Fault detection method and vehicle

Abstract

The invention relates to the technical field of vehicle detection, and relates to a fault detection method and a vehicle. The fault detection method is suitable for detecting faults of a stabilizer bar in a vehicle, and comprises the steps that the fault of the stabilizer bar in the vehicle is detected on the basis of a stabilizer bar median displacement prediction value, an oil cavity pressure prediction value and an oil cavity pressure change rate prediction value at the current moment and in combination with stabilizer bar median displacement and oil cavity pressure obtained through actual measurement at the current moment; estimating the actual median displacement of the stabilizer bar, the actual oil cavity pressure and the actual oil cavity pressure change rate of the stabilizer bar at the current moment; and whether a fault occurs or not is judged based on the estimated actual middle displacement of the stabilizer bar, the actual oil cavity pressure and the actual oil cavity pressure change rate. According to the fault detection method, the actual value of the parameter at the current moment can be estimated through the measured value and the predicted value of the parameter at the current moment, and fault diagnosis is performed by using the actual value, so that the accuracy of fault diagnosis of the stabilizer bar is improved, and the fault misjudgment probability is reduced.

Description

Technical Field

[0001] This application relates to the field of vehicle inspection technology, and in particular to a fault detection method and a vehicle. Background Technology

[0002] The stabilizer bar is a key component of a vehicle's suspension system. Hydraulic stabilizer bars utilize a built-in piston hydraulic system filled with hydraulic oil. By adjusting the hydraulic oil pressure, they balance the left and right suspensions, limiting body roll and ensuring vehicle handling stability. However, with prolonged use, the hydraulic oil is prone to leakage due to seal aging, pipeline damage, or other issues that can break the seals and affect stabilizer bar performance.

[0003] In most related technologies, hydraulic oil pressure is measured by sensors, and the measured pressure is compared with a threshold to determine oil leakage.

[0004] However, the actual measured hydraulic oil pressure may have errors due to interference from environmental noise, temperature changes, etc., and directly using it for oil leak diagnosis is prone to misjudgment and has low detection accuracy. Summary of the Invention

[0005] In view of this, this application aims to propose a fault detection method to improve the accuracy of stabilizer bar fault detection.

[0006] To achieve the above objectives, the technical solution of this application is implemented as follows: A fault detection method, applied to a vehicle, the fault detection method comprising: The stabilizer bar in the vehicle is obtained as follows: the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under a preset historical state, as well as the stabilizer bar mid-position displacement and oil chamber pressure actually measured at the current moment. Based on the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical conditions, calculate the predicted values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment. Based on the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment, as well as the calculated predicted values ​​of the mid-position displacement of the stabilizer bar, the predicted value of the oil chamber pressure, and the predicted value of the rate of change of the oil chamber pressure at the current moment, the actual mid-position displacement of the stabilizer bar, the actual oil chamber pressure, and the actual rate of change of the oil chamber pressure at the current moment are estimated using a preset estimation algorithm. Based on the estimated actual mid-position displacement of the stabilizer bar, the actual oil chamber pressure, and the actual oil chamber pressure change rate at the current moment, it is determined whether the stabilizer bar is currently malfunctioning.

[0007] Furthermore, the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical state are the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate estimated by the preset estimation algorithm at the previous moment. The calculation of the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment based on the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical conditions includes: Based on the estimated actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate obtained at the previous moment, the state vector matrix corresponding to the preset historical state is determined. Based on the preset state prediction model of the stabilizer bar, a state transition matrix is ​​established to predict the state vector matrix corresponding to the stabilizer bar at the next moment. Calculate the product of the state transition matrix and the state vector matrix corresponding to the preset historical state to obtain the predicted state vector matrix of the stabilizer at the current moment; Based on the state vector matrix of the stabilizer bar predicted at the current moment, the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are determined.

[0008] Furthermore, the step of estimating the actual mid-displacement of the stabilizer bar, the actual oil chamber pressure, and the actual rate of change of the oil chamber pressure at the current moment using a preset estimation algorithm includes: Based on the stabilizer bar mid-position displacement and oil chamber pressure obtained from the actual measurement at the current moment, the calculated predicted values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are corrected to obtain the corrected predicted values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate. The corrected predicted values ​​of the stabilizer bar mid-position displacement, the oil chamber pressure, and the oil chamber pressure change rate are used as the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the current moment, respectively.

[0009] Furthermore, the correction of the calculated predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment includes: Obtain the prediction uncertainty of the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate calculated using the preset state prediction model at the current moment; Obtain the measurement uncertainty of the stabilizer bar mid-position displacement and oil chamber pressure at the current moment; Based on the prediction uncertainty and the measurement uncertainty, the correction weights for the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are calculated. Based on the correction weight, the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment, and the calculated predicted values ​​of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the oil chamber pressure change rate at the current moment, the correction amount for correcting the predicted values ​​of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the oil chamber pressure change rate at the current moment is calculated. Based on the aforementioned correction amount, the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are corrected to obtain the corrected predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate.

[0010] Furthermore, the step of obtaining the prediction uncertainty of the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate calculated using the preset state prediction model at the current moment includes: Obtain the uncertainties corresponding to the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure under the preset historical state, and establish a preset error covariance matrix composed of the uncertainties corresponding to the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure under the preset historical state. Obtain the model noise covariance matrix, which characterizes the uncertainty of the preset state prediction model in predicting the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure. Based on the preset error covariance matrix and the model noise covariance matrix, the prediction error covariance matrix is ​​calculated. Based on the prediction error covariance matrix, determine the prediction uncertainties of the current moment's predicted values ​​for the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate.

[0011] Furthermore, the calculation process of the prediction error covariance matrix includes: Based on the preset error covariance matrix, calculate the historical error covariance matrix composed of the errors in the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate transmitted from the preset historical state to the current state; The prediction error covariance matrix is ​​obtained by superimposing the historical error covariance matrix with the model noise covariance matrix.

[0012] Furthermore, the step of calculating correction weights based on the prediction uncertainty and the measurement uncertainty to correct the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment includes: Based on the prediction uncertainty and the measurement uncertainty, calculate the confidence weights corresponding to the actual measured mid-displacement of the stabilizer bar and the oil chamber pressure at the current moment; Based on the confidence weights corresponding to the stabilizer bar mid-displacement and oil chamber pressure measured at the current moment, and the prediction uncertainty, correction weights are calculated to correct the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment.

[0013] Furthermore, the step of calculating the confidence weights corresponding to the actual measured mid-displacement of the stabilizer bar and the oil chamber pressure at the current moment based on the prediction uncertainty and the measurement uncertainty includes: Obtain the observation matrix used to transform the state vector matrix of the stabilizer bar into the measurement vector matrix; Determine the prediction error covariance matrix composed of the prediction uncertainty corresponding to the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure; Determine the measurement noise covariance matrix composed of the measurement uncertainties corresponding to the mid-position displacement of the stabilizer bar and the oil chamber pressure; The confidence weight is calculated based on the prediction error covariance matrix, the measurement noise covariance matrix, and the observation matrix.

[0014] Furthermore, the calculation process for the correction amount includes: The predicted state vector matrix is ​​determined based on the current predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate. Based on the predicted state vector matrix, the observation matrix, and the measurement vector matrix, the deviations between the actual measured values ​​and the predicted values ​​corresponding to the mid-position displacement of the stabilizer bar and the pressure in the oil chamber are calculated, and a residual matrix composed of the deviations is obtained. The correction amount is calculated based on the residual matrix and the correction weights.

[0015] Furthermore, the step of determining whether the stabilizer bar is currently malfunctioning based on the estimated actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate includes: Multiple sets of calibration data are obtained by performing a preset calibration test on the stabilizer bar. Each set of calibration data includes operating parameters and the fault state of the stabilizer bar under the operating parameters. Based on the calibration data of each group, the oil chamber pressure judgment strategy, the oil chamber pressure change rate judgment strategy, and the stabilizer bar mid-position displacement judgment strategy are determined, and decision trees corresponding to oil chamber pressure, pressure change rate, and stabilizer bar mid-position displacement are constructed respectively. Based on the estimated actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate, fault decisions are made using the decision tree corresponding to the oil chamber pressure, the decision tree corresponding to the pressure change rate, and the decision tree corresponding to the stabilizer bar mid-displacement, respectively, to obtain the fault decision results corresponding to the oil chamber pressure, the fault decision results corresponding to the oil chamber pressure change rate, and the fault decision results corresponding to the stabilizer bar mid-displacement. If the fault decision result corresponding to the oil chamber pressure, the fault decision result corresponding to the oil chamber pressure change rate, and the fault decision result corresponding to the stabilizer bar mid-position displacement meet the preset conditions, it is determined that the stabilizer bar has failed. The preset conditions include: there are at least two fault decision results that determine that the stabilizer bar has failed.

[0016] Compared with related technologies, this application has the following advantages: The fault detection method described in this application estimates the actual stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment using a pre-defined estimation algorithm, based on predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment, and the actual measured values ​​of the stabilizer bar mid-displacement and oil chamber pressure. The method then determines whether a fault has occurred based on these actual values, rather than solely relying on measured values ​​for fault diagnosis. Since the actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate more closely reflect the true state of the stabilizer bar than measured values, this method improves the accuracy of stabilizer bar fault detection and reduces the false positive rate.

[0017] Meanwhile, this application also calculates the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment based on the actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the previous moment. Compared to directly using the original measurement data from the previous moment to calculate the predicted values ​​at the current moment, this method allows the predicted data to more closely approximate the true state of the stabilizer bar, thereby improving the accuracy of oil leak detection.

[0018] In addition, this application also corrects the calculated predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment, which helps to prevent misjudgment of faults due to measurement data deviation or prediction data deviation, thereby improving the accuracy of stabilizer bar fault judgment.

[0019] In addition, this application allocates confidence levels for the predicted and measured values ​​of stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate based on prediction and measurement uncertainties, and determines correction weights for the predicted values ​​of stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate accordingly. This ensures that the corrected actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate better reflect the true state of the stabilizer bar, thereby reducing the probability of misjudging stabilizer bar oil leakage faults.

[0020] Furthermore, this application also calculates the prediction error covariance matrix by using a preset error covariance matrix and a model noise covariance matrix to calculate the prediction uncertainty at the current moment. In this way, the prediction uncertainties of the current moment's stabilizer mid-displacement prediction, oil chamber pressure prediction, and oil chamber pressure change rate prediction obtained through quantified prediction can provide a reliable basis for subsequent calculation of correction weights, thereby improving the accuracy of subsequent oil leak fault judgment.

[0021] Furthermore, this application establishes three decision trees, and only determines that the stabilizer bar has failed when at least two of the three decision trees result in a stabilizer bar failure. This helps reduce the probability of misjudging a failure due to accidental fluctuations in one of the parameters.

[0022] Another object of this application is to provide a vehicle in which a stabilizer bar is provided.

[0023] The stabilizer bar includes a piston-type hydraulic device, and the vehicle controller includes a memory and a processor; The memory stores a computer program, which, when run by the processor, performs the aforementioned fault detection method.

[0024] The vehicle described in this application can use the measured oil chamber pressure and stabilizer bar mid-position displacement, as well as the predicted values ​​of oil chamber pressure, oil chamber pressure change rate, and stabilizer bar mid-position displacement, to estimate the actual oil chamber pressure, actual oil chamber pressure change rate, and actual stabilizer bar mid-position displacement at the current moment. This allows the estimated actual oil chamber pressure, actual oil chamber pressure change rate, and actual stabilizer bar mid-position displacement to better reflect the true state of the stabilizer bar, thereby improving the accuracy of stabilizer bar fault detection and reducing the false alarm rate of stabilizer bar faults. Attached Figure Description

[0025] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the stabilizer bar structure described in the embodiments of this application; Figure 2 This is a schematic flowchart of the fault detection method described in the embodiments of this application; Figure 3 This is a schematic diagram of the process for predicting oil chamber pressure, oil chamber pressure change rate, and stabilizer rod mid-position displacement in the fault detection method described in the embodiments of this application. Figure 4 This is a schematic diagram of the process for correcting the predicted parameters in the fault detection method described in the embodiments of this application; Figure 5 This is a schematic diagram of the process for calculating prediction uncertainty in the fault detection method described in the embodiments of this application; Figure 6 This is a schematic diagram of the process for calculating the correction weight in the fault detection method described in the embodiments of this application; Figure 7 This is a schematic diagram of the process for determining whether an oil leak fault has occurred in the fault detection method described in the embodiments of this application; Figure 8 This is an example diagram of the decision tree described in the embodiments of this application; Figure 9 This is a schematic diagram of the configuration of the vehicle controller described in the embodiments of this application.

[0026] Explanation of reference numerals in the attached figures: 1. Piston-type hydraulic device; 11. Piston-type hydraulic cylinder; 12. Solenoid valve assembly; 13. Pressure sensor assembly; 14. Accumulator; 15. Hydraulic oil pipeline; 16. Stabilizer bar center displacement sensor; 2. First stabilizing half-bar; 3. Second stabilizing half-bar; 910. Processor; 920. Memory. Detailed Implementation

[0027] To make the technical solution and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0029] Furthermore, it should be noted that in the description of this application, if terms such as "upper," "lower," "inner," or "outer" appear, indicating orientation or positional relationship, these are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. In addition, if terms such as "first" or "second" appear, they are also used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0030] Furthermore, in the description of this application, unless otherwise expressly defined, the terms "installation," "connection," "joining," and "connector" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application in light of the specific circumstances.

[0031] In this application, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which 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. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0032] The present application will now be described in detail through exemplary embodiments. However, it should be understood that, without further description, elements, structures, and features in one embodiment may be advantageously incorporated into other embodiments.

[0033] An embodiment of the first aspect of this application provides a fault detection method for a vehicle, used to detect stabilizer bar oil leakage. This fault detection method calculates the predicted values ​​of the stabilizer bar's oil chamber pressure, oil chamber pressure change rate, and stabilizer bar mid-position displacement at the current moment. Combining these with the measured oil chamber pressure and stabilizer bar mid-position displacement at the current moment, it estimates the actual oil chamber pressure, actual oil chamber pressure change rate, and actual stabilizer bar mid-position displacement at the current moment. Based on the estimated actual oil chamber pressure, actual oil chamber pressure change rate, and actual stabilizer bar mid-position displacement, it detects whether the stabilizer bar has malfunctioned. This method helps reduce errors caused by using actual measured data when judging stabilizer bar faults, thus improving the accuracy of stabilizer bar fault detection.

[0034] In related technologies, the stabilizer bar (also known as an anti-roll bar) in a vehicle is one of the key components of the vehicle's suspension system, connecting to the left and right suspensions. When the vehicle is turning or driving on uneven roads, the stabilizer bar can balance the left and right suspensions, limiting the degree of body roll, thereby improving the vehicle's handling stability and ride comfort.

[0035] Figure 1 The structure of the stabilizer bar is shown, with reference to... Figure 1 , Figure 1 The stabilizer bar shown includes a piston-type hydraulic device 1, a first stabilizer bar 2, and a second stabilizer bar 3. The first stabilizer bar 2 and the second stabilizer bar 3 are connected to the left and right suspensions of the vehicle, respectively. For example, one end of the first stabilizer bar 2 is connected to the left suspension, and the other end is connected to the piston-type hydraulic device 1; one end of the second stabilizer bar 3 is connected to the piston-type hydraulic device 1, and the other end is connected to the right suspension.

[0036] The piston-type hydraulic device 1 includes a piston-type hydraulic cylinder 11, a solenoid valve group 12, a pressure sensor group 13, an accumulator 14, and a hydraulic oil pipeline 15. The piston-type hydraulic cylinder 11 is equipped with a piston, which divides the piston-type hydraulic cylinder 11 into two oil chambers filled with hydraulic oil (for example, a left chamber and a right chamber). One end of the piston is fixedly connected to the second stabilizing half rod 3, and the other end of the piston can contact the first stabilizing half rod 2.

[0037] Hydraulic oil lines 15 are connected to the two oil chambers of the piston-type hydraulic cylinder 11. The solenoid valve assembly 12 is installed on the hydraulic oil lines 15 to control the opening or closing of the hydraulic oil lines 15. The accumulator 14 is used to replenish hydraulic oil into the piston-type hydraulic cylinder 11.

[0038] The pressure sensor group 13 is used to detect the oil pressure in the chamber of the piston hydraulic cylinder 11. Specifically, the pressure sensor group 13 includes a first pressure sensor and a second pressure sensor. The first pressure sensor is used to detect the oil pressure in the left chamber, and the second pressure sensor is used to detect the oil pressure in the right chamber.

[0039] The operation of the stabilizer bar is controlled by the vehicle's ECU (Electronic Control Unit). The ECU can connect or lock the hydraulic oil line 15 between the two oil chambers of the piston hydraulic cylinder 11 by opening and closing the solenoid valve, thereby connecting or disconnecting the stabilizer bar.

[0040] When the vehicle is in a balanced state (e.g., without roll), the stabilizer bar piston is located at a preset center position within the piston-type hydraulic cylinder 11. This preset center position is the position of the piston when the vehicle is in a balanced state (without roll), for example, the center of the piston-type hydraulic cylinder 11. During vehicle operation, due to forces acting on the stabilizer bar, the stabilizer bar piston will deviate from this preset center position. The distance between the piston and the preset center position is the stabilizer bar mid-position displacement.

[0041] The piston-type hydraulic device 1 may also include a stabilizer bar mid-position displacement sensor 16, which is mounted on the piston and used to measure the distance of the piston relative to the preset center position, that is, to measure the mid-position displacement of the stabilizer bar.

[0042] However, in actual use, the hydraulic oil in the piston hydraulic cylinder 11 of this type of stabilizer bar equipped with a piston hydraulic device 1 may leak due to problems such as aging of the seals, damage to the hydraulic oil line 15, or valve leakage. The hydraulic oil leakage will affect the working performance of the stabilizer bar and may threaten driving safety.

[0043] Therefore, it is necessary to check whether the stabilizer bar is malfunctioning, and more specifically, to check whether the stabilizer bar is leaking oil, so as to detect the fault in time and repair it.

[0044] In related technologies, most oil leak checks on stabilizer bars are performed by visual inspection. However, this method is not real-time and cannot detect oil leaks in a timely manner.

[0045] To address the issue of poor real-time performance, related technologies also utilize the oil chamber pressure measured by a pressure sensor installed in the stabilizer bar to check for oil leaks. Specifically, based on the actual pressure measured by the pressure sensor, this measured pressure is compared with a preset oil leak threshold to diagnose whether an oil leak has occurred.

[0046] However, pressure sensors are prone to interference from environmental noise, temperature changes, and other factors, which can cause errors in the measured oil chamber pressure. This can lead to misjudgments when using the oil chamber pressure measured by the pressure sensor directly for oil leak diagnosis.

[0047] In view of this, in order to overcome the shortcomings of related technologies, in the fault detection method of this embodiment, which is applied to vehicles and suitable for detecting faults in stabilizer bars in vehicles, the following is combined with... Figure 1 and Figure 2 In terms of overall design, it includes the following steps S210-S240.

[0048] It is worth noting that the fault detection of the vehicle stabilizer bar in this embodiment can be applied not only to the detection of oil leakage faults of the stabilizer bar, but also to the detection of other faults that can be detected by oil chamber pressure, oil chamber pressure change rate, and stabilizer bar mid-position displacement.

[0049] Step S210: Obtain the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate in the vehicle under a preset historical state, as well as the stabilizer bar mid-position displacement and oil chamber pressure actually measured at the current moment.

[0050] Among them, such as Figure 1 As shown, the stabilizer bar includes a piston-type hydraulic device 1, and the mid-position displacement of the stabilizer bar is the displacement of the piston in the piston-type hydraulic device 1 relative to a preset center position. Specifically, the preset center position is the position of the piston when the vehicle is in a balanced state such as without roll. For example, when the vehicle is stationary or traveling in a straight line on a flat road (without torsion of the stabilizer bar), the position of the piston is the preset center position.

[0051] Specifically, the mid-position displacement of the stabilizer bar can be determined by... Figure 1 The displacement of the stabilizer bar in the stabilizer bar shown is measured by the stabilizer bar mid-position sensor 16.

[0052] The specific pressure in the oil chamber can be measured by a pressure sensor installed inside the stabilizer bar. The rate of change of the oil chamber pressure is the degree of change of the oil chamber pressure per unit time.

[0053] Figure 1 The stabilizer bar shown contains two oil chambers, namely the left chamber and the right chamber. The oil chamber pressure and the rate of change of the oil chamber pressure can be the oil chamber pressure and the rate of change of the oil chamber pressure corresponding to either one of the oil chambers, or the average of the oil chamber pressures of the two oil chambers can be used as the oil chamber pressure and the rate of change of the pressure corresponding to the average can be used as the rate of change of the oil chamber pressure. No limitation is made here.

[0054] It is worth noting that when the oil chamber pressure and oil chamber pressure change rate are the oil chamber pressure and oil chamber pressure change rate corresponding to one of the oil chambers, not only can the oil chamber pressure and oil chamber pressure change rate corresponding to that oil chamber, as well as the mid-position displacement of the stabilizer bar, be used to diagnose oil leakage of the stabilizer bar, but the same fault detection method can also be used to diagnose oil leakage based on the oil chamber pressure and oil chamber pressure change rate of another oil chamber, as well as the mid-position displacement of the stabilizer bar. No limitation is made here.

[0055] In step S210, the preset historical state can specifically be the state corresponding to the previous moment. For example, in step S210, at the current moment, the oil chamber pressure, oil chamber pressure change rate, and stabilizer rod mid-position displacement of the previous moment are obtained.

[0056] Step S220: Based on the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under preset historical conditions, calculate the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment.

[0057] Specifically, in step S220, based on the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical state, a preset model can be used to make predictions, thereby obtaining the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment.

[0058] Step S230: Based on the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment, as well as the calculated predicted values ​​of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the oil chamber pressure change rate at the current moment, the actual mid-position displacement of the stabilizer bar, the actual oil chamber pressure, and the actual oil chamber pressure change rate at the current moment are estimated using a preset estimation algorithm.

[0059] Specifically, the stabilizer bar mid-position displacement and oil chamber pressure measured at the current moment are data obtained by stabilizer bar mid-position displacement sensor 16 and pressure sensor. However, the sensors may be affected by the external environment and have certain noise errors.

[0060] The predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment also have certain errors.

[0061] Therefore, in step S230, a preset estimation algorithm is used to fuse the predicted data with errors (i.e., the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate) with the measurement data with noise errors obtained by the sensor (i.e., the actual measured stabilizer bar mid-position displacement and oil chamber pressure) to estimate the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the current moment.

[0062] The preset estimation algorithm can specifically adopt the Kalman state estimation algorithm (also known as Kalman filtering). The Kalman state estimation algorithm is an optimal state estimation algorithm for dynamic systems. It can find an estimate that is closer to the current real state through mathematical iteration based on the predicted data and the actual measurement data of the sensor. That is, it can estimate the actual mid-displacement of the stabilizer bar, the actual oil chamber pressure, and the actual oil chamber pressure change rate at the current moment, which is more accurate than the data obtained by direct measurement and the predicted data.

[0063] Step S240: Based on the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the current moment, determine whether the stabilizer bar is currently malfunctioning.

[0064] Specifically, when a stabilizer bar malfunctions, the stabilizer bar's center displacement, oil chamber pressure, and oil chamber pressure change rate will all exhibit abnormal behavior. This is especially true when the stabilizer bar experiences an oil leak.

[0065] When the stabilizer bar leaks oil, the stabilizer bar center displacement may be excessive (unlike the normal stabilizer bar center displacement during use). For example, when the vehicle is in a balanced state, the stabilizer bar center displacement should be 0, but the actual stabilizer bar center displacement is not 0.

[0066] For example, when the stabilizer bar leaks oil, the oil chamber pressure will decrease (which does not match the normal performance of the stabilizer bar's oil chamber pressure during use), and the rate of change of oil chamber pressure will show a decrease in oil chamber pressure and a relatively fast decrease (which does not match the normal performance of the stabilizer bar's oil chamber pressure change rate during use).

[0067] Therefore, based on the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate, it is possible to analyze whether the stabilizer bar is currently faulty, thereby completing fault detection. When a stabilizer bar fault is detected, especially an oil leak, it can be repaired in a timely manner, reducing the threat to driving safety.

[0068] Through steps S210-S240, the fault detection method of this embodiment determines whether a fault has occurred based on the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate, rather than solely relying on measured values ​​for fault diagnosis. Since the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate more closely reflect the true state of the stabilizer bar than measured values, fault detection based on these parameters can improve the accuracy of stabilizer bar fault detection and reduce the false positive rate.

[0069] Continue to combine Figure 1 and Figure 2 and combined Figure 3 As shown, in some exemplary embodiments, in step S210 above, the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical state are the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate estimated by the preset estimation algorithm at the previous moment.

[0070] For example, if the previous time is t1 and the current time is t2, then the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical state are as follows: At time t1, based on the predicted values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at time t1, and the stabilizer bar mid-position displacement and oil chamber pressure measured at time t1, the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at time t1 are estimated using a preset estimation algorithm.

[0071] Among them, reference Figure 3 In step S220, the predicted values ​​of the stabilizer rod mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are calculated based on the stabilizer rod mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical conditions. Specifically, this may include the following steps S221-S224.

[0072] Step S221: Based on the estimated actual stabilizer rod mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate obtained at the previous moment, determine the state vector matrix corresponding to the preset historical state.

[0073] The state vector matrix is ​​a vector matrix composed of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure.

[0074] Specifically, in this embodiment, the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure are each taken as a state parameter, and a state vector matrix is ​​defined. for: .

[0075] in, Let p be the state vector matrix corresponding to the current moment, and p be the oil chamber pressure. The rate of change of oil chamber pressure refers to the change in pressure per unit time, defined as follows: , where Δt=t (i+1) -t i This represents the time interval between two sampling points. z is the mid-position displacement of the stabilizer bar.

[0076] In step S221, the state vector matrix corresponding to the preset historical state can be obtained by substituting the estimated actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate obtained at the previous moment. The state vector matrix corresponding to the preset historical state can be obtained from the state vector matrix. For example, if Δt is 1 second, the mid-range displacement of the stabilizer bar corresponding to the preset historical state is 0, the oil chamber pressure is 2, and the oil chamber pressure change rate is 0, then the state vector matrix for this preset historical state is: .

[0077] Step S222: Based on the preset state prediction model of the stabilizer bar, establish a state transition matrix for predicting the state vector matrix corresponding to the stabilizer bar at the next moment.

[0078] Specifically, the state transition matrix A is used to describe how the state of the stabilizer bar evolves over time, that is, to predict the oil chamber pressure, the rate of change of oil chamber pressure, and the mid-position displacement of the stabilizer bar at the next moment.

[0079] For the stabilizer bar, the changes in its oil chamber pressure, rate of change of oil chamber pressure, and mid-position displacement conform to the linear dynamic model, which is also consistent with the preset state prediction model: the oil chamber pressure of the stabilizer bar at the next moment is equal to the oil chamber pressure at the current moment plus (the rate of change of oil chamber pressure multiplied by the time interval). The sum; assuming no external control input and ignoring the influence of other factors on the stabilizer bar, the rate of change of the stabilizer bar's oil chamber pressure remains constant, and the stabilizer bar's mid-position displacement also remains constant.

[0080] Based on this preset state prediction model, the established state transition matrix A can be: .

[0081] Step S223: Calculate the product of the state transition matrix and the state vector matrix corresponding to the preset historical state to obtain the predicted state vector matrix of the stabilizer at the current moment.

[0082] Step S224: Based on the state vector matrix of the stabilizer bar obtained at the current moment, determine the predicted value of the stabilizer bar mid-position displacement, the predicted value of the oil chamber pressure, and the predicted value of the oil chamber pressure change rate at the current moment.

[0083] Specifically, in step S223, the state vector matrix of the stabilizing bar at the current moment is: .

[0084] For example, the state vector matrix corresponding to the preset historical states. Then the predicted state vector matrix of the stabilizer at the current moment, that is, the predicted state vector matrix of the stabilizer at the current moment, is: .

[0085] The state vector matrix of the stabilizer bar predicted at the current moment is composed of the predicted value of the stabilizer bar mid-position displacement, the predicted value of the oil gun pressure, and the predicted value of the oil gun pressure change rate.

[0086] That is, in this example, the predicted value of the stabilizer bar's oil chamber pressure at the current moment is 2, the predicted value of the oil chamber pressure change rate is 0, and the predicted value of the stabilizer bar's mid-position displacement is 0.

[0087] Through steps S221-S224, based on the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the previous moment, the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are calculated. Compared to directly using the original measurement data from the previous moment to calculate the predicted values ​​at the current moment, this method allows the predicted data to more closely approximate the actual state of the stabilizer bar, thereby improving the accuracy of oil leak detection.

[0088] In some embodiments, after obtaining the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment in step S210, the measurement vector matrix corresponding to the current moment can also be determined.

[0089] Specifically, since the mid-position displacement of the stabilizer bar and the oil chamber pressure are parameters that can be directly measured, while the rate of change of the oil chamber pressure cannot be directly measured, in this embodiment, based on the directly measurable parameters, the measurement vector matrix at the current moment is defined as follows: .

[0090] in, This represents the measurement vector matrix at the current moment. For example, if the actual measured displacement of the stabilizer bar at the current moment is 0.2 and the oil chamber pressure is 1.5, then the measurement vector matrix at the current moment is... .

[0091] In this way, in subsequent step S230, the measurement vector matrix at the current time can be used as a basis. The predicted state vector matrix corresponding to the current time step Perform state estimation.

[0092] Continue to combine Figures 1-3 As shown, in some exemplary embodiments, step S230 above utilizes a preset estimation algorithm to estimate the actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the current moment, which may specifically include: Based on the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment, the calculated predicted values ​​of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure at the current moment are corrected to obtain the corrected predicted values ​​of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure.

[0093] The corrected predicted values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate are then used as the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the current moment, respectively.

[0094] For example, the actual measured mid-displacement of the stabilizer bar at the current moment is 0.2, and the oil chamber pressure is 1.5 (the measurement vector matrix at the current moment). The predicted values ​​for the current moment are: stabilizer mid-displacement (0), oil chamber pressure (2), and oil chamber pressure change rate (0) (the predicted state vector matrix corresponding to the current moment). If the calculated predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are corrected to reduce the error, the corrected predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate are the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate.

[0095] By correcting the calculated predicted values ​​of oil chamber pressure, oil chamber pressure change rate, and stabilizer bar mid-position displacement, the actual oil chamber pressure, actual oil chamber pressure change rate, and actual stabilizer bar mid-position displacement can be directly obtained. With the continuous iteration of the preset estimation algorithm as the stabilizer bar is used, the prediction accuracy can be gradually improved. This allows subsequent predictions of various parameters calculated using the preset estimation algorithm to be closer to the true values, thus facilitating the acquisition of more accurate data and reducing the misjudgment rate of oil leak fault detection.

[0096] Continue by Figures 1-3 and combined Figure 4 As shown, in some exemplary embodiments, the above step S230, which corrects the calculated predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment, may specifically include the following steps S231-S235.

[0097] Step S231: Obtain the predicted uncertainty of the current moment's stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate.

[0098] The prediction uncertainty characterizes the "confidence level" of the calculated predicted values ​​of the current state parameters (i.e., stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate). For example, the greater the prediction uncertainty corresponding to the stabilizer bar mid-displacement, the closer the calculated predicted value of the stabilizer bar mid-displacement is to the actual stabilizer bar mid-displacement.

[0099] Specifically, in this embodiment, the covariance matrix can be used to quantify the uncertainty of the parameters. In some embodiments, combined with Figure 5As shown, in step S231, the prediction uncertainty of the current moment's stabilizer rod mid-position displacement prediction value, oil chamber pressure prediction value, and oil chamber pressure change rate prediction value can be obtained, which may specifically include the following steps S2311-S2314.

[0100] Step S2311: Obtain the uncertainties corresponding to the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure under the preset historical state, and establish a preset error covariance matrix composed of the uncertainties corresponding to the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure under the preset historical state.

[0101] Specifically, this preset error covariance matrix represents the uncertainty of the state parameters (oil chamber pressure, oil chamber pressure change rate, and stabilizer rod mid-displacement) under the preset historical state. For example, this preset error covariance matrix... for: .

[0102] Each element in the preset error covariance matrix represents the variance of each state parameter or the covariance between different state parameters under preset historical conditions.

[0103] Specifically, this preset error covariance matrix The first line in the table represents the variance of the oil chamber pressure under the preset historical state (this variance represents the uncertainty of the oil chamber pressure), as well as the covariance between the oil chamber pressure, the rate of change of the oil chamber pressure, and the mid-position displacement of the stabilizer bar.

[0104] The second line represents the covariance between the rate of change of oil chamber pressure and the oil chamber pressure, the variance of the rate of change of oil chamber pressure (which represents the uncertainty of the rate of change of oil chamber pressure), and the covariance between the rate of change of oil chamber pressure and the mid-position displacement of the stabilizer bar under the preset historical state.

[0105] The third line represents the covariance between the mid-range displacement of the stabilizer bar and the pressure in the oil chamber, the covariance between the mid-range displacement of the stabilizer bar and the rate of change of the pressure in the oil chamber, and the variance of the mid-range displacement of the stabilizer bar (this variance represents the uncertainty of the mid-range displacement of the stabilizer bar) under the preset historical state.

[0106] Among these, the larger the variance of the oil chamber pressure, the greater the uncertainty of the oil chamber pressure under the preset historical conditions. Similarly, the larger the variance of the rate of change of the oil chamber pressure, the greater the uncertainty of the rate of change of the oil chamber pressure under the preset historical conditions. Finally, the larger the variance of the stabilizer bar mid-displacement, the greater the uncertainty of the stabilizer bar mid-displacement under the preset historical conditions.

[0107] Step S2312: Obtain the model noise covariance matrix, which represents the uncertainty of the preset state prediction model in predicting the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure.

[0108] Specifically, the noise covariance matrix Q of the model is used to represent the uncertainty caused by environmental disturbances or model inaccuracies, which can be obtained by calibrating the stabilizer bar.

[0109] For example, the noise covariance matrix Q of this model is: .

[0110] The first row corresponds to the oil chamber pressure, the second row corresponds to the oil chamber pressure change rate, and the third row corresponds to the mid-position displacement of the stabilizer bar.

[0111] Step S2313: Calculate the prediction error covariance matrix based on the preset error covariance matrix and the model noise covariance matrix.

[0112] Step S2314: Based on the prediction error covariance matrix, determine the prediction uncertainty of the current moment's predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate.

[0113] Specifically, the prediction error covariance matrix P1 can be calculated using the model noise covariance matrix Q and the preset error covariance matrix under the preset historical state.

[0114] The prediction error covariance matrix P1 is a matrix composed of the prediction uncertainty of the stabilizer bar mid-displacement, the prediction uncertainty of the oil chamber pressure, and the prediction uncertainty of the oil chamber pressure change rate. Specifically, the first row of this prediction error covariance matrix corresponds to the prediction uncertainty of the oil chamber pressure; the second row corresponds to the prediction uncertainty of the oil chamber pressure change rate; and the third row corresponds to the prediction uncertainty of the stabilizer bar mid-displacement.

[0115] Through steps S2311-S2313, the prediction error covariance matrix is ​​calculated using the preset error covariance matrix and the model noise covariance matrix to determine the prediction uncertainty at the current moment. This quantification of the prediction uncertainties for the current moment's stabilizer mid-displacement, oil chamber pressure, and oil chamber pressure change rate provides a reliable basis for subsequent weight calculations, thereby improving the accuracy of subsequent oil leak fault diagnosis.

[0116] In some embodiments, the prediction error covariance matrix The calculation process includes: Based on the preset error covariance matrix The historical error covariance matrix is ​​calculated, consisting of the errors in the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate transmitted from the preset historical state to the current state. The historical error covariance matrix is ​​then superimposed with the model noise covariance matrix Q to obtain the prediction error covariance matrix. .

[0117] Specifically, the prediction error covariance matrix The calculation formulas include:

[0118] in, Let be the covariance matrix of the prediction error.

[0119] A is the state transition matrix. For example, the state transition matrix A (same as the example in step S212 above) is: .

[0120] P0 is a preset error covariance matrix, such as the example in step S2311 above: the preset error covariance matrix is ​​P0: .

[0121] Q is the model noise covariance matrix, which can be, for example, the one in step S2312 above: .

[0122] Among them, through The historical error covariance matrix is ​​calculated as described above. This historical error covariance matrix describes the error in the predicted values ​​of the state parameters at the previous time step that has been propagated to the current time step. Then, this historical error covariance matrix is ​​summed with the model noise covariance matrix to obtain the current prediction error covariance matrix. .

[0123] For example, the prediction error covariance matrix at the current moment :

[0124] Among them, the prediction error covariance matrix This represents the prediction uncertainty of the current moment's predicted oil chamber pressure, oil chamber pressure change rate, and stabilizer bar mid-position displacement, based on data corresponding to preset historical states.

[0125] Among them, the prediction error covariance matrix The first row corresponds to the prediction uncertainty of the predicted value of the oil chamber pressure, the second row corresponds to the prediction uncertainty of the predicted value of the oil chamber pressure change rate, and the third row corresponds to the prediction uncertainty of the predicted value of the stabilizer bar mid-displacement.

[0126] It is worth noting that, assuming the detection at the current moment is completed and the next moment is reached, the prediction error covariance matrix calculated at the current moment... This will be used as the preset error covariance matrix for the next time step, and the prediction error covariance matrix for that next time step will be calculated, and so on.

[0127] Thus, the prediction error covariance matrix is ​​used... The calculation formula can be used to calculate the prediction error covariance matrix, which represents the prediction uncertainty of the predicted oil chamber pressure, the predicted rate of change of oil chamber pressure, and the predicted mid-position displacement of the stabilizer bar at the current moment. This makes it easier to use the prediction error covariance matrix for subsequent correction, thereby reducing the probability of misjudging oil leakage.

[0128] Step S232: Obtain the measurement uncertainty of the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment.

[0129] Specifically, the stabilizer bar mid-position displacement and oil chamber pressure actually measured at the current moment are obtained by the stabilizer bar mid-position displacement sensor 16 and the pressure sensor, respectively. By testing the stabilizer bar mid-position displacement sensor 16 and the pressure sensor, the measurement uncertainty of the stabilizer bar mid-position displacement and oil chamber pressure can be obtained.

[0130] In this embodiment, the measurement noise covariance matrix R is used to represent the measurement uncertainty of the mid-position displacement of the stabilizer bar and the pressure of the oil chamber obtained by actual measurement at the current moment.

[0131] For example: the measurement noise covariance matrix R is: .

[0132] In this measurement noise covariance matrix R, the first row represents the measurement uncertainty of the current actual measured oil chamber pressure, and the second row represents the measurement uncertainty of the current actual measured mid-position displacement of the stabilizer bar.

[0133] Step S233: Based on the prediction uncertainty and measurement uncertainty, calculate the correction weights for the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment.

[0134] Specifically, the predicted and measured values ​​are balanced based on the prediction uncertainty and measurement uncertainty to obtain parameter values ​​that better reflect the actual state. For example, if the prediction uncertainty corresponding to the oil cavity pressure is larger and the measurement uncertainty is smaller, then the measured oil cavity pressure can be given more weight, that is, the oil cavity pressure actually measured by the sensor can be trusted more, while the predicted oil cavity pressure can be trusted less.

[0135] Based on the prediction uncertainty and measurement uncertainty, the confidence levels of the predicted and measured values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate are allocated. Based on this, the correction weights for the predicted values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate are determined. This ensures that the corrected actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate better reflect the true state of the stabilizer bar, thereby reducing the probability of misdiagnosis of stabilizer bar oil leakage faults.

[0136] In some of these exemplary implementations, refer to Figure 6 In this context, step S233 can be specifically implemented through steps S2331 and S2332.

[0137] Step S2331: Based on the prediction uncertainty and measurement uncertainty, calculate the confidence weights corresponding to the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment.

[0138] Specifically, in step S2331, the calculation process of the trust weight includes: Obtain the observation matrix H used to transform the state vector matrix of the stabilizer bar into the measurement vector matrix.

[0139] Then, the prediction error covariance matrix, consisting of the prediction uncertainties corresponding to the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate, is determined; that is, the prediction error covariance matrix mentioned above. .

[0140] The measurement noise covariance matrix, which is composed of the measurement uncertainty corresponding to the mid-position displacement of the stabilizer bar and the oil chamber pressure, is determined. This is the aforementioned measurement noise covariance matrix R.

[0141] Then, based on the prediction error covariance matrix We measure the noise covariance matrix R and the observation matrix H, and calculate the confidence weights.

[0142] Wherein, the observation matrix H is used for the state vector matrix X k With measurement vector matrix z k Transformation is performed between them. The state vector matrix X... k The vector matrix z is composed of the mid-position displacement of the stabilizer bar, the pressure in the oil chamber, and the rate of change of the oil chamber pressure, and is used to characterize the state of the stabilizer bar. k It is a vector matrix composed of the mid-position displacement of the stabilizer bar and the pressure in the oil chamber, used to characterize the stabilizer bar parameters measured in practice.

[0143] Continuing with the example from the above embodiments, the prediction error covariance matrix is ​​composed of the prediction uncertainties of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate. :

[0144] The measurement noise covariance matrix R, composed of the measurement uncertainties of the mid-position displacement of the stabilizer bar and the oil chamber pressure, is: .

[0145] Since the stabilizer bar's state parameters include three parameters—stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate—and the stabilizer bar's measurement parameters include two parameters—stabilizer bar mid-position displacement and oil chamber pressure—an observation matrix H is also provided in this embodiment to establish a mapping relationship between the state parameters and the measurement parameters.

[0146] For example, for the state vector matrix and measurement vector matrix in this embodiment, the observation matrix H can be: .

[0147] In step S2331, the confidence weights corresponding to the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment can be calculated using the following formula: Trust weight matrix = .For example: .

[0148] In this example, the first row of the confidence weights represents the confidence weight corresponding to the oil chamber pressure, and the second row represents the confidence weight corresponding to the stabilizer bar mid-displacement. The confidence weights indicate the weight that the measured values ​​of the corresponding parameters should be assigned. For example, the confidence weight for oil chamber pressure is relatively small at 0.8196, while the confidence weight for stabilizer bar mid-displacement is relatively large at 9.009. This means that the actual measured oil chamber pressure is not overly trusted, while the actual measured stabilizer bar mid-displacement is trusted more (correspondingly, the predicted oil chamber pressure is trusted more, while the predicted stabilizer bar mid-displacement is not overly trusted).

[0149] After the trust weight is calculated, the following step S2332 can be performed to calculate the corrected weight based on the trust weight.

[0150] Step S2332: Based on the confidence weights corresponding to the stabilizer bar mid-displacement and oil chamber pressure measured at the current moment, and the prediction uncertainty, calculate the correction weights for the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment.

[0151] Specifically, the formula for calculating this corrected weight includes:

[0152] Wherein, K is the corrected weight matrix (also known as the Kalman gain matrix), which is a vector matrix composed of the corrected weights of the current moment's stabilizer mid-displacement, oil chamber pressure, and oil chamber pressure change rate.

[0153] This is the prediction error covariance matrix.

[0154] H is the observation matrix mentioned above.

[0155] R is the measurement noise covariance matrix, which is a vector matrix composed of the measurement uncertainties of the stabilizer mid-displacement, oil chamber pressure, and oil chamber pressure change rate obtained at the current moment.

[0156] For example, .

[0157] Wherein, 0.9097 is the correction weight corresponding to the stabilizer bar's oil chamber pressure, 0.0819 is the correction weight corresponding to the stabilizer bar's oil chamber pressure change rate, and 0.9099 is the correction weight corresponding to the stabilizer bar's mid-position displacement. A larger correction weight indicates greater confidence in the measured values.

[0158] Through steps S2331-S2332, the confidence weight of the measured data is first calculated, and then the correction weight is derived. This provides a method for determining the correction weight, and enables the measured data with high confidence to play a greater corrective role, thereby improving the accuracy of the corrected data.

[0159] After determining the correction weight, step S234 is executed to calculate the correction amount based on the correction weight. After calculating the correction amount, step S235 is executed to calculate the corrected predicted value of the stabilizer bar mid-position displacement, the predicted value of the oil chamber pressure, and the predicted value of the oil chamber pressure change rate.

[0160] Step S234: Based on the correction weight, the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment, and the calculated predicted values ​​of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the oil chamber pressure change rate at the current moment, calculate the correction amount to correct the predicted values ​​of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the oil chamber pressure change rate at the current moment.

[0161] Specifically, in step S233, correction weights for the predicted stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate have been calculated. These correction weights define the degree of confidence between the predicted values ​​and the measured values ​​obtained by the sensors. Based on these correction weights, the predicted values ​​can be corrected so that the corrected predicted values ​​are closer to the true values.

[0162] Specifically, in step S234, the calculation process of the correction amount includes: determining the predicted state vector matrix based on the predicted current moment's mid-displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure, such as the predicted state vector matrix at the current moment in the example of step S213 above. .

[0163] Then, based on the predicted state vector matrix (that is, the... The observation matrix H and the measurement vector matrix Zk are used to calculate the deviation between the actual measured value and the predicted value of the mid-position displacement of the stabilizer bar and the pressure in the oil chamber, and the residual matrix composed of the deviation is obtained.

[0164] Then, based on the residual matrix and the corrected weights, the correction amount is calculated.

[0165] Specifically, the formula for calculating this correction amount includes:

[0166] in, The measurement vector matrix is ​​a vector matrix composed of the mid-position displacement of the stabilizer bar and the pressure in the oil chamber obtained from the actual measurement at the current moment.

[0167] The predicted state vector matrix is ​​a vector matrix composed of the predicted current moment's mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure.

[0168] Specifically, through Calculate the residual matrix, and then multiply the residual matrix by the correction weight to obtain the correction amount.

[0169] For example, the corrected weight K and the measurement vector matrix calculated in the above example... Predicted state vector matrix And the observation matrix H, substituted into the formula for calculating the correction, first calculate the residual matrix: In the residual matrix, -0.5 represents the deviation between the actual measured oil chamber pressure of the stabilizer bar and the predicted oil chamber pressure of the stabilizer bar, which is -0.5, and 0.2 represents the deviation between the actual measured mid-range displacement of the stabilizer bar and the predicted mid-range displacement of the stabilizer bar, which is 0.2.

[0170] Then, based on the residual matrix and the corrected weights, the following calculations were performed: .

[0171] Step S235: According to the correction amount, correct the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment to obtain the corrected predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate.

[0172] For example, the predicted mid-displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure at the current moment are updated and corrected based on the correction amount, which can be determined by the following formula: .

[0173] in, The corrected state vector matrix is ​​given. The calculated value of 1.5452 (in MPa) represents the corrected oil chamber pressure of the stabilizer bar at the current moment, -0.0409 (in MPa / s) represents the corrected oil chamber pressure change rate of the stabilizer bar at the current moment, and 0.1819 (in mm) represents the corrected mid-position displacement of the stabilizer bar at the current moment.

[0174] Through steps S231-S235, the prediction uncertainty and measurement uncertainty are first calculated. Then, based on the two uncertainties, the correction weight is determined, and the correction amount is obtained. This correction amount is then used to update and correct the predicted mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure at the current moment. This ensures that the errors of the corrected mid-position displacement of the stabilizer bar, the corrected oil chamber pressure, and the corrected rate of change of the oil chamber pressure are smaller than the errors of the actual measurement and the prediction. This improves the accuracy of the determined oil chamber pressure, the rate of change of the oil chamber pressure, and the mid-position displacement of the stabilizer bar, thereby improving the accuracy of stabilizer bar oil leakage detection and reducing misjudgments.

[0175] After obtaining the corrected stabilizer bar mid-position displacement, the corrected oil chamber pressure, and the corrected oil chamber pressure change rate, that is, the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the current moment, the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate can then be used to determine whether the stabilizer bar has an oil leakage fault.

[0176] Continue by Figure 1 and Figure 2 and combined Figure 7 As shown, in some exemplary embodiments, step S240 above, based on the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate, determines whether the stabilizer bar is currently faulty, which may specifically include the following steps S241-S244.

[0177] Step S241: Perform a preset calibration test on the stabilizer bar to obtain multiple sets of calibration data.

[0178] Each set of calibration data includes operating parameters and the failure status of the stabilizer bar under the operating parameters. The operating parameters include oil chamber pressure, oil chamber pressure change rate, and stabilizer bar mid-position displacement.

[0179] For example, when the stabilizer bar in the vehicle is in normal operating condition (no oil leakage), multiple sets of calibration data are measured, and when the stabilizer bar in the vehicle is in fault condition (oil leakage condition), multiple sets of calibration data are also measured. The calibration data includes operating parameters and the corresponding stabilizer bar status (stabilizer bar status includes normal operating condition and fault condition).

[0180] For example: At any given moment, the measured operating parameters include: stabilizer bar oil chamber pressure of 0.5, oil chamber pressure change rate of 0, and stabilizer bar mid-position displacement of 0. The stabilizer bar state corresponding to these operating parameters is normal operating condition (no oil leakage). At another given moment, the measured operating parameters include: stabilizer bar oil chamber pressure of 1.48, oil chamber pressure change rate of -0.02, and stabilizer bar mid-position displacement of 0.01. The stabilizer bar state corresponding to these operating parameters is also normal operating condition (no oil leakage).

[0181] For example, at a certain moment, the operating parameters are: oil chamber pressure is 1.42, oil chamber pressure change rate is -0.05, and stabilizer bar mid-position displacement is 0.05. The stabilizer bar state corresponding to these operating parameters is a fault state (oil leakage).

[0182] In this way, through calibration, a large amount of operating parameters of the stabilizer bar under normal operating conditions and oil leakage operating conditions, as well as calibration data of the corresponding oil leakage conditions, can be obtained.

[0183] Step S242: Based on the calibration data of each group, determine the oil chamber pressure judgment strategy, the oil chamber pressure change rate judgment strategy, and the stabilizer rod mid-position displacement judgment strategy, and construct the decision tree corresponding to the oil chamber pressure, the decision tree corresponding to the oil chamber pressure change rate, and the decision tree corresponding to the stabilizer rod mid-position displacement, respectively.

[0184] Among them, the decision tree is used to make fault state decisions based on the corresponding judgment strategy (more specifically, it is used to make oil leak state decisions based on the corresponding judgment strategy).

[0185] For example, by analyzing the relationship between the oil chamber pressure and the stabilizer bar state through calibration data, it can be found that when the oil chamber pressure is less than a preset pressure value (e.g., less than 1.45), it corresponds to a fault state. Therefore, the oil chamber pressure judgment strategy can determine the fault when the oil chamber pressure is less than the preset pressure value.

[0186] Similarly, by analyzing the relationship between the oil chamber pressure change rate and the stabilizer bar state through calibration data, for example, when the oil chamber pressure change rate is negative and the absolute value of the oil chamber pressure change rate is greater than the preset pressure change rate threshold (e.g., the oil chamber pressure change rate is less than -0.04), it corresponds to a fault state. Therefore, this oil chamber pressure change rate judgment strategy can determine the fault when the oil chamber pressure change rate is negative and the absolute value of the oil chamber pressure change rate is greater than the preset pressure change rate threshold.

[0187] Similarly, by using calibration data, the relationship between stabilizer bar mid-position displacement and oil leakage status can be analyzed. For example, when the stabilizer bar mid-position displacement is greater than a preset displacement threshold (e.g., stabilizer bar mid-position displacement greater than 0.05), it corresponds to a fault state. Therefore, this stabilizer bar mid-position displacement judgment strategy can determine a fault when the stabilizer bar mid-position displacement is greater than the preset displacement threshold.

[0188] After determining the decision strategy corresponding to each state parameter, a decision tree is built for each state parameter. A decision tree is a classification and regression algorithm that segments data through a tree-like structure, thereby achieving data classification and prediction.

[0189] For example, for the three state parameters—oil chamber pressure, oil chamber pressure change rate, and stabilizer bar mid-displacement—decision trees are established for oil chamber pressure, oil chamber pressure change rate, and stabilizer bar mid-displacement, respectively. Figure 8 As shown, where, Figure 8 The left side shows an example of a decision tree corresponding to the oil chamber pressure, the middle side shows an example of a decision tree corresponding to the rate of change of oil chamber pressure, and the right side shows an example of a decision tree corresponding to the mid-displacement of the stabilizer bar.

[0190] Step S243: Based on the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate, use the decision tree corresponding to the oil chamber pressure, the decision tree corresponding to the oil chamber pressure change rate, and the decision tree corresponding to the stabilizer bar mid-position displacement to make fault decisions, and obtain the oil leakage decision result corresponding to the oil chamber pressure, the fault decision result corresponding to the oil chamber pressure change rate, and the fault decision result corresponding to the stabilizer bar mid-position displacement.

[0191] Specifically, in step S243, the estimated actual mid-displacement of the stabilizer bar is used as the input to the decision tree corresponding to the mid-displacement of the stabilizer bar. The decision tree makes the corresponding fault decision and obtains the fault decision result corresponding to the mid-displacement of the stabilizer bar.

[0192] The estimated actual oil chamber pressure is used as the input to the decision tree corresponding to the oil chamber pressure. The decision tree corresponding to the oil chamber pressure makes the corresponding fault decision and obtains the fault decision result corresponding to the oil chamber pressure.

[0193] The estimated actual oil chamber pressure change rate is used as the input to the decision tree corresponding to the oil chamber pressure change rate. The decision tree corresponding to the oil chamber pressure change rate makes the corresponding fault decision and obtains the fault decision result corresponding to the oil chamber pressure change rate.

[0194] Step S244: If the fault decision results corresponding to the oil chamber pressure, the fault decision results corresponding to the oil chamber pressure change rate, and the fault decision results corresponding to the stabilizer bar mid-position displacement meet the preset fault conditions, then determine that the stabilizer bar has failed.

[0195] The preset conditions include: there are at least two fault decision results that determine that the stabilizer bar has failed.

[0196] Correspondingly, if there is a fault decision result indicating a stabilizer bar fault, or if there is no fault decision result indicating a stabilizer bar fault, then it is determined that the stabilizer bar has not failed.

[0197] By using steps S241-S244, three decision trees are set up, and a stabilizer bar failure is only confirmed when at least two of the three decision trees result in a stabilizer bar failure. This helps reduce misjudgments caused by random fluctuations in a single parameter. Furthermore, analyzing a large amount of calibration data to obtain the decision trees also helps reduce the probability of misjudgments caused by changes in oil chamber pressure and stabilizer bar mid-position displacement during normal operation of the stabilizer bar.

[0198] It is worth noting that, regarding the fault detection method of this embodiment, based on the above exemplary implementations, in specific implementation, as a preferred embodiment, it is still based on... Figures 1-7 As shown, it may include, for example: Taking the detection of oil leakage faults in stabilizer bars as an example, other faults caused by the stabilizer bars (faults that can be determined using stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate) can be referred to the oil leakage fault detection process, which will not be elaborated here.

[0199] Based on the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate estimated at the previous moment, predict the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment.

[0200] Obtain the current moment's actual measurement of the stabilizer bar's mid-position displacement and the oil chamber pressure.

[0201] Then, based on the actual measured mid-displacement of the stabilizer bar and the oil chamber pressure at the current moment, as well as the predicted mid-displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure at the current moment, the prediction error covariance matrix and the measurement noise covariance matrix are calculated using a preset estimation algorithm, and the correction weight K (i.e., Kalman gain) is calculated based on the prediction error covariance matrix and the measurement noise covariance matrix.

[0202] Using the correction weight K, the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are corrected to obtain the corrected stabilizer bar mid-position displacement, corrected oil chamber pressure, and corrected oil chamber pressure change rate.

[0203] The corrected stabilizer bar mid-position displacement, corrected oil chamber pressure, and corrected oil chamber pressure change rate are used as the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate for subsequent oil leakage fault diagnosis.

[0204] Based on the corrected stabilizer bar mid-position displacement, corrected oil chamber pressure, and corrected oil chamber pressure change rate, the corresponding decision trees are used to make oil leakage decisions. If there are two or more oil leakage decisions indicating oil leakage, it is determined that the stabilizer bar has an oil leakage fault, thus completing the oil leakage fault detection.

[0205] In the above preferred embodiments, the specific processing procedures, such as correcting the weight K, correcting the calculated predicted values ​​of oil chamber pressure, oil chamber pressure change rate, and stabilizer rod mid-position displacement based on the corrected weight K, can still be found in the descriptions of the above exemplary embodiments, and will not be repeated here.

[0206] The fault detection method in this embodiment adopts the design described above. It uses predicted values ​​of the current stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate, along with measured values ​​of the current stabilizer bar mid-position displacement and oil chamber pressure, to comprehensively estimate the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate—a value closer to the actual situation—using a preset estimation algorithm. The method then determines whether a fault has occurred based on these actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate, rather than solely relying on measured values ​​for fault diagnosis. Because the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate more closely reflect the true state of the stabilizer bar than measured values, fault detection using these parameters improves the accuracy of stabilizer bar fault detection and reduces the false positive rate.

[0207] Furthermore, the fault detection method in this embodiment also calculates the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment based on the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the previous moment. Compared to directly using the original measurement data from the previous moment to calculate the predicted values ​​at the current moment, this allows the predicted data to be closer to the actual state of the stabilizer bar, thereby improving the accuracy of oil leak detection.

[0208] Meanwhile, in the fault detection method of this embodiment, the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are also corrected. This helps to prevent misjudgment of faults due to measurement data deviation or prediction data deviation, thereby improving the accuracy of stabilizer bar fault judgment.

[0209] Meanwhile, in the fault detection method of this embodiment, the confidence levels of the predicted and measured values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate are allocated based on the prediction uncertainty and measurement uncertainty. The correction weights for the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate are determined accordingly. This makes the corrected actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate more consistent with the true state of the stabilizer bar, thereby helping to reduce the probability of misjudging stabilizer bar oil leakage faults.

[0210] Meanwhile, in the fault detection method of this embodiment, the prediction error covariance matrix is ​​also calculated using a preset error covariance matrix and a model noise covariance matrix to calculate the prediction uncertainty at the current moment. Thus, by quantifying the prediction uncertainties of the current moment's stabilizer median displacement, oil chamber pressure, and oil chamber pressure change rate, a reliable basis can be provided for subsequent calculation of correction weights, thereby improving the accuracy of subsequent oil leak fault judgment.

[0211] Furthermore, the fault detection method in this embodiment also sets up three decision trees, and a fault is determined to have occurred in the stabilizer bar only when at least two of the three decision trees result in a stabilizer bar fault. This helps to reduce the probability of misjudging a fault due to accidental fluctuations in one of the parameters.

[0212] An embodiment of the second aspect of this application provides a vehicle equipped with a stabilizer bar. For example... Figure 1 As shown, the stabilizer bar includes a piston-type hydraulic device 1.

[0213] Reference Figure 9The vehicle's controller includes a processor 910 and a memory 920. The processor 910 and the memory 920 are connected, for example, via a bus. Optionally, the vehicle's controller may also include a transceiver. It should be noted that in practical applications, the transceiver is not limited to one unit.

[0214] The memory 920 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 910. The processor 910 is used to execute the application code stored in the memory 920 to implement the content shown in the aforementioned fault detection method embodiment.

[0215] The vehicle controller in this embodiment, by executing the detection method in the above-described fault detection method embodiment, can make the estimated actual state parameters more closely match the true state of the stabilizer bar, thereby improving the accuracy of stabilizer bar fault detection and reducing the fault misjudgment rate.

[0216] The above descriptions are merely some embodiments of this application and are not intended to limit this application. The technical features or structures in the foregoing different embodiments can be arbitrarily combined to form other specific technical solutions as needed. For those skilled in the art, this application can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of the claims of this application.

Claims

1. A fault detection method applied to vehicles, characterized in that, The fault detection method includes: The stabilizer bar in the vehicle is obtained as follows: the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under a preset historical state, as well as the stabilizer bar mid-position displacement and oil chamber pressure actually measured at the current moment. Based on the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical conditions, calculate the predicted values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment. Based on the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment, as well as the calculated predicted values ​​of the mid-position displacement of the stabilizer bar, the predicted value of the oil chamber pressure, and the predicted value of the rate of change of the oil chamber pressure at the current moment, the actual mid-position displacement of the stabilizer bar, the actual oil chamber pressure, and the actual rate of change of the oil chamber pressure at the current moment are estimated using a preset estimation algorithm. Based on the estimated actual mid-position displacement of the stabilizer bar, the actual oil chamber pressure, and the actual oil chamber pressure change rate at the current moment, it is determined whether the stabilizer bar is currently malfunctioning.

2. The fault detection method according to claim 1, characterized in that, The stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical state are the actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate estimated by the preset estimation algorithm at the previous moment. The step of calculating the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment based on the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate under the preset historical conditions includes: Based on the estimated actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate obtained at the previous moment, the state vector matrix corresponding to the preset historical state is determined. Based on the preset state prediction model of the stabilizer bar, a state transition matrix is ​​established to predict the state vector matrix corresponding to the stabilizer bar at the next moment. Calculate the product of the state transition matrix and the state vector matrix corresponding to the preset historical state to obtain the predicted state vector matrix of the stabilizer at the current moment; Based on the state vector matrix of the stabilizer bar predicted at the current moment, the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are determined.

3. The fault detection method according to claim 1, characterized in that, The step of estimating the actual mid-displacement of the stabilizer bar, the actual oil chamber pressure, and the actual rate of change of the oil chamber pressure at the current moment using a preset estimation algorithm includes: Based on the stabilizer bar mid-position displacement and oil chamber pressure obtained from the actual measurement at the current moment, the calculated predicted values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are corrected to obtain the corrected predicted values ​​of stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate. The corrected predicted values ​​of the stabilizer bar mid-position displacement, the oil chamber pressure, and the oil chamber pressure change rate are used as the estimated actual stabilizer bar mid-position displacement, actual oil chamber pressure, and actual oil chamber pressure change rate at the current moment, respectively.

4. The fault detection method according to claim 3, characterized in that, The correction of the calculated predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment includes: Obtain the prediction uncertainty of the current moment's predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate; Obtain the measurement uncertainty of the stabilizer bar mid-position displacement and oil chamber pressure at the current moment; Based on the prediction uncertainty and the measurement uncertainty, the correction weights for the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are calculated. Based on the correction weight, the actual measured mid-position displacement of the stabilizer bar and the oil chamber pressure at the current moment, and the calculated predicted values ​​of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the oil chamber pressure change rate at the current moment, the correction amount for correcting the predicted values ​​of the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the oil chamber pressure change rate at the current moment is calculated. Based on the aforementioned correction amount, the predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment are corrected to obtain the corrected predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate.

5. The fault detection method according to claim 4, characterized in that, The prediction uncertainty of obtaining the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment includes: Obtain the uncertainties corresponding to the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure under the preset historical state, and establish a preset error covariance matrix composed of the uncertainties corresponding to the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure under the preset historical state. Obtain the model noise covariance matrix, which characterizes the uncertainty of the preset state prediction model in predicting the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure. Based on the preset error covariance matrix and the model noise covariance matrix, the prediction error covariance matrix is ​​calculated. Based on the prediction error covariance matrix, determine the prediction uncertainties of the current moment's predicted values ​​for the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate.

6. The fault detection method according to claim 5, characterized in that, The calculation process of the prediction error covariance matrix includes: Based on the preset error covariance matrix, calculate the historical error covariance matrix composed of the errors in the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate transmitted from the preset historical state to the current state; The prediction error covariance matrix is ​​obtained by superimposing the historical error covariance matrix with the model noise covariance matrix.

7. The fault detection method according to claim 4, characterized in that, The correction weights, calculated based on the prediction uncertainty and the measurement uncertainty, for correcting the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment, include: Based on the prediction uncertainty and the measurement uncertainty, calculate the confidence weights corresponding to the actual measured mid-displacement of the stabilizer bar and the oil chamber pressure at the current moment; Based on the confidence weights corresponding to the stabilizer bar mid-displacement and oil chamber pressure measured at the current moment, and the prediction uncertainty, correction weights are calculated to correct the predicted values ​​of the stabilizer bar mid-displacement, oil chamber pressure, and oil chamber pressure change rate at the current moment.

8. The fault detection method according to claim 7, characterized in that, The step of calculating the confidence weights corresponding to the actual measured mid-displacement of the stabilizer bar and the oil chamber pressure at the current moment based on the prediction uncertainty and the measurement uncertainty includes: Obtain the observation matrix used to transform the state vector matrix of the stabilizer bar into the measurement vector matrix; Determine the prediction error covariance matrix composed of the prediction uncertainty corresponding to the mid-position displacement of the stabilizer bar, the oil chamber pressure, and the rate of change of the oil chamber pressure; Determine the measurement noise covariance matrix composed of the measurement uncertainties corresponding to the mid-position displacement of the stabilizer bar and the oil chamber pressure; The confidence weight is calculated based on the prediction error covariance matrix, the measurement noise covariance matrix, and the observation matrix.

9. The fault detection method according to claim 8, characterized in that, The calculation process for the correction amount includes: The predicted state vector matrix is ​​determined based on the current predicted values ​​of the stabilizer bar mid-position displacement, oil chamber pressure, and oil chamber pressure change rate. Based on the predicted state vector matrix, the observation matrix, and the measurement vector matrix, the deviations between the actual measured values ​​and the predicted values ​​corresponding to the mid-position displacement of the stabilizer bar and the pressure in the oil chamber are calculated, and a residual matrix composed of the deviations is obtained. The correction amount is calculated based on the residual matrix and the correction weights.

10. The fault detection method according to claim 1, characterized in that, The determination of whether the stabilizer bar is currently faulty, based on the estimated actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate, includes: Multiple sets of calibration data are obtained by performing a preset calibration test on the stabilizer bar. Each set of calibration data includes operating parameters and the fault state of the stabilizer bar under the operating parameters. Based on the calibration data of each group, the oil chamber pressure judgment strategy, the oil chamber pressure change rate judgment strategy, and the stabilizer bar mid-position displacement judgment strategy are determined, and decision trees corresponding to oil chamber pressure, oil chamber pressure change rate, and stabilizer bar mid-position displacement are constructed respectively. Based on the estimated actual stabilizer bar mid-displacement, actual oil chamber pressure, and actual oil chamber pressure change rate, fault decisions are made using the decision tree corresponding to the oil chamber pressure, the decision tree corresponding to the oil chamber pressure change rate, and the decision tree corresponding to the stabilizer bar mid-displacement, respectively, to obtain the fault decision results corresponding to the oil chamber pressure, the fault decision results corresponding to the oil chamber pressure change rate, and the fault decision results corresponding to the stabilizer bar mid-displacement. If the fault decision result corresponding to the oil chamber pressure, the fault decision result corresponding to the oil chamber pressure change rate, and the fault decision result corresponding to the stabilizer bar mid-position displacement meet the preset conditions, it is determined that the stabilizer bar has failed. The preset conditions include: there are at least two fault decision results that determine that the stabilizer bar has failed.

11. A vehicle, wherein the vehicle is equipped with a stabilizer bar, characterized in that: The vehicle controller includes a memory (920) and a processor (910). The memory (920) stores a computer program, which, when run by the processor (910), executes the fault detection method according to any one of claims 1-10.

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