Performance detection method for automobile sensor production

Abstract

The invention discloses a performance detection method for automobile sensor production, and relates to the technical field of sensor detection, and the method comprises the steps: reading and recognizing the type of a sensor through non-contact coding, and matching a detection template; automatic electrical connection is realized through self-adaptive clamping and visual auxiliary alignment; temperature circulation, electromagnetic interference and random vibration are synchronously applied in the temperature control cavity to form a three-field coupling stress environment; synchronously acquiring multi-channel response data at a high sampling rate, and marking and storing the multi-channel response data; performing three-dimensional comparison and abnormality diagnosis based on the standard reference model and a gradient lifting decision tree algorithm, and generating a structured report; a result is uploaded to a quality management system, a process optimization instruction is fed back, and closed-loop control is achieved. By means of the technical scheme, various sensor types can be fully automatically compatible, vehicle-mounted complex working conditions can be truly reproduced, early failure can be accurately recognized, and whole-process traceability and production line flexibility improvement are achieved.

Description

A performance testing method for automotive sensor manufacturing Technical Field

[0001] This invention relates to the field of sensor testing technology, and in particular to a performance testing method for automotive sensor production. Background Technology

[0002] With the continuous improvement of automotive electronics and intelligence, various automotive sensors are increasingly widely used in vehicle systems, and their performance stability directly affects the safety and reliability of the vehicle. As core components of the sensing layer, automotive sensors encompass various types, including pressure, temperature, position, acceleration, angle, and gas concentration sensors, and are widely deployed in powertrain, chassis control, body electronics, and advanced driver assistance systems. In the manufacturing process, efficient, comprehensive, and accurate testing of sensor output accuracy, response speed, environmental tolerance, and long-term operational stability has become a crucial prerequisite for ensuring the functional safety and quality consistency of the entire vehicle.

[0003] Among these, performance testing methods for automotive sensors aim to quantitatively evaluate the output characteristics of sensors under different physical stimuli by simulating real-world in-vehicle conditions. These methods typically involve core steps such as signal acquisition, environmental loading, data comparison, and fault diagnosis, with the goal of completing closed-loop verification of multi-dimensional performance indicators within production line cycle time constraints. However, existing technologies still face multiple structural limitations in achieving these goals.

[0004] Existing automotive sensor testing solutions generally suffer from problems such as limited testing dimensions, insufficient automation, and weak universal adaptability. On the one hand, most testing systems design dedicated fixtures and test logic only for specific types of sensors, making it difficult to be compatible with the differentiated interface protocols and performance boundaries of new or heterogeneous sensors. On the other hand, although some systems have introduced multi-station parallel architectures to improve throughput efficiency, their environmental simulation capabilities are limited to static or single-factor loading, failing to reproduce dynamic operating conditions involving multi-physical field coupling such as temperature cycling, mechanical vibration, and electromagnetic interference. This results in significant blind spots in the evaluation of the sensor's true performance in complex automotive environments. Furthermore, existing testing processes heavily rely on manual intervention for clamping, positioning, parameter configuration, and result interpretation, which not only introduces the risk of human error but also restricts the traceability and intelligence level of the testing process. Summary of the Invention

[0005] The purpose of this invention is to provide a performance testing method for automotive sensor production, in order to solve the problems of existing testing schemes, such as single testing dimensions, insufficient automation, weak universal adaptability, inability to reproduce dynamic working conditions of multi-physical field coupling, and poor traceability.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A performance testing method for automotive sensor manufacturing includes the following specific steps:

[0008] Step S1: Receive the sensor under test and identify its type. Obtain the unique identification code on the sensor housing through a non-contact coding reading device. Parse the identification code to extract the sensor type, interface protocol, measurement range and batch information. Automatically match the corresponding detection process template based on the pre-stored sensor parameter database.

[0009] Step S2: Perform adaptive clamping and electrical connection. According to the sensor type, call the corresponding flexible clamping component, drive the multi-axis servo mechanism to position the sensor under test to the test station, use the vision-assisted alignment system to correct its spatial posture deviation, ensure that the pins or interfaces are accurately connected to the general probe array, and complete the automatic connection of power supply, signal lines and communication bus.

[0010] Step S3: Apply multi-environmental stress synergistic excitation, control the temperature control cavity to perform temperature cyclic loading at a preset slope within the range of -40℃ to 150℃, and simultaneously activate the electromagnetic interference generation module to emit a programmable intensity radio frequency signal in the frequency band of 30MHz to 6GHz, and apply a random vibration spectrum with a frequency of 5Hz to 2000Hz and an acceleration peak of 50g through the vibration table to form a composite stress environment of temperature-electromagnetic-mechanical three fields coupled;

[0011] Step S4: Collect dynamic response data, synchronously record the output voltage, current, frequency and digital communication frames of the sensor under combined stress, with a sampling frequency of not less than 1MHz and a time resolution of less than 1μs. Cache the data to a high-speed solid-state storage unit and attach timestamps and environmental parameter tags.

[0012] Step S5: Perform intelligent data analysis and fault diagnosis. Compare the collected raw response data with the standard benchmark model in three dimensions: time domain, frequency domain, and feature space. Calculate the deviation index, drift rate, and signal-to-noise ratio attenuation curve. Use an anomaly detection algorithm based on gradient boosting decision tree to identify potential failure modes and generate a structured report containing defect location, type, and confidence level.

[0013] Step S6: Output the test results and provide feedback optimization instructions. Upload the structured report to the central quality management system, trigger the isolation mechanism for non-conforming products, extract historical test data sequences, analyze the performance degradation trend using a sliding window regression model, and send process parameter correction suggestions to the production line control system to complete closed-loop quality control.

[0014] In step S1, the non-contact encoding and reading device adopts high-contrast laser imaging technology with a resolution of 1280×1024 pixels, a decoding success rate of more than 99.9%, supports three identification forms: one-dimensional barcode, two-dimensional matrix code and RFID electronic tag, and has a parsing response time of less than 200ms.

[0015] In step S2, the flexible clamping assembly consists of adaptive jaws driven by shape memory alloy. The jaw surface is covered with a conductive silicone layer with an elastic modulus of 0.5MPa to 3MPa. It can be compatible with cylindrical, square and irregular shells with diameters of 5mm to 80mm, and the docking repeatability is better than ±0.02mm.

[0016] In step S2, the vision-assisted alignment system is equipped with a binocular industrial camera with a focal length of 8mm and a field of view of 45 degrees. The pin offset is measured in real time through a sub-pixel edge detection algorithm, the closed-loop adjustment period is 10ms, and the alignment error is controlled within ±5μm.

[0017] In step S3, the temperature control cavity adopts a Peltier and liquid cooling composite temperature control structure, with a heating and cooling rate of up to 15℃ / minute and a temperature uniformity within ±1.5℃. The inner wall of the cavity is coated with a wave-absorbing material to reduce electromagnetic reflection interference.

[0018] Preferably, the electromagnetic interference generating module in step S3 has an omnidirectional radiating antenna array with a maximum field strength output of 200V / m and a step adjustment accuracy of 0.5dB, which can simulate the vehicle electromagnetic environment profile specified in the ISO11452 series standards.

[0019] In step S3, the vibration table is driven by a hydraulic servo, with an effective load capacity of 100 kg, a lateral motion suppression ratio higher than -40 dB, and a random vibration power spectral density control error of less than ±1.5 dB.

[0020] In step S4, the data acquisition system integrates a synchronous multi-channel ADC chip with 64 channels, a vertical resolution of 16 bits, an input impedance greater than 10MΩ, a common-mode rejection ratio higher than 120dB, and supports data frame capture for multiple vehicle bus protocols such as CAN, LIN, FlexRay, and SENT.

[0021] In step S4, the timestamp is provided by a GPS-disciplined crystal oscillator with a long-term frequency stability better than ±0.1ppm and a time deviation from Coordinated Universal Time less than 1μs, ensuring time consistency of data across devices.

[0022] In step S5, the standard benchmark model is constructed from the statistical mean of no less than 1,000 sets of valid samples, covering the typical response characteristics under four basic operating conditions: room temperature static, high temperature steady state, low temperature start-up, and transient impact. The model is updated every 7 days.

[0023] The deviation index in step S5 is calculated using the following formula:

[0024]

[0025] Where S(t) is the measured signal sequence, R(t) is the reference signal sequence, and t is the time variable. This indicates that the integral is performed over time t, where T is the integration duration. A value exceeding the set threshold of 0.05 is considered a significant deviation.

[0026] In step S5, the drift rate is calculated using the following formula:

[0027]

[0028] in, The output value is the value at the end of the stress period. This is the initial output value. The nominal full-scale value is defined as D. If the absolute value of D is greater than 2%, it is marked as parameter drift.

[0029] In step S5, the signal-to-noise ratio attenuation curve is extracted using short-time Fourier transform to determine the ratio of the main frequency energy to the noise floor level. A decrease in SNR of more than 3dB for three consecutive observation windows is considered a performance degradation warning.

[0030] Preferably, the gradient boosting decision tree algorithm in step S5 includes 200 weak classifiers with a maximum depth of 8, a learning rate of 0.1, and a training set consisting of 100,000 labeled historical fault cases. The anomaly detection rate is greater than 98.5%, and the false alarm rate is less than 1.2%.

[0031] In step S6, the structured report is encapsulated in XML format and includes sensor ID, test time, environmental conditions, key indicator values, defect code, image evidence link, and diagnostic conclusion fields, with a transmission delay of less than 50ms.

[0032] In step S6, the sliding window regression model uses a historical data sequence with a width of 30 sets, and the model's coefficient of determination R² is not less than 0.93. The prediction target is the average zero-point offset of the next batch of products. The prediction target is used to guide the feedforward adjustment of assembly pressure and welding temperature.

[0033] The performance testing method for automotive sensor production also includes establishing a distributed testing node cluster, with each node sharing a unified parameter database and diagnostic model library, achieving nanosecond-level clock synchronization through a time-sensitive network, supporting simultaneous online testing of no less than 64 sensors, and compressing the average testing cycle of a single piece to within 90 seconds.

[0034] The performance testing method for automotive sensor production also includes setting up a safety interlock protection mechanism, which monitors the internal air pressure, leakage current and vibration table load status of the temperature control chamber in real time during the testing process. If any parameter exceeds the safety threshold, the energy supply is immediately cut off and an emergency stop procedure is initiated to ensure the safety of equipment and personnel.

[0035] The performance testing method for automotive sensor production also includes deploying an edge computing unit to complete raw data compression and feature extraction locally, and only uploading key diagnostic results and metadata to the cloud server, reducing bandwidth usage by 85% and meeting automotive-grade data privacy protection requirements.

[0036] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0037] This invention fundamentally solves the bottleneck problems of traditional methods in terms of detection dimensions, versatility and intelligence level by constructing a fully automated detection system that integrates type recognition, adaptive clamping, multi-physics field coupling excitation, high-precision synchronous acquisition and intelligent diagnosis.

[0038] This invention enables fully automated compatible testing of different types of automotive sensors without the need to change special fixtures or manually configure parameters, significantly improving production line flexibility and operational efficiency. By introducing a three-field synergistic loading mechanism of temperature, electromagnetic, and mechanical fields, it realistically replicates the complex service environment of vehicles, effectively exposing hidden defects that are difficult to detect under single stress, and greatly improving the effectiveness and reliability of testing.

[0039] This invention combines a gradient boosting decision tree diagnostic model based on big data analysis to achieve a leap from "threshold judgment" to "pattern recognition," which can accurately distinguish between normal fluctuations and early failures, reducing the risk of misjudgment.

[0040] The entire testing process of this invention is unmanned, and the data is traceable throughout, forming a complete closed loop from testing execution to process reverse optimization. This not only enhances quality control capabilities but also provides a solid data foundation for the continuous evolution of intelligent manufacturing systems. Attached Figure Description

[0041] Figure 1 is a schematic diagram of the overall technical solution architecture of a performance testing method for automotive sensor production proposed in this invention.

[0042] Figure 2 is a schematic diagram of the core principle framework of the multi-physics field coupling excitation and intelligent diagnosis collaborative mechanism proposed in this invention. Detailed Implementation

[0043] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present invention and not to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present invention by illustrating examples of the invention.

[0044] Example 1

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0046] Currently, with the continuous improvement of automotive electronics and intelligence, various automotive sensors are increasingly widely used in vehicle systems, and their performance stability directly affects the safety and reliability of the vehicle. As core components of the sensing layer, automotive sensors encompass various types, including pressure, temperature, position, acceleration, rotation angle, and gas concentration, and are widely deployed in powertrain, chassis control, body electronics, and advanced driver assistance systems. In the manufacturing process, efficient, comprehensive, and accurate testing of sensor output accuracy, response speed, environmental tolerance, and long-term operational stability has become a crucial prerequisite for ensuring the functional safety and quality consistency of the entire vehicle. To address these technical challenges, this invention proposes a fully automated testing system integrating type recognition, adaptive clamping, multi-physics field coupling excitation, high-precision synchronous acquisition, and intelligent diagnostics. This system fundamentally solves the bottlenecks of traditional methods in terms of testing dimensions, versatility, and intelligence, and is applied to a performance testing method for automotive sensor production.

[0047] Referring to Figure 1, the schematic diagram of the overall technical architecture of the performance testing method for automotive sensors proposed in this invention illustrates the complete closed-loop process from the input of the sensor under test to the output of the test result and the feedback of optimization instructions. This architecture consists of core modules such as a non-contact encoding and reading device, a flexible fixture assembly, a multi-axis servo mechanism, a vision-assisted alignment system, a universal probe array, a temperature-controlled cavity, an electromagnetic interference generation module, a vibration table, a high-speed data acquisition system, an edge computing unit, and a central quality management system. All modules work collaboratively under the coordination of a unified timing controller to ensure high precision, high efficiency, and high reliability in the testing process. Referring to Figure 2, the schematic diagram of the core principle framework of the multi-physics field coupled excitation and intelligent diagnostic collaborative mechanism in this invention further reveals how the temperature-electromagnetic-mechanical stresses interact with the sensor's dynamic response data, and how the standard benchmark model and gradient boosting decision tree algorithm achieve accurate identification of fault modes and predictive analysis of performance degradation trends.

[0048] In a performance testing method for automotive sensor production, step S1 involves receiving the sensor to be tested and identifying its type. A unique identification code on the sensor housing is obtained through a non-contact coding reader. The identification code is parsed to extract the sensor type, interface protocol, measurement range, and batch information. The corresponding testing process template is then automatically matched based on a pre-stored sensor parameter database.

[0049] Specifically, the non-contact coding and reading device employs high-contrast laser imaging technology. Its optical system features a CMOS image sensor with a resolution of 1280×1024 pixels, coupled with a 650nm red laser light source, creating high signal-to-noise ratio illumination conditions and effectively suppressing image overexposure caused by reflections from the metal casing. The non-contact coding and reading device supports mixed reading of three identification forms: one-dimensional barcodes, two-dimensional matrix codes, and RFID electronic tags. It integrates a multi-modal decoding engine capable of processing different types of coded information in parallel. In actual operation, after the sensor under test is transmitted to the identification station, the laser imaging system completes image capture and preprocessing within 200ms, including adaptive histogram equalization, noise suppression, and edge enhancement. Subsequently, the decoding engine is activated to locate and parse the effective coded areas in the image, achieving a decoding success rate greater than 99.9%. The parsed sensor type information, including pressure sensors, Hall effect angle sensors, or oxygen concentration sensors, is transmitted to the central controller in real time. The central controller immediately queries the locally stored sensor parameter database, which is organized in key-value pairs. The primary key is a combination of sensor type and batch number, and the value range contains a complete detection process template. The template predefines the parameter set required for all subsequent steps, including temperature control curves, vibration spectra, electromagnetic field strength profiles, sampling channel configurations, and diagnostic thresholds. This process requires no manual intervention, achieving adaptive loading of the detection logic and laying the foundation for compatible detection of different sensor types.

[0050] In a performance testing method for automotive sensor production, step S2 involves performing adaptive clamping and electrical connection. According to the sensor type, the corresponding flexible fixture component is called, and the multi-axis servo mechanism is driven to position the sensor under test to the test station. The spatial posture deviation is corrected by a vision-assisted alignment system to ensure that the pins or interfaces are accurately connected to the general probe array, and the power supply, signal lines and communication bus are automatically connected.

[0051] Specifically, the flexible gripper assembly consists of adaptive jaws driven by shape memory alloy. Each jaw incorporates a nickel-titanium alloy wire. By precisely controlling the magnitude and direction of the current flowing through the alloy wire, it can produce controllable contraction or expansion deformation, thereby adjusting the opening and closing dimensions and clamping force of the jaw. The jaw surface is coated with a layer of conductive silicone, which has an elastic modulus of 0.5MPa to 3MPa and a Shore hardness of 30A to 60A. This ensures sufficient flexibility to conform to sensor housings with different geometries, including cylindrical, square, and irregularly shaped housings with diameters ranging from 5mm to 80mm; it also possesses good conductivity, providing a reliable grounding path during clamping. The multi-axis servo mechanism is a six-degree-of-freedom parallel robot with a repeatability accuracy better than ±0.01mm. Its motion trajectory is pre-planned by the central controller according to the clamping coordinate system corresponding to the sensor type. After the sensor is initially positioned, the vision-assisted alignment system is activated. This system is equipped with a binocular industrial camera with an 8mm focal length, a 45-degree field of view, and a 120mm baseline distance. It reconstructs the 3D point cloud of the sensor pins or interfaces in real time using a stereo matching algorithm. A sub-pixel edge detection algorithm is applied to the point cloud data to calculate the offset between the actual position and the theoretical design position of the pin center with sub-pixel accuracy better than 0.1 pixels. This offset includes three translational components (X, Y, and Z) and rotational components around the three axes. This offset is fed back to the closed-loop control system of the multi-axis servo mechanism, with an adjustment cycle of 10ms. Through iterative fine-tuning, the alignment error is ultimately controlled within ±5 micrometers. Based on this high-precision alignment, a general-purpose probe array is vertically pressed down. The probe tips are made of gold-plated beryllium copper with a contact resistance of less than 10mΩ, ensuring a low-impedance, high-reliability electrical connection with the sensor pins. This completes the automatic connection of power, analog signal lines, and communication buses such as CAN and LIN in one step.

[0052] In a performance testing method for automotive sensor production, step S3 involves applying a multi-environmental stress synergistic excitation, controlling the temperature control cavity to perform temperature cyclic loading at a preset slope within the range of -40℃ to 150℃, simultaneously activating an electromagnetic interference generation module to emit a programmable intensity radio frequency signal in the 30MHz to 6GHz frequency band, and applying a random vibration spectrum with a frequency of 5Hz to 2000Hz and a peak acceleration of 50g through a vibration table to form a composite stress environment with temperature-electromagnetic-mechanical three-field coupling.

[0053] Specifically, the temperature control chamber employs a Peltier thermocouple and liquid-cooled composite temperature control structure. A multi-stage Peltier thermopile is embedded within the chamber wall for rapid heating and cooling, while a liquid-cooled plate is integrated at the back. Deionized water circulates internally as the cooling medium to remove waste heat generated by the Peltier thermocouple. This composite structure enables a heating / cooling rate of up to 15℃ / minute, maintaining excellent dynamic response across the entire temperature range of -40℃ to 150℃. The chamber incorporates a multi-point platinum resistance temperature sensor with a sampling frequency of 10Hz. A PID algorithm is used to adjust the Peltier power and coolant flow rate in real time, ensuring temperature uniformity within the working area of ​​the chamber is within ±1.5℃. To reduce electromagnetic interference, the inner wall of the chamber is coated with a 2mm thick ferrite absorbing material, exhibiting a reflection loss greater than 20dB in the 30MHz to 6GHz frequency band. Simultaneously, an electromagnetic interference generation module is activated. This module consists of a vector signal generator, a broadband power amplifier, and an omnidirectional radiating antenna array. The antenna array comprises eight dipole antennas distributed at the vertices of a cube, achieving omnidirectional field strength coverage. The module has a maximum field strength output of 200V / m and a step adjustment accuracy of 0.5dB. Its output waveform can be preset with various vehicle electromagnetic environment profiles according to the ISO11452 series standards, such as narrowband scanning, broadband noise, and transient pulses. Regarding mechanical stress, the vibration table uses a hydraulic servo drive. Its actuators are fixed to the bottom of the temperature control chamber via high-rigidity connectors, with an effective load capacity of 100 kg, sufficient to support the chamber and all its internal equipment. The vibration table incorporates a triaxial accelerometer, forming a closed-loop control system with a lateral motion suppression ratio higher than -40dB, ensuring that excitation energy is primarily transmitted along the Z-axis. The random vibration power spectral density (PSD) is retrieved from the template library by the central controller based on the sensor type, with a control error of less than ±1.5dB, realistically reproducing the vibration environment of a vehicle under different road conditions. The loading of the three stress fields strictly follows a time synchronization protocol, triggered by the same clock source, ensuring the spatiotemporal consistency of the composite stress environment.

[0054] In a performance testing method for automotive sensor production, step S4 involves collecting dynamic response data and simultaneously recording the sensor's output voltage, current, frequency, and digital communication frames under combined stress. The sampling frequency is not less than 1MHz, the time resolution is less than 1μs, the data is cached in a high-speed solid-state storage unit, and timestamps and environmental parameter tags are added.

[0055] Specifically, the data acquisition system integrates a synchronous multi-channel ADC chip, with each chip providing 16 analog input channels. The system achieves synchronous sampling of 64 channels through four cascaded chips. Each channel has a vertical resolution of 16 bits, an input impedance greater than 10MΩ, and a common-mode rejection ratio (CMRR) higher than 120dB, effectively suppressing common-mode noise interference with weak signals. For digital communication signals, the system is equipped with a dedicated protocol analyzer. The hardware supports physical layer access for various automotive bus protocols such as CAN, LIN, FlexRay, and SENT, while the software has a built-in corresponding protocol stack, enabling real-time parsing of data frame content, ID, checksum, and error flags. All 64 analog channels share the same sampling clock with the digital protocol analyzer, provided by a high-stability crystal oscillator, ensuring a time offset of less than 1ns between channels. The acquired raw data stream is written in real-time to a high-speed solid-state storage unit with a PCIe 4.0 interface, with a continuous write bandwidth exceeding 7GB / s, sufficient to handle the data deluge of 64 channels at a 1MHz sampling rate. To ensure data traceability and cross-device consistency, each data record is appended with a high-precision timestamp provided by a GPS-disciplined crystal oscillator. Its internal atomic clock is periodically calibrated by receiving GPS satellite signals, achieving a long-term frequency stability better than ±0.1 ppm and a time deviation from Coordinated Universal Time (UTC) of less than 1 μs. In addition, the data records are tagged with complete environmental parameters, including the real-time temperature of the temperature control chamber, the output field strength and frequency of the electromagnetic interference module, and the PSD value of the vibration table's acceleration. These tags are broadcast by the controllers of each subsystem via a time-sensitive network, strictly aligned with the data acquisition time.

[0056] In a performance testing method for automotive sensor production, step S5 involves performing intelligent data analysis and fault diagnosis. The collected raw response data is compared with a standard benchmark model in three dimensions: time domain, frequency domain, and feature space. The deviation index, drift rate, and signal-to-noise ratio attenuation curve are calculated. An anomaly detection algorithm based on gradient boosting decision tree is used to identify potential failure modes and generate a structured report containing defect location, type, and confidence level.

[0057] Specifically, the standard benchmark model is constructed from the statistical mean of no fewer than 1000 sample sets. These samples were tested under the same composite stress environment, and their response data underwent rigorous screening and cleaning to remove any samples with minor anomalies. The benchmark model covers typical response characteristics under four basic operating conditions: room temperature static, high temperature steady state, low temperature start-up, and transient impact. These characteristics are stored in the model library as multidimensional arrays, and the model is updated every 7 days to track minor drifts in the production process. In the time-domain comparison, the deviation exponent E between the measured signal sequence S(t) and the reference signal sequence R(t) is calculated using the following formula:

[0058]

[0059] Where T is the integration time, typically a complete temperature cycle or vibration excitation cycle. When the value of E exceeds a set threshold of 0.05, the sensor's output is considered to have a significant deviation. For parameter stability assessment, the drift rate D is calculated using the following formula:

[0060]

[0061] in The output value is the value at the end of the stress period. This is the initial output value. This is the nominal full-scale value. When the absolute value of D is greater than 2%, it is marked as parameter drift. In frequency domain analysis, the signal is decomposed into a time-frequency representation using short-time Fourier transform to extract the dominant frequency energy. With noise floor level The ratio of signal to noise is defined as the signal-to-noise ratio. The system uses a sliding observation window with a width of 100ms and a step size of 10ms. A performance degradation warning is issued when the SNR drops by more than 3dB over three consecutive observation windows. After calculating the quantification metrics, all feature vectors, including E, D, SNR sequence, spectral peak offset, and communication error count, are input into an anomaly detection algorithm based on gradient boosting decision trees. This algorithm model contains 200 weak classifier decision trees, each with a maximum depth of 8 and a learning rate of 0.1. A second-order Taylor expansion is used to optimize the loss function. The model's training set consists of 100,000 labeled historical fault cases, covering dozens of failure modes such as pin soldering defects, chip cracks, package leakage, and EMC shielding failure. After thorough training, the model achieves an anomaly detection rate greater than 98.5% and a false alarm rate less than 1.2%, accurately distinguishing between normal performance dispersion caused by process variations and early failures caused by physical damage.

[0062] In a performance testing method for automotive sensor production, step S6 involves outputting the test results and providing optimization instructions, uploading a structured report to the central quality management system, triggering an isolation mechanism for non-conforming products, extracting historical test data sequences, analyzing performance degradation trends using a sliding window regression model, and sending process parameter correction suggestions to the production line control system to complete closed-loop quality control.

[0063] Specifically, the inspection results are packaged into a structured report in XML format and uploaded to the central quality management system. The XML uses InspectionReport as its root element, and its child elements include: DeviceID; DefectType; ConfidenceScore; PerformanceData (embedded with values ​​such as deviation index, drift rate, and signal-to-noise ratio); ErrorCode (a five-digit failure code, the first digit representing the failure category and the last four digits representing the specific pattern); DataReference (a URL pointing to the original data and feature map on the local server); and InspectionTime. The report is uploaded via gigabit Ethernet with a transmission latency of less than 50ms. If the system determines a product to be non-conforming upon receipt, it immediately instructs the robotic arm controller to move it to the isolation area. More importantly, the system continuously extracts historical inspection data sequences, especially for products from the same batch or production line, and uses a sliding window regression model to analyze the long-term trends of key performance indicators. The sliding window regression model uses a historical data sequence with a width of 30 sets as input, predicts the average zero-point offset of the next batch of products, and employs the ridge regression algorithm with a coefficient of determination R² of no less than 0.93. The prediction results are transformed into specific process parameter correction suggestions. For example, if the predicted zero-point offset will increase, it is recommended to slightly reduce the welding temperature or reduce the assembly pressure. The feedforward adjustment instructions are sent to the production line control system through the OPC UA protocol, thereby proactively intervening before defects occur on a large scale and completing a complete closed loop from detection to optimization.

[0064] To further improve the overall performance and security of the system, this invention also includes establishing a distributed detection node cluster, with each node sharing a unified parameter database and diagnostic model library, achieving nanosecond-level clock synchronization through a time-sensitive network, supporting simultaneous online detection of no less than 64 sensors, and compressing the average detection cycle of a single item to less than 90 seconds.

[0065] Furthermore, this invention includes a safety interlock protection mechanism that monitors the internal air pressure, leakage current, and vibration table load status of the temperature control chamber in real time during testing. If any parameter exceeds a safety threshold, the energy supply is immediately cut off and an emergency stop procedure is initiated to ensure the safety of equipment and personnel. Simultaneously, this invention also includes the deployment of an edge computing unit to perform raw data compression and feature extraction locally, uploading only key diagnostic results and metadata to the cloud server, reducing bandwidth usage by 85% and meeting automotive-grade data privacy protection requirements.

[0066] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A performance testing method for automotive sensor manufacturing, characterized in that, Includes the following steps: Step S1: Receive the sensor under test and identify its type. Automatically match the corresponding detection process template based on the pre-stored sensor parameter database. Step S2: Perform adaptive clamping and electrical connection. Call the corresponding flexible fixture component according to the sensor type. Drive the multi-axis servo mechanism to position the sensor under test to the test station. Use the vision-assisted alignment system to correct spatial posture deviation. Connect the general probe array and pins or interfaces. Connect the power supply, signal lines and communication bus. Step S3: Apply multi-environmental stress synergistic excitation, control the temperature control cavity to perform temperature cyclic loading, activate the electromagnetic interference generation module to emit radio frequency signals, and apply random vibration spectrum through the vibration table to form a composite stress environment with temperature-electromagnetic-mechanical three-field coupling; Step S4: Collect dynamic response data, synchronously record the output voltage, current, frequency, and digital communication frames of the sensor under composite stress, with a sampling frequency of not less than 1MHz and a time resolution of less than 1 microsecond, cache the data to a high-speed solid-state storage unit, and attach timestamps and environmental parameter tags; Step S5: Perform intelligent data analysis and fault diagnosis, and transfer the collected data... The raw response data of the dataset is compared with the standard benchmark model in three dimensions: time domain, frequency domain, and feature space. The deviation index, drift rate, and signal-to-noise ratio attenuation curve are calculated. An anomaly detection algorithm based on gradient boosting decision tree is used to identify potential failure modes and generate a structured report containing defect location, type, and confidence level. In step S6, the detection results are output and optimization instructions are fed back. The structured report is uploaded to the central quality management system. The isolation mechanism is triggered for non-conforming products. Historical detection data sequences are extracted. The performance degradation trend is analyzed using a sliding window regression model. Suggestions for process parameter correction are sent to the production line control system to achieve closed-loop quality control.

2. The performance testing method for automotive sensor production according to claim 1, characterized in that: In step S1, a unique identification code on the sensor housing is obtained through a non-contact coding and reading device. The unique identification code is then parsed to extract sensor type, interface protocol, measurement range, and batch information. The non-contact coding and reading device uses high-contrast laser imaging technology with a resolution of 1280×1024 pixels, a decoding success rate of over 99.9%, and supports three identification forms: one-dimensional barcode, two-dimensional matrix code, and RFID electronic tag. The parsing response time is less than 200ms.

3. The performance testing method for automotive sensor production according to claim 1, characterized in that: The flexible clamping assembly consists of adaptive jaws driven by shape memory alloy. The jaw surface is covered with a conductive silicone layer with an elastic modulus of 0.5MPa to 3MPa. It is compatible with cylindrical, square and irregularly shaped shells with diameters of 5mm to 80mm, and the docking repeatability is no greater than ±0.02mm.

4. The performance testing method for automotive sensor production according to claim 1, characterized in that: The vision-assisted alignment system is equipped with a binocular industrial camera with a focal length of 8mm and a field of view of 45 degrees. It achieves real-time measurement of pin offset through a sub-pixel edge detection algorithm, with a closed-loop adjustment cycle of 10ms and alignment error controlled within ±5μm.

5. The performance testing method for automotive sensor production according to claim 1, characterized in that: In step S3, the temperature-controlled cavity undergoes cyclic loading at a preset slope within a temperature range of -40℃ to 150℃. The electromagnetic interference generation module emits a programmable intensity radio frequency signal in the 30MHz to 6GHz frequency band. The vibration table applies a random vibration spectrum with a frequency of 5Hz to 2000Hz and a peak acceleration of 50g. The temperature-controlled cavity adopts a Peltier and liquid-cooled composite temperature control structure, with a maximum heating and cooling rate of 15℃ / min and a temperature uniformity within ±1.5℃. The inner wall of the cavity is coated with absorbing material to reduce electromagnetic reflection interference. The electromagnetic interference generation module has an omnidirectional radiating antenna array with a maximum field strength output of 200V / m and a step adjustment accuracy of 0.5dB, capable of simulating the vehicle electromagnetic environment profile specified in the ISO11452 series standards. The vibration table is hydraulically servo driven with an effective load capacity of 100kg, a lateral motion suppression ratio higher than -40dB, and a random vibration power spectral density control error of less than ±1.5dB.

6. The performance testing method for automotive sensor production according to claim 1, characterized in that: The data acquisition system integrates a synchronous multi-channel ADC chip with 64 channels, a vertical resolution of 16 bits, an input impedance greater than 10MΩ, a common-mode rejection ratio higher than 120dB, and supports data frame capture for multiple vehicle bus protocols such as CAN, LIN, FlexRay, and SENT. The timestamp is provided by a GPS-disciplined crystal oscillator, with long-term frequency stability maintained within ±0.1ppm and a time deviation from UTC less than 1μs.

7. The performance testing method for automotive sensor production according to claim 1, characterized in that: The standard benchmark model is constructed from the statistical mean of no less than 1,000 sets of grid samples, covering typical response characteristics under four basic operating conditions: room temperature static, high temperature steady state, low temperature start-up, and transient impact. The model is updated every 7 days. The deviation index is judged as significant deviation when it exceeds the set threshold of 0.

05. The absolute value of the drift rate is marked as parameter drift when it is greater than 2%. The signal-to-noise ratio attenuation curve is extracted by short-time Fourier transform to extract the ratio of the main frequency energy to the noise floor level. A decrease in SNR of more than 3dB for three consecutive observation windows is regarded as a performance degradation warning.

8. The performance testing method for automotive sensor production according to claim 1, characterized in that: The gradient boosting decision tree anomaly detection algorithm contains 200 weak classifiers with a maximum depth of 8 and a learning rate of 0.

1. The training set consists of 100,000 labeled historical fault cases. The anomaly detection rate is greater than 98.5%, and the false alarm rate is less than 1.2%.

9. The performance testing method for automotive sensor production according to claim 1, characterized in that: The structured report is encapsulated in XML format and includes sensor ID, test time, environmental conditions, key indicator values, defect code, image evidence link, and diagnostic conclusion fields, with a transmission delay of less than 50ms. The sliding window regression model uses a historical data sequence with a width of 30 sets, and the prediction target is the average zero-point offset of the next batch of products. The model's coefficient of determination R² is not less than 0.93, which is used to guide the feedforward adjustment of assembly pressure and welding temperature.

10. The performance testing method for automotive sensor production according to claim 1, characterized in that: It also includes establishing a distributed detection node cluster, with each node sharing a unified parameter database and diagnostic model library. Nanosecond-level clock synchronization is achieved through a time-sensitive network, supporting simultaneous online detection of no less than 64 sensors, and reducing the average detection cycle per item to less than 90 seconds. It also includes setting up a safety interlock protection mechanism, which monitors the internal air pressure, leakage current, and vibration table load status of the temperature control chamber in real time during the detection process. If any parameter exceeds the safety threshold, the energy supply is immediately cut off and an emergency stop procedure is initiated. It also includes deploying an edge computing unit to complete the compression and feature extraction of raw data locally, and only upload key diagnostic results and metadata to the cloud server, reducing bandwidth usage by 85%.

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