Signal Processing and Noise Assessment Stage

[0208] According to the appendix Figure 2 Process completion signal processing:

[0209] Filtering, spike removal, and zero-drift calibration of multiple signals;

[0210] Convert the vibration signal into a dBA time-domain curve;

[0211] The peak dBA value within the shift window is 95 dBA, with a difference of 5 dBA between it and the background. This exceeds the judgment and alarm thresholds, indicating the presence of significant shift noise and triggering an alarm.

[0212] 6. Noise source tracing and calibration optimization stage

[0213] After aligning the dBA curve with the synchronization signal timing, the analysis showed that the noise peak occurred during the synchronizer engagement stage, with the corresponding speed fluctuation of ±50 r / min, the measured torque experiencing a 20 N·m instantaneous impact, and the shift pressure rising to 1.6 MPa overshoot. The cause was determined to be that the synchronizer engaged too quickly under no-load conditions and the hydraulic parameters were not properly matched.

[0214] Based on this, the calibration was optimized by controlling the shift pressure between 0.8 and 1.2 MPa, slowing down the engagement speed, and limiting the torque impact to ≤5 N·m. After retesting, the peak dBA dropped to 92 dBA, a difference of 2 dBA, which is below the threshold, thus eliminating the noise problem.

[0215] The core technology of the embodiments of this application lies in:

[0216] 1) In the DHT bench test scenario, the same data acquisition device is used to realize the synchronous acquisition of multiple signals (vibration, speed, torque, shift fork displacement, shift pressure, current). The timing consistency is ensured only through hardware channel synchronization, which effectively avoids signal misalignment caused by multi-source acquisition or CAN transmission delay, and provides a basis for accurate noise source tracing (adapted to the working condition of the bench where only torque is measured and not controlled).

[0217] 2) Construct a conversion logic from vibration signal to A-weighted sound pressure level (dBA) to quantify the vibration signal into an acoustic index that is in line with the vehicle NVH evaluation system, so as to achieve accurate quantitative identification of the bench shift noise under high background noise, and solve the pain point that traditional methods can only make qualitative judgments and have no unified quantitative standards.

[0218] 3) Based on the synchronously acquired multi-dimensional signals (including hydraulic / actuation signals such as shift pressure and current), time sequence alignment and correlation analysis are performed. Combined with the characteristics of no-load shifting conditions, the specific stage of noise generation (such as the synchronizer engagement stage) and the core causes are accurately located, providing a direct basis for TCU calibration optimization.

[0219] 4) No soundproofing modifications or additional specialized equipment are required for the existing three-dimensional dynamometer test bench. Relying solely on conventional test bench hardware and sensors, it is possible to identify, trace, and calibrate and optimize no-load shifting noise. It has strong engineering applicability and controllable costs.

[0220] 5) Establish a complete technical logic of "background noise acquisition → synchronous acquisition of no-load shifting signal → vibration-acoustic conversion → noise determination → multi-signal source tracing → calibration optimization" to specifically solve the industry pain points of DHT no-load shifting test bench noise identification, accurate source tracing, and lack of calibration basis.

[0221] Furthermore, in another specific embodiment, an alternative is disclosed.

[0222] The core objective of this application is to achieve accurate quantitative identification, source tracing, and calibration optimization guidance for the noise of the DHT no-load shifting test bench. Based on this objective, the following alternative solutions can all achieve the same effect and can be included in the scope of protection of this application:

[0223] 1) Sensor type alternatives

[0224] Vibration sensor: Single-axis or dual-axis vibration sensors can be used instead of the three-axis vibration sensor in the embodiment. As long as the vibration characteristic signal during the DHT shifting process can be collected, there is no need to limit the number of axes of the vibration sensor, and vibration-acoustic conversion and noise quantification identification can be achieved.

[0225] Pressure / displacement sensor: Different installation methods (such as non-contact displacement sensor, integrated pressure sensor) can be used to replace the existing installation method. As long as the shift pressure and shift fork displacement signals can be accurately collected and the synchronous acquisition and timing alignment of multiple signals are not affected, they are all within the protection scope of this invention.

[0226] Torque measurement: An independent torque sensor can be used to replace the torque measurement module built into the three-way dynamometer. As long as it can realize the real-time measurement of torque signal during no-load shifting (without torque control) and does not affect the multi-signal correlation and traceability, it is feasible.

[0227] 2) Alternative solutions for signal processing parameters

[0228] Sampling frequency: can be set arbitrarily within the range of 4000Hz to 10000Hz, and does not need to be limited to 4800Hz. As long as the sampling frequency can cover the characteristics of the shift transient signal and ensure that the signal is not missed or distorted, the purpose of the invention can be achieved.

[0229] Filtering parameters: The vibration signal filtering range can be adjusted within the range of 10Hz~2500Hz. The acoustic conversion parameters can be replaced by different industry standards (such as GB / T3241-2010) instead of the IEC61672 standard. As long as the vibration-acoustic conversion can be accurately achieved and the dBA index is in line with the vehicle NVH system, it is within the protection range.

[0230] Judgment threshold: The decibel difference judgment threshold and alarm threshold can be flexibly adjusted according to different DHT models and different test bench background noise characteristics (such as judgment threshold 2~4dBA, alarm threshold 3~5dBA). As long as it can effectively determine whether the no-load shifting noise is abnormal, it is feasible.

[0231] 3) Alternative solutions for the vibration-acoustic conversion model

[0232] Different engineering acoustic conversion models (such as spherical radiation model and planar radiation model) can be used to replace the acoustic radiation model in the embodiment. As long as the vibration signal can be converted into a quantifiable acoustic index that can be compared with the NVH of the whole vehicle (not limited to dBA, but also including other weighted sound pressure levels) to achieve quantitative identification of shift noise under high background noise, the purpose of the invention can be achieved.

[0233] 4) Alternative solution for synchronous signal acquisition

[0234] In addition to using the same data acquisition device to achieve multi-channel synchronization, a combination of "multiple acquisition devices + clock synchronization module" can be used to ensure the consistency of the acquisition timing of multiple signals through external clock synchronization. As long as the signal misalignment problem can be avoided, a reliable timing basis for multi-signal correlation and tracing can be provided, and the core logic of the present invention is not changed, it is within the scope of protection.

[0235] 5) Alternative solution for no-load shifting mode

[0236] This application is primarily adapted to no-load shifting conditions and can be extended to "light-load shifting conditions" (the test bench still only measures torque, does not control torque, and only applies a small load). As long as there is no active torque control during the shifting process and the shifting noise characteristics of the transmission body are highlighted, the technical solution of this application can achieve noise identification and source tracing. This extended condition should also be included in the scope of protection.

[0237] Figure 5 This is a block diagram of an electronic device for identifying and calibrating a hybrid transmission bench shift noise optimization method provided in one or more embodiments of the present invention.

[0238] like Figure 5 As shown, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0239] The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of a hybrid transmission bench shift noise identification and calibration optimization method.

[0240] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a hybrid transmission bench shift noise identification and calibration optimization method.

[0241] This application also provides a transmission test bench, including:

[0242] Electronic equipment used to implement the steps of a method for identifying and calibrating optimization of shift noise on a hybrid transmission bench;

[0243] The processor runs a program, and when the program runs, it executes the steps of the hybrid transmission bench shift noise identification and calibration optimization method based on data output from the electronic device.

[0244] The storage medium is used to store the program, which, when running, executes the steps of the hybrid transmission bench shift noise identification and calibration optimization method based on data output from the electronic device.

[0245] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0246] The electronic device comprises a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control the electronic device through processes, such as Linux, Unix, Android, iOS, or Windows. Furthermore, in this embodiment of the invention, the electronic device can be a smartphone, tablet computer, or other handheld device, or a desktop computer, portable computer, or other electronic device; there is no particular limitation in this embodiment.

[0247] In this embodiment of the invention, the executing entity for electronic device control can be an electronic device itself, or a functional module within an electronic device capable of calling and executing a program. The electronic device can obtain the firmware corresponding to the storage medium. This firmware is provided by the supplier, and different storage media may have the same or different firmware; no limitation is made here. After obtaining the firmware corresponding to the storage medium, the electronic device can write this firmware into the storage medium; specifically, it burns the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology, and will not be elaborated upon in this embodiment of the invention.

[0248] Electronic devices can also obtain reset commands corresponding to the storage media. The reset commands corresponding to the storage media are provided by the supplier. The reset commands corresponding to different storage media can be the same or different, and no restrictions are imposed here.

[0249] At this time, the storage medium of the electronic device is a storage medium on which the corresponding firmware has been written. The electronic device can respond to the reset command corresponding to the storage medium on which the corresponding firmware has been written, thereby resetting the storage medium on which the corresponding firmware has been written according to the reset command. The process of resetting the storage medium according to the reset command can be implemented by existing technology and will not be described in detail in this embodiment of the invention.

[0250] For ease of description, the above devices are described separately by function as various units and modules. Of course, in implementing this application, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0251] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0252] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0253] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0254] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hybrid transmission bench shifting noise identification and calibration optimization method, characterized in that, The hybrid transmission bench shift noise identification, calibration and optimization method includes: Step S1: Hardware installation and system integration testing to ensure consistent timing of multi-channel signal acquisition; Step S2: Configure the parameters required for the entire noise calibration process; Step S3: Background noise reference acquisition, obtaining background noise reference value; Step S4: Perform synchronous acquisition of multiple signals; Step S5: Signal preprocessing to ensure the acquisition of clean operating condition characteristic signals and vibration characteristic signals; Step S6: Vibration-acoustic quantization conversion to obtain the A-weighted sound pressure level time-domain curve; Step S7: Based on the obtained A-weighted sound pressure level time-domain curve, noise identification and judgment are performed; Step S8: Based on the synchronous acquisition of multiple signals, multi-signal fusion is used to trace the source and locate the noise generation and core causes; Step S9: Based on the source tracing results, output calibration optimization suggestions, complete the optimization and retest to form a closed loop.

2. The hybrid transmission bench shift noise identification and calibration optimization method according to claim 1, characterized in that, Step S1, hardware installation and system integration to ensure consistent timing of multi-channel signal acquisition, includes: Install the speed sensor, torque sensor, shift fork displacement sensor, shift pressure sensor, current sensor, and vibration sensor in the preset corresponding positions; All sensors are connected to the same signal acquisition system to complete channel calibration, zero-point calibration and synchronization testing to ensure consistent timing of multi-channel signal acquisition.

3. The hybrid transmission bench shift noise identification and calibration optimization method of claim 1, wherein, Step S2, configuring the parameters required for the entire noise calibration process, includes: Configure basic acquisition parameters, signal processing parameters, acoustic conversion parameters, and judgment and alarm parameters.

4. The hybrid transmission bench shift noise identification and calibration optimization method of claim 1, wherein, In step S3, background noise reference acquisition, the background noise reference value is obtained including: Under stable operating conditions without gear shifting, background vibration signals are collected, preprocessed, converted into A-weighted sound pressure levels, and buffered to obtain background noise reference values.

5. The hybrid transmission bench shift noise identification and calibration optimization method of claim 1, wherein, Step S4, which involves synchronously acquiring multiple signals, includes: The bench control system sends a shift command, and the signal acquisition system simultaneously triggers all sensors to collect multiple signals such as speed, torque, shift fork displacement, shift pressure, current, and vibration during the shift process.

6. The hybrid transmission bench shift noise identification and calibration optimization method of claim 1, wherein, Step S5, signal preprocessing, to ensure the acquisition of pure operating condition characteristic signals and vibration characteristic signals includes: The collected multi-channel signals are filtered, abnormal spikes are removed, and zero drift is calibrated to obtain pure operating condition characteristic signals and vibration characteristic signals.

7. The method for identifying, calibrating, and optimizing shift noise on a hybrid transmission bench as described in claim 1, characterized in that, Step S6, vibration-acoustic quantization conversion, to obtain the A-weighted sound pressure level time-domain curve includes: The preprocessed vibration characteristic signal is input into the acoustic conversion model and converted into an A-weighted sound pressure level time domain curve.

8. The method for identifying, calibrating, and optimizing shift noise on a hybrid transmission bench as described in claim 1, characterized in that, Step S7, based on the obtained A-weighted sound pressure level time-domain curve, includes noise identification and judgment, which includes: Extract the A-weighted sound pressure level curve within the shift time window, calculate the decibel difference between the peak value and the background noise reference value, compare it with the preset threshold, and determine whether shift noise exists. If the value exceeds the limit, a trigger signal is sent to the bench alarm module to activate an audible and visual warning.

9. The method for identifying, calibrating, and optimizing shift noise on a hybrid transmission bench as described in claim 1, characterized in that, Step S8, based on the synchronous acquisition of multiple signals, multi-signal fusion and source tracing, locates the noise generation and core causes, including: The A-weighted sound pressure level signal and the synchronously acquired operating condition signal are mapped to the same time axis to locate the timestamp corresponding to the noise peak. By combining the characteristics of each signal, the shifting stage and core cause of noise generation can be located.

10. A hybrid transmission bench shift noise identification, calibration, and optimization system, characterized in that, The hybrid transmission bench shift noise identification, calibration, and optimization system includes: The hardware installation and system integration module is used to install speed sensors, torque sensors, shift fork displacement sensors, shift pressure sensors, current sensors, and vibration sensors in their corresponding positions. All sensors are connected to the same signal acquisition system to complete channel calibration, zero-point calibration, and synchronization testing, ensuring consistent timing of multi-channel signal acquisition. The parameter configuration module is used to configure basic acquisition parameters, signal processing parameters, acoustic conversion parameters, and judgment and alarm parameters. The background noise reference acquisition module is used to acquire background vibration signals under stable operating conditions without gear shifting, preprocess them, convert them into A-weighted sound pressure levels, and cache them to obtain background noise reference values. The multi-channel signal synchronous acquisition module is used to send shift commands to the bench control system. The signal acquisition system synchronously triggers all sensors to synchronously acquire multiple signals such as speed, torque, shift fork displacement, shift pressure, current, and vibration during the shift process. The signal preprocessing module is used to filter, remove abnormal spikes, and zero drift calibration of the acquired multi-channel signals to obtain pure operating condition characteristic signals and vibration characteristic signals. The vibration-acoustic quantization conversion module is used to input the preprocessed vibration characteristic signal into the acoustic conversion model and convert it into an A-weighted sound pressure level time domain curve. The noise identification and judgment module is used to extract the A-weighted sound pressure level curve within the shift time window, calculate the decibel difference between the peak value and the background noise reference value, compare it with the preset threshold, and determine whether shift noise exists. If it exceeds the standard, a trigger signal is sent to the bench alarm module to activate the audible and visual warning. The multi-signal fusion and source tracing module is used to map the A-weighted sound pressure level signal and the synchronously acquired operating condition signal to the same time axis, locate the timestamp corresponding to the noise peak, and combine the characteristics of each signal to locate the shift stage and core cause of noise generation. The calibration and optimization module is used to output shift calibration and optimization suggestions based on the source tracing results, and to retest after optimization to form a closed loop.

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