Self-diagnosis system and method for strain type torque angle sensor

Abstract

The application relates to a self-diagnosis system and method of a strain type torque angle sensor, and the system comprises a signal acquisition module for acquiring original sine and cosine signals of the torque angle sensor; a core self-diagnosis module for performing multi-dimensional fault diagnosis, wherein a dynamic trajectory diagnosis unit detects nonlinear distortion and sticking by analyzing the deviation of the trajectory of the signal in a coordinate system from a standard circle, a health degree evaluation unit calculates a health index and predicts a remaining life based on historical data, and a fault tracing unit distinguishes between a sensor self-fault and an external mechanical fault through active testing and cross verification; and a decision and output module outputs sensor data and a health state word after diagnosis and verification. Thus, the problems of single diagnosis dimension, lack of predictive maintenance capability and insufficient fault tracing capability of the torque angle sensor in the related art are solved, the diagnosis coverage and accuracy of the torque angle sensor are improved, and the safety performance of an automobile is ensured.

Description

Technical Field

[0001] This application relates to the fields of automotive electronics technology and sensor fault diagnosis technology, and in particular to a self-diagnostic system and method for a strain gauge torque angle sensor. Background Technology

[0002] The torque and angle sensor (TAS) is a core component of the electric power steering (EPS) system in automobiles, and its reliability directly affects the vehicle's handling safety and driving experience. Currently, mainstream TAS systems possess advantages such as high precision (torque resolution up to 0.0029°), zero temperature drift (-40°C to 150°C), and strong mechanical robustness.

[0003] However, the TAS diagnostic technology in related technologies has the following shortcomings. On the one hand, the technology only performs threshold checks on the voltage amplitude and range of the output signal, and cannot identify "soft faults" such as signal waveform distortion, phase deviation, and nonlinear distortion. When the sine (sin) and cosine (cos) signals are simultaneously attenuated or distorted, but the amplitude is still within a reasonable range, the technology cannot effectively detect this, posing a safety hazard. On the other hand, the technology cannot perceive the gradual degradation of sensor performance and cannot provide early warning before it completely fails, making it a "post-fault alarm" rather than a "pre-fault prevention." In addition, when the diagnostic system of the related technology reports an error, it is difficult to distinguish whether the fault is due to the sensor itself, an external circuit problem, or an abnormality in the connected mechanical components (such as a torsion bar), resulting in low maintenance efficiency and even misjudgment.

[0004] Therefore, there is an urgent need for a strain gauge torque angle sensor self-diagnostic solution that can perform multi-dimensional, in-depth diagnosis and enable predictive maintenance and accurate fault tracing. Summary of the Invention

[0005] This application provides a self-diagnostic system and method for a strain gauge torque angle sensor to solve the problems of limited diagnostic dimensions, lack of predictive maintenance capabilities, and insufficient fault tracing capabilities of torque angle sensors in related technologies, thereby improving the diagnostic coverage and accuracy of torque angle sensors and ensuring vehicle safety performance.

[0006] The first aspect of this application provides a self-diagnostic system for a strain gauge torque angle sensor, comprising: a signal acquisition module, a core self-diagnostic module, and a decision and output module, wherein...

[0007] The signal acquisition module is used to acquire the original sine signal value and the original cosine signal value of the torque angle sensor;

[0008] The core self-diagnosis module is used to perform multi-dimensional fault diagnosis on the original sine signal value and the original cosine signal value.

[0009] The decision and output module is used to output the sensor data and health status words after diagnostic verification;

[0010] The core self-diagnosis module includes:

[0011] The dynamic trajectory diagnosis unit performs a fitting degree analysis on the trajectory graphic formed by continuously collected data points in a Cartesian coordinate system composed of the original sine signal value and the original cosine signal value. By calculating the roundness error or radius variance between the trajectory graphic and the preset standard circle, it determines whether the torque angle sensor has nonlinear distortion or jamming at arbitrary angle positions.

[0012] The health assessment unit calculates the current health index of the torque angle sensor based on the historical diagnostic data of the dynamic trajectory diagnosis unit and / or signal quality assessment unit through a performance degradation model, and estimates the remaining service life of the torque angle sensor based on the degradation trend of the current health index.

[0013] The fault tracing unit is used to distinguish between faults in the torque angle sensor itself and faults in external mechanical connections through a combination of active testing and cross-validation.

[0014] Optionally, in some embodiments, the fault tracing unit includes:

[0015] The fault injection subunit is used to apply a preset test excitation signal to the signal link of the torque angle sensor when the torque angle sensor is powered on and initialized or under a preset safe operating condition, and to verify the integrity of the internal functional link of the torque angle sensor based on the deviation between the output response of the torque angle sensor and the expected response.

[0016] The cross-validation subunit is used to compare the torque signal and / or angle signal output by the torque angle sensor with the signal of at least one auxiliary sensor in the vehicle system that has a physical or logical relationship in real time, and to locate the fault source by judging the physical and logical rationality between the signals.

[0017] Optionally, in some embodiments, the preset test excitation signal is a millisecond-level small amplitude disturbance to the power supply voltage of the excitation coil of the torque angle sensor, or a transient capacitive load switch to the sensing signal acquisition circuit.

[0018] Optionally, in some embodiments, the cross-validation subunit is further configured to:

[0019] Under the condition that the vehicle is driving in a straight line and making minor adjustments to the steering wheel, a mathematical relationship model is established between the torque value output by the torque angle sensor and the motor resistance torque measured by the motor encoder.

[0020] Real-time monitoring is conducted to determine whether the actual deviation between the torque value and the motor resistance torque exceeds the reasonable range determined based on the mathematical relationship model.

[0021] If the actual deviation continues to exceed the reasonable range, it is determined that the torque angle sensor has an external mechanical connection fault.

[0022] Optionally, in some embodiments, the dynamic trajectory diagnosis unit is specifically used for:

[0023] The least squares method is used to fit the sine and cosine signal data points of multiple consecutive sampling periods into a circle;

[0024] Calculate the distance from each data point to the center of the fitted circle, and obtain the standard deviation or variance of the distances from all data points to the center of the fitted circle as the radius variance;

[0025] The radius variance is compared with a preset variance threshold. If the radius variance continues to exceed the preset variance threshold, the trajectory pattern is determined to be abnormal.

[0026] Optionally, in some embodiments, the formula for calculating the current health index in the health assessment unit is:

[0027] HI(t) = K / V(t);

[0028] Wherein, HI(t) is the health index of the torque angle sensor at the current time t, K is the normalization coefficient calibrated based on the initial health state of the torque angle sensor, and V(t) is the moving average of the radius variance calculated by the dynamic trajectory diagnosis unit at the current time t.

[0029] The health assessment unit is further configured to: record the sequence of health index changes over time, fit the health index decay curve using linear regression or exponential smoothing, and determine the remaining service life as the time corresponding to the current health index decaying to a preset failure threshold.

[0030] Optionally, in some embodiments, the core self-diagnostic module further includes:

[0031] A signal quality assessment unit is used to verify whether the sum of the squares of the original sine signal value and the original cosine signal value remains near a constant theoretical value;

[0032] If the deviation between the sum of squares and the theoretical value exceeds the preset tolerance range, it is determined that there is an abnormality in the signal generator or the calculation circuit.

[0033] A second aspect of this application provides a self-diagnostic method for a strain gauge torque angle sensor, comprising the following steps:

[0034] Obtain the raw sine and raw cosine signal values ​​from the torque angle sensor;

[0035] Dynamic trajectory diagnosis, health assessment, and fault tracing are performed on the original sine signal value and the original cosine signal value.

[0036] Output a diagnostic report based on diagnostically validated sensor data and health status words;

[0037] The dynamic trajectory diagnosis, health assessment, and fault tracing of the original sine signal value and the original cosine signal value specifically include:

[0038] In a Cartesian coordinate system composed of the original sine signal value and the original cosine signal value, the fitting degree of the trajectory graph formed by the continuously collected data points is analyzed, and the roundness error or radius variance of the trajectory graph with the preset standard circle is calculated to determine whether the torque angle sensor has nonlinear distortion or jamming at arbitrary angle position.

[0039] Based on historical diagnostic data generated by the dynamic trajectory diagnosis and / or signal quality assessment process, the current health index of the torque angle sensor is calculated through a performance degradation model, and the remaining service life of the torque angle sensor is estimated based on the degradation trend of the current health index.

[0040] By combining active testing with cross-validation, we can distinguish between faults in the torque angle sensor itself and faults in external mechanical connections.

[0041] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a self-diagnostic method for a strain gauge torque angle sensor as described in the above embodiments.

[0042] A fourth aspect of this application provides a computer program product having a computer program stored thereon, which is executed by a processor to implement a self-diagnostic method for a strain gauge torque angle sensor as described in the above embodiments.

[0043] The beneficial effects of the embodiments of this application are as follows:

[0044] (1) This application combines correlation diagnosis and dynamic trajectory diagnosis, which can discover hidden faults and soft faults that cannot be identified by related technologies, thereby improving the coverage of torque angle sensor fault diagnosis.

[0045] (2) This application introduces a performance degradation model, which can provide early warning of the degradation of torque angle sensor performance, realize the leap from "passive alarm" to "active warning", and improve the safety level of automobiles;

[0046] (3) This application can effectively distinguish whether the torque angle sensor is faulty due to electronic components or external mechanical connection through fault injection and cross-validation, providing key information for guiding maintenance and reducing misjudgment and invalid replacement.

[0047] (4) This application combines and integrates multiple diagnostic methods to effectively solve the long-standing technical problems in the field of sensor fault diagnosis. At the same time, the solution of this application is based on existing sensor hardware, which does not require additional cost and is easy to industrialize.

[0048] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0049] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein...

[0050] Figure 1 This is a schematic diagram of the structure of a self-diagnostic system for a strain gauge torque angle sensor provided according to an embodiment of this application.

[0051] Figure 2 This is a schematic diagram of a Lissajous figure in a sin-cos plane rectangular coordinate system according to a specific embodiment of this application.

[0052] Figure 3 This is a flowchart illustrating the implementation steps of a health assessment unit according to a specific embodiment of this application.

[0053] Figure 4 This is a schematic diagram of a fault tracing mechanism according to a specific embodiment of this application.

[0054] Figure 5 This is a flowchart of a self-diagnostic method for a strain gauge torque angle sensor provided according to an embodiment of this application.

[0055] Figure 6 This is a flowchart of a dynamic trajectory diagnosis, health assessment, and fault tracing method provided according to an embodiment of this application.

[0056] Figure 7 This is a block diagram of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0057] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0058] The following description, with reference to the accompanying drawings, illustrates a self-diagnostic system and method for a strain gauge torque angle sensor according to embodiments of this application. Addressing the problems mentioned in the background art regarding the limited diagnostic dimensions, lack of predictive maintenance capabilities, and insufficient fault tracing capabilities of torque angle sensors, this application first provides a self-diagnostic system for a strain gauge torque angle sensor. Specifically, Figure 1 This is a schematic diagram of the structure of a self-diagnostic system for a strain gauge torque angle sensor provided in an embodiment of this application.

[0059] like Figure 1 As shown, the self-diagnostic system 10 of the strain gauge torque angle sensor includes: a signal acquisition module 100, a core self-diagnostic module 200, and a decision and output module 300.

[0060] The signal acquisition module 100 is used to acquire the original sine signal value and the original cosine signal value of the torque angle sensor.

[0061] The signal acquisition module 100 in this embodiment is a collection of hardware circuits (such as analog front-end) and software drivers (such as ADC (Analog-to-Digital Converter) sampling programs), responsible for acquiring raw electrical signals from physical sensors and converting them into digital quantities that can be processed by subsequent modules.

[0062] In this application, the torque angle sensor is a sensor based on the principle of induction (such as Shenming's TAS in the background art).

[0063] Understandably, if processed angle and torque values ​​are collected, many underlying faults (such as minor coil short circuits, signal amplitude attenuation, and waveform distortion) will be masked or "averaged out" during the calculation process. Therefore, this embodiment obtains the original sine and cosine signal values ​​to obtain complete information containing the sensor's health status. Thus, the signal acquisition module 100 of this embodiment obtains the most accurate and fundamental sensor status information, providing a data foundation for subsequent in-depth diagnostics.

[0064] Specifically, on the PCB (Printed Circuit Board) of the torque angle sensor, the excitation coil generates an alternating magnetic field, and the induction coil generates a changing voltage due to the disturbance of the rotor gear, which are the original sine and original cosine signals in this embodiment. The signal acquisition module 100 conditions these two original signals through circuits such as operational amplifiers. Optionally, the conditioning operation includes amplification (because the signal is very weak) and filtering (removing high-frequency noise).

[0065] It should be noted that, under the control of the microcontroller, this embodiment uses synchronous sampling technology to simultaneously capture the instantaneous voltage values ​​of the original sine and cosine signals at the same time. In actual execution, "synchronization" can effectively avoid errors caused by time differences in the subsequently constructed coordinate points.

[0066] The analog-to-digital converter then converts the captured analog voltage values ​​into digital quantities, namely the original sine and cosine signal values ​​according to this embodiment. The signal acquisition module 100 sends these digital values ​​to the core self-diagnosis module 200 for processing.

[0067] Therefore, the signal acquisition module acquires the original orthogonal signals from the torque angle sensor with high precision and synchronization, providing a real and reliable data foundation for subsequent diagnostic functions such as dynamic trajectory analysis and health assessment.

[0068] The core self-diagnostic module 200 is used to perform multi-dimensional fault diagnosis on the original sine signal value and the original cosine signal value.

[0069] The core self-diagnosis module 200 in this application embodiment is a software algorithm system integrated inside the sensor controller. As the "brain" of the entire self-diagnosis system, it receives raw signals, executes a series of advanced diagnostic algorithms, and realizes in-depth perception, evaluation and fault root cause location of the sensor health status.

[0070] Understandably, the diagnostics of sensors in related technologies are limited to simple checks such as signal out-of-range, and cannot cope with slow performance degradation, complex nonlinear faults, or precise fault source localization. This application, however, employs a multi-dimensional, cross-level information fusion analysis strategy to elevate post-event alarms to in-process diagnosis and pre-event warning, thereby achieving intelligent health management of the sensor throughout its entire lifecycle.

[0071] Specifically, such as Figure 1As shown, the core self-diagnostic module 200 receives raw sine and cosine signal values ​​from the signal acquisition module 100. The dynamic trajectory diagnosis unit 201 analyzes the instantaneous quality of the raw signals, the health assessment unit 202 predicts the future state of the sensor based on historical data, and the fault tracing unit 203 accurately locates the fault source when a sensor problem is detected. The three units within the core self-diagnostic module 200 operate in parallel and are interconnected. Therefore, by combining the outputs of the three units, the core self-diagnostic module 200 can understand the overall state of the sensor being tested.

[0072] Therefore, the core self-diagnostic module of this application embodiment integrates real-time signal analysis, long-term trend prediction and active verification diagnosis, transforming the torque angle sensor from a simple sensing component into an intelligent device with self-sensing, self-predicting and self-diagnostic capabilities, providing reliability assurance for scenarios with high safety requirements such as vehicle steering systems.

[0073] Furthermore, such as Figure 1 As shown, the core self-diagnosis module 200 includes: a dynamic trajectory diagnosis unit 201, a health assessment unit 202, and a fault tracing unit 203.

[0074] The dynamic trajectory diagnosis unit 201 performs a fitting degree analysis on the trajectory graphic formed by continuously collected data points in a Cartesian coordinate system composed of original sine signal values ​​and original cosine signal values. By calculating the roundness error or radius variance between the trajectory graphic and the preset standard circle, it determines whether the torque angle sensor has nonlinear distortion or jamming at arbitrary angle positions.

[0075] In this embodiment, the Cartesian coordinate system is a virtual analysis plane with the original cosine signal value cos(θ) as the X-axis and the original sine signal value sin(θ) as the Y-axis. In this embodiment, the trajectory graph formed by continuously collected data points is a Lissajous figure, which is a graph formed by connecting data points generated by the sensor during continuous rotation within the Cartesian coordinate system of this embodiment. The roundness error and radius variance in this embodiment quantify the degree to which the actual trajectory deviates from the ideal circle. The radius variance is a statistical indicator reflecting the degree of dispersion of the data points relative to the fitted circle center.

[0076] As will be understood by those skilled in the art, an ideal sensor based on inductive technology outputs [sin(θ)]. 2 +[cos(θ)] 2 =1, the trajectory in the coordinate system is a standard circle. Any physical imperfection (such as coil asymmetry, rotor eccentricity, mechanical jamming) will disrupt this ideal orthogonality and amplitude relationship, causing the trajectory to be distorted into an ellipse, burr rings, or local flattening. For example, Figure 2This is a schematic diagram of a Lissajous figure in a sin-cos plane rectangular coordinate system according to a specific embodiment of this application. Figure 2 In the diagram, (a) represents the normal trajectory, with cos(θ) as the horizontal axis and sin(θ) as the vertical axis. Under ideal conditions, it presents a regular, standard circle. Figure 2 (b) in the figure represents the trajectory of the abnormal state (elliptical distortion). The actual trajectory deviates from the ideal circle (dashed line) and presents a distorted shape with undulating edges and irregular shape.

[0077] The dynamic trajectory diagnostic unit 201 of this application embodiment detects deep, hidden electrical and mechanical faults by detecting the above-mentioned geometric distortions.

[0078] Optionally, in some embodiments, the dynamic trajectory diagnosis unit 201 is specifically used to: fit sine signal data points and cosine signal data points of multiple consecutive sampling periods into a circle using the least squares method; calculate the distance from each data point to the center of the fitted circle, and obtain the standard deviation or variance of the distances from all data points to the center of the fitted circle as the radius variance; compare the radius variance with a preset variance threshold, and if the radius variance continuously exceeds the preset variance threshold, determine that the trajectory pattern is abnormal.

[0079] Specifically, this application takes the least squares method as an example. During the process of the car steering wheel rotating at a constant speed for at least one revolution, N synchronous data points (cos(θ_n), sin(θ_n)) are collected. Using the least squares algorithm, a circle center (a, b) and radius R are found such that the sum of the squares of the geometric distances from all data points to this circle is minimized. This circle is the "fitted circle" that best represents the current data characteristics. Then, the actual distance d_n from each data point to the fitted circle center (a, b) is calculated, and the standard deviation or variance of all d_n is obtained. The larger the variance value, the less complete the trajectory graph is.

[0080] Furthermore, in this embodiment of the application, the calculated radius variance is compared with a preset variance threshold calibrated through a large number of good product experiments. If the variance of multiple consecutive calculation cycles exceeds the preset variance threshold, it is determined that there is an abnormality inside the sensor, and a diagnostic alarm is triggered.

[0081] Considering that dynamic trajectory analysis alone may not be able to capture rapid signal change faults in a timely manner, the self-diagnostic system of this application embodiment can synchronously run a high-frequency [sin(θ)]... 2 +[cos(θ)] 2 Calculation. For example, suppose that at a certain moment, due to strong external electromagnetic interference or a transient fault in the internal circuit, the amplitude of the sin signal drops by 50% instantaneously, while the cos signal remains normal. In this case, the ideal sum of squares should be 1, but the actual calculated value may become (0.5). 2 +(1)2 =1.25, which is a significant deviation from the theoretical value. The self-diagnostic system will immediately detect this sudden change and, in conjunction with trajectory analysis, determine it as a transient signal distortion, thereby achieving supplementary capture of rapid and latent faults.

[0082] Therefore, the dynamic trajectory diagnostic unit of this application embodiment can accurately detect nonlinear distortion and local mechanical jamming inside the sensor that cannot be identified by related technologies by converting abstract voltage signals into intuitive geometric shape analysis, thus providing a deep insight into signal quality for the self-diagnostic system.

[0083] The health assessment unit 202 calculates the current health index of the torque angle sensor based on the historical diagnostic data of the dynamic trajectory diagnosis unit 201 and / or the signal quality assessment unit through a performance degradation model, and estimates the remaining service life of the torque angle sensor based on the degradation trend of the current health index.

[0084] In this application embodiment, the Health Index (HI) is a quantified value between 0 and 1 (or 0% to 100%), used to characterize the sensor's current performance as a percentage of its brand-new state. The remaining lifetime in this application embodiment is the predicted time remaining from the current moment until the sensor's Health Index drops to a preset failure threshold. The performance degradation model in this application embodiment is a mathematical model describing the degradation of sensor performance over time and with use.

[0085] It is understandable that sensor aging (such as coil insulation aging and chip performance drift) is a slow and gradual process. The health assessment unit 202 in this application establishes a mapping relationship between key indicators that reflect sensor performance (such as signal noise and nonlinear error) and health status by monitoring their long-term changing trends, thereby providing early warnings before failures occur and achieving predictive maintenance.

[0086] Optionally, in some embodiments, the formula for calculating the current health index in the health assessment unit 202 is:

[0087] HI(t) = K / V(t);

[0088] Where HI(t) is the health index of the torque angle sensor at the current time t, K is the normalization coefficient based on the initial health status calibration of the torque angle sensor, and V(t) is the moving average of the radius variance calculated by the dynamic trajectory diagnosis unit at the current time t.

[0089] Specifically, considering that the radius variance can reflect the deterioration of signal quality, this embodiment selects the radius variance V(t) calculated by the dynamic trajectory diagnosis unit 201 as the core health indicator and establishes a health index model: HI(t)=K / V(t).

[0090] This application provides the derivation process of the above-mentioned health index calculation formula (health index model). First, initial calibration is performed, i.e., when the sensor is brand new, its radius variance is measured over a period of time under standard conditions, and its initial average value V_initial is calculated. A normalization coefficient K is set such that the initial health index HI_initial = K / V_initial = 1 (or 100%), thus K = V_initial, and this K value is stored. Real-time calculation is then possible. During vehicle use, the self-diagnostic system periodically (e.g., every 100 kilometers) calculates the recent moving average of the radius variance V(t) and substitutes it into the formula HI(t) = K / V(t). It should be noted that as the sensor ages, V(t) will slowly increase, causing HI(t) to gradually decrease from 1.

[0091] The health assessment unit 202 is also used to: record the sequence of health index changes over time, fit the health index decay curve using linear regression or exponential smoothing, and determine the remaining service life as the time corresponding to the current health index decaying to a preset failure threshold.

[0092] Specifically, the self-diagnostic system can predict the remaining lifespan of a sensor. First, it continuously records data, specifically a series of time points t_i and their corresponding health index HI(t_i). Then, it uses linear regression or exponential smoothing to fit these data points, obtaining a decay curve of HI over time t. Finally, it extrapolates this fitted curve to find its intersection point with a preset failure threshold (e.g., HI_failure is 0.5, representing a 50% performance degradation). The difference between the time t_failure at this intersection point and the current time t_now is the predicted remaining lifespan.

[0093] In some embodiments, Figure 3 This is a flowchart illustrating the implementation steps of a health assessment unit according to a specific embodiment of this application, as follows: Figure 3As shown, this application first initializes health-related parameters, including: the normalization coefficient K based on the initial health status calibration of the torque angle sensor, the initial average value V_initial, and the preset failure threshold HI_failure; it collects the current radius variance data V(t), calculates its moving average value V_avg(t), and then obtains the health index HI(t) using the formula HI(t)=K / V_avg(t), and stores its historical sequence; then it determines whether the historical data meets the minimum requirement. If it is insufficient, it waits for more data. If it is sufficient, it uses linear regression to fit the decay curve of HI and calculates RUL (Remaining Useful Life, i.e., the difference between the failure time t_failure and the current time t_current); finally, it determines whether HI is less than or equal to the warning threshold. If so, it generates a health warning and outputs HI and RUL to the decision module. Otherwise, it continues monitoring, and the process ends or enters the next cycle.

[0094] Therefore, the health assessment unit in this application embodiment monitors the long-term drift of key performance parameters and uses mathematical models to extrapolate trends, thereby predicting the remaining lifespan of the sensor. This changes the maintenance strategy from "repairing when it breaks" to "early warning and maintenance as needed," greatly improving the safety and economy of the self-diagnostic system.

[0095] The fault tracing unit 203 is used to distinguish between faults in the torque angle sensor itself and faults in external mechanical connections through a combination of active testing and cross-validation.

[0096] Understandably, when a self-diagnostic system detects an anomaly, it is not enough to simply know that "there is a problem"; it is also necessary to know "where the problem lies." It needs to determine whether the sensor itself is faulty or whether a connected mechanical component (such as a torsion bar) is malfunctioning. The fault tracing unit 203 in this embodiment combines "internal self-testing" (fault injection) and "external verification" (cross-validation) to achieve precise isolation and location of the fault source.

[0097] Optionally, in some embodiments, the fault tracing unit 203 includes: a fault injection subunit and a cross-validation subunit.

[0098] The fault injection subunit is used to apply a preset test excitation signal to the signal link of the torque angle sensor when the torque angle sensor is powered on and initialized or under preset safe operating conditions, and to verify the integrity of the internal functional link of the torque angle sensor based on the deviation between the output response of the torque angle sensor and the expected response.

[0099] Fault injection is an active testing technique that uses a known, small stimulus applied to a system and observes its response to determine whether the system is internally sound.

[0100] Optionally, in some embodiments, the preset test excitation signal is a millisecond-level small amplitude disturbance to the power supply voltage of the excitation coil of the torque angle sensor, or a transient capacitive load switch to the induction signal acquisition circuit.

[0101] Specifically, under safe operating conditions such as vehicle start-up self-test or straight-line driving, the fault injection subunit applies a voltage pulse (a test excitation) lasting 5 milliseconds with an amplitude increase of 50mV to the excitation coil of the torque angle sensor, and monitors the output signal of the induction coil. Understandably, a healthy sensor will produce a response signal with predictable amplitude, phase, and waveform. If the response signal is significantly different from or missing from expectations, it indicates a fault in the signal link within the sensor (such as the coil or front-end circuitry).

[0102] The cross-validation subunit is used to compare the torque signal and / or angle signal output by the torque and angle sensor with the signal of at least one auxiliary sensor in the vehicle system that has a physical or logical relationship in real time, and to locate the fault source by judging the physical and logical rationality between the signals.

[0103] Cross-validation utilizes the known physical and logical relationships between multiple sensor signals in a system to detect and locate anomalies by comparing their consistency.

[0104] Optionally, in some embodiments, the cross-validation subunit is further configured to: establish a mathematical relationship model between the torque value output by the torque angle sensor and the motor resistance torque measured by the motor encoder when the vehicle is currently driving in a straight line and finely adjusting the steering wheel; monitor in real time whether the actual deviation between the torque value and the motor resistance torque exceeds the reasonable range determined based on the mathematical relationship model; if the actual deviation continues to exceed the reasonable range, determine that the torque angle sensor has an external mechanical connection fault.

[0105] Specifically, the cross-validation subunit establishes a relationship model between the torque T output by the TAS and the resistance torque T_m measured by the EPS motor encoder when the vehicle is driving straight and making minor steering wheel adjustments. It should be noted that under ideal mechanical connections, T and T_m should be highly linearly correlated and their deviation should be stable. The cross-validation subunit monitors the actual deviation between T and T_m in real time. If this deviation consistently exceeds the reasonable range defined by the model, but the fault injection test shows that the sensor internals are normal, it strongly indicates that the fault originates from the external mechanical connection, such as plastic deformation of the torsion bar or excessive gear backlash.

[0106] As can be seen from the above embodiments, the fault tracing unit 203 of this application embodiment achieves accurate location of the fault source through the collaborative mechanism of active injection testing and multi-source information cross-verification. It can effectively distinguish between sensor faults and external mechanical faults, providing clear guidance for maintenance, avoiding misjudgment and ineffective replacement, and improving the maintainability and safety reliability of the system.

[0107] Figure 4 This is a schematic diagram of the fault tracing mechanism according to a specific embodiment of this application, such as... Figure 4 As shown, the fault tracing unit 203 includes a fault injection subunit and a cross-validation subunit. The fault injection subunit verifies the integrity of the sensor's internal functional links by applying a preset test excitation signal to the signal link of the torque angle sensor and analyzing the deviation between the sensor's output response and the expected response. The cross-validation subunit locates the fault source by comparing the torque signal and / or angle signal output by the torque angle sensor with signals from other auxiliary sensors in the vehicle system in real time and judging the physical logic rationality between the signals. The fault tracing unit 203 integrates the analysis results of the two subunits, distinguishes between sensor faults and external mechanical connection faults, and outputs the fault location information to the decision and output module 300.

[0108] Optionally, in some embodiments, the core self-diagnostic module 200 further includes: a signal quality assessment unit, used to verify whether the sum of squares of the original sine signal value and the original cosine signal value is maintained near a constant theoretical value; if the deviation of the sum of squares from the theoretical value exceeds a preset tolerance range, it is determined that there is an abnormality in the signal generator or the calculation circuit.

[0109] The signal quality assessment unit in this embodiment is a basic diagnostic unit in the core self-diagnostic module 200, responsible for performing real-time and basic quality checks on the sensor's most original sin(θ) and cos(θ) signals.

[0110] Here, the sum of the squares of the original sine and cosine signal values ​​refers to the mathematical calculation [sin(θ)]. 2 +[cos(θ)] 2 The constant theoretical value refers to the value of [sin(θ)] for an ideal orthogonal sensor, regardless of how the angle θ changes. 2 +[cos(θ)] 2 The result should always be equal to 1. The preset tolerance range of this application embodiment is a pre-set, allowable error boundary. Since actual sensors have small noise and errors, the sum of squares cannot be absolutely equal to 1. Therefore, this application embodiment sets a reasonable range (such as 1.0±0.1) to distinguish between normal fluctuations and real faults.

[0111] Based on the above embodiments, it can be understood that the self-diagnostic system of this application is used to detect "hard faults" and severe degradation of sensor signal links. The diagnostic method of this system is based on a solid mathematical theory. The destruction of ideal sine waves and cosine waves will be directly reflected in the deviation of this identity. Therefore, this application uses this diagnostic criterion to make the self-diagnostic system more robust and universal.

[0112] This application can simultaneously detect various faults affecting signal amplitude by verifying whether the sum of the squares of the original sine signal value and the original cosine signal value remains near a constant theoretical value. These faults include, for example, aging of the excitation coil or failure of the drive circuit (causing overall signal amplitude attenuation); short circuit / open circuit of the induction coil (causing loss of one or two signals); drift of the ADC reference voltage of the solution chip (causing signal amplitude scaling); and severe electromagnetic interference (causing signal saturation or distortion).

[0113] Furthermore, the calculation is very simple, has extremely low processor requirements, and can achieve high-frequency real-time monitoring, ensuring an immediate response in the event of a serious failure.

[0114] Therefore, the signal quality assessment unit in this embodiment of the application provides the sensor with a fast and reliable first-level fault detection capability by continuously verifying a basic mathematical identity. It can capture major anomalies on the signal link in real time and provide a basic guarantee to prevent system-level failures caused by signal source errors.

[0115] The decision and output module 300 is used to output the sensor data and health status words after diagnostic verification.

[0116] Among them, the sensor data that has been diagnosed and verified refers to the original torque and angle signals that have been marked as "reliable" or "unreliable" after undergoing a series of verifications such as dynamic trajectory diagnosis and signal quality assessment. The decision and output module 300 will decide whether to output the real data or a substitute value (such as a default value or the previous valid value) based on the diagnostic results.

[0117] The health status word in this application embodiment is one or more standardized encoded digital codes that comprehensively summarize the current health status, fault type, and level of the sensor in a compact format (such as an 8-bit or 16-bit binary number), with each bit or field having a specific meaning. For example, the health status word in this application embodiment could be: 0x91, meaning: health warning, signal quality degradation; 0xC4, meaning: fatal fault, external mechanical connection failure.

[0118] Understandably, for safety-critical systems like EPS, simply stopping data output when a sensor malfunctions could lead to system instability. Therefore, this application outputs a definitive, safe status according to a pre-defined safety strategy (such as the safety mechanisms in the ISO 26262 standard), guiding the system into a degraded or safe mode. Furthermore, vehicle control systems need to quickly acquire information via a standard bus; a "health status word" containing rich information is far more efficient than lengthy raw diagnostic data, allowing the receiver to make rapid decisions through simple table lookups or bit operations.

[0119] Specifically, the decision and output module 300 synchronously receives inputs from each diagnostic unit, including: a "signal reliability" flag from the signal quality assessment unit, a "trajectory anomaly" flag and radius variance value from the dynamic trajectory diagnostic unit 201, a health index (HI) and remaining service life (RUL) from the health assessment unit 202, a fault type code from the fault tracing unit 203, and raw torque and angle data from the signal acquisition module 100. The decision and output module 300 generates a health status word according to preset logic.

[0120] For example, the [7-6]th bit of the health status word is the global status flag, where 00 indicates normal, 01 indicates warning, 10 indicates error, and 11 indicates fatal fault; the [5]th bit is the signal quality flag, where 0 indicates normal and 1 indicates abnormal; the [4]th bit is the dynamic trajectory flag, where 0 indicates normal and 1 indicates abnormal; the [3-2]th bits are the health level flag, where 00 indicates health greater than 80%, 01 indicates health between 50% and 80%, 10 indicates health between 20% and 50%, and 11 indicates health less than 20%; the [1-0]th bits are the fault source flag, where 00 indicates no fault, 01 indicates internal fault, 10 indicates external fault, and 11 is undefined. For example, the decision and output module 300 receives "signal quality abnormal" and "health level below 50%", and then generates a health status word based on the above rules as: 0b11_1_0_10_01 (binary), which means "fatal fault, signal abnormality, low health level, originating from internal fault".

[0121] Furthermore, when all sensors are functioning normally, the decision and output module 300 directly outputs highly reliable sensor data that has undergone filtering and other processing. When a sensor malfunction is detected, the decision and output module 300 executes a safety strategy based on the fault level. For example, for a "warning" level fault, the decision and output module 300 still outputs the actual data, but alerts the vehicle system through a health status word. For a "fatal" level fault, the decision and output module 300 stops outputting the actual data and instead outputs a pre-calibrated safety alternative value (such as torque of 0), while the health status word indicates that the data is unreliable, thereby preventing the EPS system from providing incorrect assistance based on erroneous data.

[0122] Finally, the decision and output module 300 packages and outputs the diagnostically verified sensor data and health status words to the next-level vehicle controller via a specified communication protocol.

[0123] In actual operation, the decision and output module 300 in the vehicle's EPS system receives all diagnostic results from the core self-diagnosis module 200. The core self-diagnosis module 200 can generate health status words for the sensors and execute corresponding actions.

[0124] In some embodiments, the self-diagnostic system detects that the HI has dropped to 70% and that the recent radius variance has an increasing trend, but no immediate fault code is triggered. In this case, the vehicle system will not restrict the EPS function, but will send a warning message to the driver or cloud service platform through the self-diagnostic system, "It is recommended to check the steering sensor at the next maintenance".

[0125] In other embodiments, the cross-validation subunit continuously detects a severe mismatch between the torque signal and the motor resistance torque, and the fault injection test passes (proving that the sensor circuit is intact). The self-diagnostic system determines that a serious mechanical fault (such as torsion bar breakage) has occurred at this time. It immediately illuminates the red warning light on the instrument panel through the decision and output module 300 and gradually reduces the EPS assist level (entering limp mode). At the same time, it forcibly records the fault code for maintenance personnel to read, directly pointing to the repair of mechanical components.

[0126] Therefore, the decision and output module of this application embodiment integrates multi-dimensional diagnostic information and follows functional safety principles to transform the complex internal state of the sensor into a standardized "data-state word" output, providing the vehicle control system with a clear and reliable decision basis and an indispensable safety guarantee, ensuring that even if the sensor itself fails, the whole vehicle system can make a safe and controllable response.

[0127] To enable those skilled in the art to further understand a self-diagnostic system for a strain gauge torque angle sensor according to an embodiment of this application, the following specific embodiments will be provided to illustrate the workflow of the self-diagnostic system.

[0128] This application takes a torque angle sensor used in Shenming EPS as an example. The mechanical structure of this torque angle sensor includes an input shaft (18-tooth rotor), an output shaft (9-tooth rotor), and a torsion bar. The system architecture includes multiple IC (Integrated Circuit) modules (IC100 / IC200 / IC300). The self-diagnostic system of this application is integrated into the main signal processing chip (such as IC100) in the form of a software algorithm module, or as the core function of an independent safety monitoring chip (IC200).

[0129] Specifically, the signal acquisition module continuously reads the original sin(θ) and cos(θ) signals from the induction coil.

[0130] The signal quality assessment unit calculates [sin(θ)] every 10ms. 2 +[cos(θ)] 2 Under fault-free conditions, this value should be stable within the range of 1.0±0.1. If more than 95 out of 100 consecutive calculations fall outside the range of 1.0±0.1, the "signal correlation failure" flag will be triggered.

[0131] During the steering wheel rotation, the dynamic trajectory diagnostic unit records at least one sin-cos data point for 360°. The dynamic trajectory diagnostic unit uses the least squares method to fit these points into a circle and calculates the variance of the radius of the fitted circle. If the variance exceeds the threshold, the trajectory is judged to be abnormal and a "dynamic trajectory distortion" warning is triggered.

[0132] The health assessment unit averages the calculated radius variance monthly and records its changes. Assuming the initial average variance is V0, and it increases to V1 after one year, the health index HI = V0 / V1. When HI drops to a preset threshold (e.g., 0.7), the system sends a warning message to the vehicle's dashboard suggesting a check of the steering sensors.

[0133] Each time the vehicle starts, the fault tracing unit controls the fault injection subunit to apply a small, known capacitive load disturbance to the excitation coil at angles of 0° and 180°, and detects the change in the induced signal. If the change does not match the expectation, it is determined that there is a fault in the sensor's internal circuitry. Simultaneously, the cross-validation subunit continuously compares the absolute angle calculated by the TAS with the angle integrated by the motor encoder. When the vehicle is traveling straight and the steering wheel is near the center position, if the difference between the two exceeds 2° and persists for a certain period, it indicates "there may be a gap in the mechanical connection or plastic deformation of the torsion bar."

[0134] The decision and output module (which may be located in IC200) summarizes all information. Finally, the output via the SENT (Single Edge Nibble Transmission) protocol includes not only torque and angle data, but also an 8-bit status word.

[0135] Therefore, the self-diagnostic system of this application embodiment achieves predictive maintenance and accurate fault tracing of torque angle sensors through multi-dimensional and in-depth diagnosis.

[0136] A self-diagnostic system for a strain gauge torque angle sensor, as proposed in this application, acquires the original sine and cosine signals of the torque angle sensor through a signal acquisition module. A core self-diagnostic module performs multi-dimensional fault diagnosis. The dynamic trajectory diagnosis unit detects nonlinear distortion and jamming by analyzing the deviation of the signal trajectory in the coordinate system from a standard circle. The health assessment unit calculates a health index and predicts the remaining lifespan based on historical data. The fault tracing unit distinguishes between sensor-specific faults and external mechanical faults through active testing and cross-validation. Finally, the decision and output module outputs verified sensor data and a diagnostic report containing a comprehensive health status, thereby achieving intelligent monitoring and early warning of the sensor's health status throughout its entire lifecycle. This system addresses the problems of limited diagnostic dimensions, lack of predictive maintenance capabilities, and insufficient fault tracing capabilities in related technologies for torque angle sensors, improving the diagnostic coverage and accuracy of torque angle sensors and ensuring vehicle safety performance.

[0137] Next, referring to the accompanying drawings, a self-diagnostic method for a strain gauge torque angle sensor according to an embodiment of this application is described.

[0138] Figure 5 This is a flowchart illustrating a self-diagnostic method for a strain gauge torque angle sensor according to an embodiment of this application. Figure 6 This is a flowchart of the dynamic trajectory diagnosis, health assessment and fault tracing method according to an embodiment of this application.

[0139] like Figure 5 and Figure 6 As shown, the self-diagnostic method for a strain gauge torque angle sensor includes the following steps:

[0140] In step S501, the original sine signal value and the original cosine signal value of the torque angle sensor are obtained;

[0141] In step S502, dynamic trajectory diagnosis, health assessment and fault tracing are performed on the original sine signal value and the original cosine signal value;

[0142] In step S503, a diagnostic report based on the diagnostically verified sensor data and health status words is output;

[0143] Step S502 involves performing dynamic trajectory diagnosis, health assessment, and fault tracing on the original sine and cosine signal values, specifically including:

[0144] In step S5021, in a Cartesian coordinate system composed of the original sine signal value and the original cosine signal value, the fitting degree of the trajectory graphic formed by the continuously collected data points is analyzed, and the roundness error or radius variance between the trajectory graphic and the preset standard circle is calculated to determine whether the torque angle sensor has nonlinear distortion or jamming at arbitrary angle position.

[0145] In step S5022, based on the historical diagnostic data generated by the dynamic trajectory diagnosis and / or signal quality assessment process, the current health index of the torque angle sensor is calculated through the performance degradation model, and the remaining service life of the torque angle sensor is estimated according to the degradation trend of the current health index.

[0146] In step S5023, a combination of active testing and cross-validation is used to distinguish between faults in the torque angle sensor itself and faults in the external mechanical connection.

[0147] It should be noted that the foregoing explanation of an embodiment of a self-diagnostic system for a strain gauge torque angle sensor also applies to a self-diagnostic method for a strain gauge torque angle sensor in this embodiment, and will not be repeated here.

[0148] A self-diagnostic method for a strain gauge torque angle sensor, as proposed in this application, acquires the original sine and cosine signals of the torque angle sensor; detects nonlinear distortion and jamming by analyzing the deviation of the signal trajectory in the coordinate system from a standard circle; calculates a health index and predicts the remaining lifespan based on historical data; and distinguishes between sensor-specific faults and external mechanical faults through active testing and cross-validation. Finally, it outputs validated sensor data and a diagnostic report containing a comprehensive health status, thereby achieving intelligent monitoring and early warning of the sensor's health status throughout its entire lifecycle. This solves the problems of limited diagnostic dimensions, lack of predictive maintenance capabilities, and insufficient fault tracing capabilities in related technologies for torque angle sensors, improving the diagnostic coverage and accuracy of torque angle sensors and ensuring vehicle safety performance.

[0149] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0150] The memory 701, the processor 702, and the computer program stored on the memory 701 and capable of running on the processor 702.

[0151] When the processor 702 executes the program, it implements a self-diagnostic method for a strain gauge torque angle sensor provided in the above embodiments.

[0152] Furthermore, electronic devices also include:

[0153] Communication interface 703 is used for communication between memory 701 and processor 702.

[0154] The memory 701 is used to store computer programs that can run on the processor 702.

[0155] The memory 701 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0156] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0157] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0158] The processor 702 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0159] This application also provides a computer program product on which a computer program is stored, which, when executed by a processor, implements the above-described self-diagnostic method for a strain gauge torque angle sensor.

[0160] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0161] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0162] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0163] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0164] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0165] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A self-diagnostic system for a strain gauge torque angle sensor, characterized in that, include: The module consists of a signal acquisition module, a core self-diagnosis module, and a decision and output module. The signal acquisition module is used to acquire the original sine signal value and the original cosine signal value of the torque angle sensor; The core self-diagnosis module is used to perform multi-dimensional fault diagnosis on the original sine signal value and the original cosine signal value. The decision and output module is used to output the sensor data and health status words after diagnostic verification; The core self-diagnosis module includes: The dynamic trajectory diagnosis unit performs a fitting degree analysis on the trajectory graphic formed by continuously collected data points in a Cartesian coordinate system composed of the original sine signal value and the original cosine signal value. By calculating the roundness error or radius variance between the trajectory graphic and the preset standard circle, it determines whether the torque angle sensor has nonlinear distortion or jamming at arbitrary angle positions. The health assessment unit calculates the current health index of the torque angle sensor based on the historical diagnostic data of the dynamic trajectory diagnosis unit and / or signal quality assessment unit through a performance degradation model, and estimates the remaining service life of the torque angle sensor based on the degradation trend of the current health index. The fault tracing unit is used to distinguish between faults in the torque angle sensor itself and faults in external mechanical connections through a combination of active testing and cross-validation. The fault tracing unit includes: The fault injection subunit is used to apply a preset test excitation signal to the signal link of the torque angle sensor when the torque angle sensor is powered on and initialized or under a preset safe operating condition, and to verify the integrity of the internal functional link of the torque angle sensor based on the deviation between the output response of the torque angle sensor and the expected response. The cross-validation subunit is used to compare the torque signal and / or angle signal output by the torque angle sensor with the signal of at least one auxiliary sensor in the vehicle system that has a physical or logical relationship in real time, and to locate the fault source by judging the physical and logical rationality between the signals. The cross-validation subunit is further configured to: Under the condition that the vehicle is driving in a straight line and making minor adjustments to the steering wheel, a mathematical relationship model is established between the torque value output by the torque angle sensor and the motor resistance torque measured by the motor encoder. Real-time monitoring is conducted to determine whether the actual deviation between the torque value and the motor resistance torque exceeds the reasonable range determined based on the mathematical relationship model. If the actual deviation continues to exceed the reasonable range, it is determined that the torque angle sensor has an external mechanical connection fault. The preset test excitation signal is either a millisecond-level small amplitude disturbance to the power supply voltage of the excitation coil of the torque angle sensor, or a transient capacitive load switch to the sensing signal acquisition circuit.

2. The self-diagnostic system for a strain gauge torque angle sensor according to claim 1, characterized in that, The dynamic trajectory diagnosis unit is specifically used for: The least squares method is used to fit the sine and cosine signal data points of multiple consecutive sampling periods into a circle; Calculate the distance from each data point to the center of the fitted circle, and obtain the standard deviation or variance of the distances from all data points to the center of the fitted circle as the radius variance; The radius variance is compared with a preset variance threshold. If the radius variance continues to exceed the preset variance threshold, the trajectory pattern is determined to be abnormal.

3. The self-diagnostic system for a strain gauge torque angle sensor according to claim 1, characterized in that, The formula for calculating the current health index in the health assessment unit is as follows: HI(t) = K / V(t); Wherein, HI(t) is the health index of the torque angle sensor at the current time t, K is the normalization coefficient calibrated based on the initial health state of the torque angle sensor, and V(t) is the moving average of the radius variance calculated by the dynamic trajectory diagnosis unit at the current time t. The health assessment unit is further configured to: record the sequence of health index changes over time, fit the health index decay curve using linear regression or exponential smoothing, and determine the remaining service life as the time corresponding to the current health index decaying to a preset failure threshold.

4. The self-diagnostic system for a strain gauge torque angle sensor according to claim 1, characterized in that, The core self-diagnostic module also includes: A signal quality assessment unit is used to verify whether the sum of the squares of the original sine signal value and the original cosine signal value remains near a constant theoretical value; If the deviation between the sum of squares and the theoretical value exceeds the preset tolerance range, it is determined that there is an abnormality in the signal generator or the calculation circuit.

5. A self-diagnostic method for a strain gauge torque angle sensor, characterized in that, Includes the following steps: Obtain the raw sine and raw cosine signal values ​​from the torque angle sensor; Dynamic trajectory diagnosis, health assessment, and fault tracing are performed on the original sine signal value and the original cosine signal value. Output a diagnostic report based on diagnostically validated sensor data and health status words; The dynamic trajectory diagnosis, health assessment, and fault tracing of the original sine signal value and the original cosine signal value specifically include: In a Cartesian coordinate system composed of the original sine signal value and the original cosine signal value, the fitting degree of the trajectory graph formed by the continuously collected data points is analyzed, and the roundness error or radius variance of the trajectory graph with the preset standard circle is calculated to determine whether the torque angle sensor has nonlinear distortion or jamming at arbitrary angle position. Based on historical diagnostic data generated by the dynamic trajectory diagnosis and / or signal quality assessment process, the current health index of the torque angle sensor is calculated through a performance degradation model, and the remaining service life of the torque angle sensor is estimated based on the degradation trend of the current health index. By combining active testing and cross-validation, we can distinguish between faults in the torque angle sensor itself and faults in external mechanical connections. The active test includes: when the torque angle sensor is powered on and initialized or under a preset safe operating condition, applying a preset test excitation signal to the signal link of the torque angle sensor, and verifying the integrity of the internal functional link of the torque angle sensor based on the deviation between the output response of the torque angle sensor and the expected response. The cross-validation includes: comparing the torque signal and / or angle signal output by the torque angle sensor with the signal of at least one auxiliary sensor in the vehicle system that has a physical or logical connection in real time, and locating the fault source by judging the physical and logical rationality between the signals. The cross-validation further includes: under the condition that the vehicle is driving in a straight line and finely adjusting the steering wheel, establishing a mathematical relationship model between the torque value output by the torque angle sensor and the motor resistance torque measured by the motor encoder; monitoring in real time whether the actual deviation between the torque value and the motor resistance torque exceeds the reasonable range determined based on the mathematical relationship model; if the actual deviation continues to exceed the reasonable range, it is determined that the torque angle sensor has an external mechanical connection fault. The preset test excitation signal is either a millisecond-level small amplitude disturbance to the power supply voltage of the excitation coil of the torque angle sensor, or a transient capacitive load switch to the sensing signal acquisition circuit.

6. An electronic device, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement a self-diagnostic method for a strain gauge torque angle sensor as described in claim 5.

7. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement a self-diagnostic method for a strain gauge torque angle sensor as described in claim 5.

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