Semi-active structure system load and damper health state monitoring method and device

Abstract

The invention relates to the technical field of vehicle suspension parameter identification and health monitoring, in particular to a semi-active structure system load and damper health state monitoring method and device.The method comprises the steps that under the condition that a vehicle is in a target monitoring working condition, based on a preset semi-active suspension damping value, a damper is monitored; the method comprises the following steps: respectively carrying out data processing on a first target measurement value of a first target sensor at the center of a wheel of a vehicle and a second target measurement value of a second target sensor at the center of a vehicle body, so as to obtain a first observation vector and a second observation vector, and carrying out offline analysis by utilizing a target batch least square method; and the sprung mass and the vehicle damping value of the vehicle are obtained through identification, so that the semi-active structure system load of the vehicle and the health state scoring result of the damper are determined respectively. Therefore, the problems that a damper health monitoring method in the related technology is difficult to adapt to complex working conditions, the health state of the damper cannot be monitored in real time, and the real-time performance and accuracy of damper health monitoring are reduced are solved.

Description

Technical Field

[0001] This application relates to the technical field of vehicle suspension parameter identification and health monitoring, and particularly relates to a method and device for monitoring the load and damper health status of a semi-active structure system. Background Art

[0002] The vehicle suspension system plays a key role in filtering road excitations, improving ride comfort, handling stability, and driving safety. In the suspension system, the damping adjustable semi-active suspension with less energy consumption and higher stability shows great application prospects. The key component that plays the damping adjustment role in the semi-active suspension system is the damping adjustable damper, and typical devices such as magnetorheological dampers and solenoid valve dampers. With the increase of service time, the aging and wear of the damper will lead to a decrease in the output damping of the damper, and when the damping drops to a certain extent, it will seriously affect the vibration reduction performance of the semi-active suspension.

[0003] In the related art, an accurate mathematical model is established to simulate the behavior of the damper, and potential faults or performance degradation are detected by comparing the difference between the actual output and the model prediction results, or data collected from sensors, such as vibration signals and displacement changes, are analyzed by machine learning algorithms to identify abnormal patterns or trends, so as to monitor the health status of the damper.

[0004] However, the damper health monitoring methods in the related art are difficult to adapt to complex working conditions, require a large amount of high-quality data and have poor interpretability, cannot monitor the health status of the damper in real time, reduce the real-time performance and accuracy of damper health monitoring, and urgently need to be solved. Summary of the Invention

[0005] This application provides a method and device for monitoring the load and damper health status of a semi-active structure system to solve the problems that the damper health monitoring methods in the related art are difficult to adapt to complex working conditions, require a large amount of high-quality data and have poor interpretability, cannot monitor the health status of the damper in real time, and reduce the real-time performance and accuracy of damper health monitoring.

[0006] The first aspect embodiment of the present application provides a method for monitoring the load and damper health status of a semi-active structure system, including the following steps: When the vehicle is in the target monitoring working condition, based on the pre-set semi-active suspension damping value, collect the first target measurement value of the first target sensor at the wheel center of the vehicle and the second target measurement value of the second target sensor at the vehicle body center; respectively perform data processing on the first target measurement value and the second target measurement value to obtain a first observation vector corresponding to the first target measurement value and a second observation vector corresponding to the second target measurement value, and based on the pre-constructed discrete-time model, use the target batch least squares method to offline analyze the first observation vector and the second observation vector to identify the sprung mass and vehicle damping value of the vehicle; based on the sprung mass, combine the pre-determined target vehicle body parameter information to determine the load of the semi-active structure system of the vehicle, and use the target historical damper state detection data and the vehicle damping value to generate a health status score result of the semi-active structure system damper of the vehicle.

[0007] Optionally, in an embodiment of the present application, the generating the health status score result of the semi-active structure system damper of the vehicle includes: comparing the target historical damper state detection data with the identified vehicle damping value to generate a comparison result; based on the comparison result, using the vehicle damping value and the pre-set allowable working damping value to generate the health status score result of the semi-active structure system damper.

[0008] Optionally, in an embodiment of the present application, after generating the health status score result of the semi-active structure system damper of the vehicle, it further includes: determining whether the health status score of the semi-active structure system damper is greater than or equal to a pre-set score; if the health status score is greater than or equal to the pre-set score, generate a health score report of the semi-active structure system damper and send the health score report to a pre-set terminal; if the health status score is less than the pre-set score, generate a replacement suggestion report of the semi-active structure system damper and send the replacement suggestion report to the pre-set terminal.

[0009] Optionally, in an embodiment of the present application, the calculation formulas for the sprung mass and vehicle damping value of the vehicle are as follows:

[0010]

[0011] where, m s is the sprung mass of the vehicle, k s is the spring in the semi-active suspension system, and are both parameters, c is the vehicle damping value, and f0 is the sampling frequency of the sensor.

[0012] In the second aspect of the embodiments of the present application, a device for monitoring the load and damper health status of a semi-active structure system of a vehicle is provided, including: an acquisition module, configured to collect a first target measurement value of a first target sensor at the wheel center of the vehicle and a second target measurement value of a second target sensor at the vehicle body center based on a preset semi-active suspension damping value when the vehicle is in a target monitoring working condition; a processing module, configured to perform data processing on the first target measurement value and the second target measurement value respectively to obtain a first observation vector corresponding to the first target measurement value and a second observation vector corresponding to the second target measurement value, and based on a pre-constructed discrete-time model, offline analyze the first observation vector and the second observation vector by using a target batch least squares method to identify the sprung mass and vehicle damping value of the vehicle; a generation module, configured to determine the load of the semi-active structure system of the vehicle based on the sprung mass and in combination with pre-determined target vehicle body parameter information, and generate a health status scoring result of the damper of the semi-active structure system of the vehicle by using target historical damper state detection data and the vehicle damping value.

[0013] Optionally, in an embodiment of the present application, the generation module includes: a comparison unit, configured to compare target historical damper state detection data with the identified vehicle damping value to generate a comparison result; a generation unit, configured to generate a health status scoring result of the damper of the semi-active structure system based on the comparison result by using the vehicle damping value and a preset allowable working damping value.

[0014] Optionally, in an embodiment of the present application, the device of the embodiments of the present application further includes: a judgment module, configured to judge whether the health status score of the damper of the semi-active structure system is greater than or equal to a preset score after generating the health status scoring result of the damper of the semi-active structure system of the vehicle; a first processing module, configured to generate a health score report of the damper of the semi-active structure system and send the health score report to a preset terminal if the health status score is greater than or equal to the preset score after generating the health status scoring result of the damper of the semi-active structure system of the vehicle; a second processing module, configured to generate a replacement suggestion report of the damper of the semi-active structure system and send the replacement suggestion report to the preset terminal if the health status score is less than the preset score after generating the health status scoring result of the damper of the semi-active structure system of the vehicle.

[0015] Optionally, in an embodiment of the present application, the calculation formulas for the sprung mass and vehicle damping value of the vehicle are as follows:

[0016]

[0017] wherein, m s is the sprung mass of the vehicle, k s is the spring in the semi-active suspension system, and are both parameters, c is the damping value of the vehicle, and f0 is the sampling frequency of the sensor.

[0018] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the program to implement the method for monitoring the load and damper health status of the semi-active structure system as described in the above embodiments.

[0019] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium storing a computer program, and when the program is executed by a processor, the method for monitoring the load and damper health status of the semi-active structure system as described above is implemented.

[0020] An embodiment of the fifth aspect of the present application provides a computer program product including a computer program, and when the computer program is executed, it is used to implement the method for monitoring the load and damper health status of the semi-active structure system as described above.

[0021] In the embodiments of the present application, when the vehicle is in the target monitoring working condition, based on the pre-set semi-active suspension damping value, the first target measurement value of the first target sensor at the wheel center of the vehicle and the second target measurement value of the second target sensor at the vehicle body center collected are respectively subjected to data processing to obtain a first observation vector and a second observation vector, and then off-line analysis is performed using the target batch least squares method to identify the sprung mass and vehicle damping value of the vehicle. Thus, based on the sprung mass and vehicle damping value, the load of the semi-active structure system of the vehicle and the health status score result of the damper can be respectively determined, effectively improving the real-time performance and accuracy of damper health monitoring. Thereby, the problems in the related art that the damper health monitoring method is difficult to adapt to complex working conditions, requires a large amount of high-quality data and has poor interpretability, cannot monitor the health status of the damper in real time, and reduces the real-time performance and accuracy of damper health monitoring are solved.

[0022] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. Description of the Drawings

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0024] Figure 1Flow chart of a method for monitoring the load and damper health status of a semi - active structure system provided according to an embodiment of the present application;

[0025] Figure 2 Schematic diagram of a two - degree - of - freedom suspension model of a typical semi - active suspension;

[0026] Figure 3 Schematic diagram of the layout model for monitoring the load and damper health status of a semi - active structure system in a specific embodiment of the present application;

[0027] Figure 4 Schematic diagram for monitoring and scoring the damper health status using historical monitoring data in a specific embodiment of the present application;

[0028] Figure 5 Schematic diagram of the random road surface input time - history curves of grades B, C, and D in a specific embodiment of the present application;

[0029] Figure 6 Flow chart of a method for monitoring the load and damper health status of a semi - active structure system in a specific embodiment of the present application;

[0030] Figure 7 Schematic diagram of the structure of a device for monitoring the load and damper health status of a semi - active structure system provided according to an embodiment of the present application;

[0031] Figure 8 Schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Detailed implementation manners

[0032] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described by referring to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0033] The following describes a method and device for monitoring the load and damper health status of a semi-active structure system according to an embodiment of the present application with reference to the accompanying drawings. In view of the problems in the related art mentioned in the above background technology that the damper health monitoring method is difficult to adapt to complex working conditions, requires a large amount of high-quality data and has poor interpretability, and cannot monitor the health status of the damper in real time, reducing the real-time performance and accuracy of damper health monitoring, etc., the present application provides a method for monitoring the load and damper health status of a semi-active structure system. In this method, when the vehicle is in the target monitoring working condition, based on the pre-set semi-active suspension damping value, the first target measurement value of the first target sensor at the wheel center of the vehicle and the second target measurement value of the second target sensor at the vehicle body center collected are respectively processed to obtain a first observation vector and a second observation vector, and then the target batch least squares method is used for offline analysis to identify the sprung mass and vehicle damping value of the vehicle, so that based on the sprung mass and vehicle damping value, the load of the semi-active structure system of the vehicle and the health status score result of the damper can be respectively determined, effectively improving the real-time performance and accuracy of damper health monitoring. Thus, the problems in the related art that the damper health monitoring method is difficult to adapt to complex working conditions, cannot monitor the health status of the damper in real time, and reduces the real-time performance and accuracy of damper health monitoring are solved.

[0034] Specifically, Figure 1 FIG. is a schematic flow chart of a method for monitoring the load and damper health status of a semi-active structure system provided by an embodiment of the present application.

[0035] As Figure 1 shown, the method for monitoring the load and damper health status of the semi-active structure system includes the following steps:

[0036] In step S101, when the vehicle is in the target monitoring working condition, based on the pre-set semi-active suspension damping value, the first target measurement value of the first target degree sensor at the wheel center of the vehicle and the second target measurement value of the second target sensor at the vehicle body center are collected.

[0037] In the embodiment of the present application, the target monitoring working condition is the working condition when the semi-active structure system load and damper health status monitoring is started during vehicle operation; the first target sensor and the second target sensor can be of types such as acceleration sensors, displacement sensors or speed sensors, etc., and can be specifically set by relevant technical personnel and will not be specifically limited here.

[0038] It can be understood that in the embodiments of the present application, when the vehicle is in the target monitoring working condition, based on the pre-set semi-active suspension damping value, that is, setting the semi-active suspension damping as the damping value to be monitored, and keeping the damping unchanged during data collection in the following steps. Then, the first target measurement value of the first target sensor at the wheel center of the vehicle and the second target measurement value of the second target sensor at the vehicle body center can be collected, so as to accurately reflect the dynamic response characteristics of the current suspension system and provide a reliable data basis for the health state assessment of the damper.

[0039] It should be noted that the two target sensors in the embodiments of the present application are respectively arranged on the upper part and the lower part of the suspension. Among them, the lower part of the suspension includes positions such as the wheels, and the upper part of the suspension includes positions such as the vehicle body center.

[0040] For example, as Figure 2 shown, it is a schematic diagram of a suspension model of a specific embodiment of the present application. Among them, for typical four-wheel vehicles and two-wheel vehicles, on the premise of certain simplification operations, they can both be simplified into a two-degree-of-freedom suspension model, which is also a classic model for suspension performance evaluation. The two-degree-of-freedom suspension model includes m s as the sprung mass 10, m t as the unsprung mass 20, k t as the equivalent stiffness 30 of the tire, k s as the spring 40 in the suspension system, c0 as the non-adjustable damper 50, and c a as the adjustable damper 60.

[0041] Among them, the sprung mass 10 includes the mass of the vehicle body, personnel, or items above the suspension, the unsprung mass 20 includes the mass of the wheels and other connecting components below the suspension, the equivalent stiffness 30 of the tire refers to the equivalent spring effect between the tire and the road surface, the spring 40 and the non-adjustable damper 50 in the suspension system are fixed suspension parameters, and the adjustable damper 60 is the semi-active suspension damping adjustment part.

[0042] As Figure 3 shown, two sensors can be arranged in the embodiments of the present application. Among them, in the present application, an acceleration sensor is taken as an example for illustration. Therefore, in the embodiments of the present application, a vertical acceleration sensor 70 needs to be arranged at the tire center 20 to measure the acceleration signal and a vertical acceleration sensor 80 needs to be arranged at the vehicle body center 10 to measure the acceleration signal Then, during the vehicle operation, data is collected for a certain period of time. For example, during the vehicle operation, the monitoring algorithm is started. At this time, the semi-active suspension damping is set to the state to be monitored (e.g., maximum damping or minimum damping), and the damping is kept unchanged for a period of time. During this period, the measurement values of two sensors are collected. The sampling frequency of the sensors is set to f0, and the data collection time is T. Then the total number of collected points for a single sensor is N = f0T, which can provide a reliable data basis for the evaluation of the vehicle body load and the health status of the damper.

[0043] In step S102, the first target measurement value and the second target measurement value are respectively processed to obtain the first observation vector corresponding to the first target measurement value and the second observation vector corresponding to the second target measurement value. Based on the pre-constructed discrete-time model, the first observation vector and the second observation vector are analyzed offline using the target batch least squares method to identify the sprung mass of the vehicle and the vehicle damping value.

[0044] It can be understood that in the embodiments of the present application, the first target measurement value and the second target measurement value can be respectively processed. For example, filtering processing, integration processing, etc. can be performed to obtain the first observation vector Z corresponding to the first target measurement value s,N and the second observation vector Φ corresponding to the second target measurement value N , where the observation vector can reflect the vertical displacement data of the suspension vibration. Then, the first observation vector Z s,N and the second observation vector Φ N can be analyzed offline using the target batch least squares method to identify the sprung mass m s of the vehicle and the vehicle damping value c, so that the mass and damping parameters in the system dynamic characteristics can be more accurately separated, and the accuracy and reliability of the identification of the sprung mass and the vehicle damping value can be improved.

[0045] In the actual execution process, in the embodiments of the present application, a discrete state model of the semi-active suspension system can be constructed using the difference method. Through the discrete state model of the semi-active suspension system, the first observation vector Z s,N and the second observation vector Φ N are analyzed offline using the matrix parameters and optimization solution steps of the batch least squares method to identify the sprung mass m s of the vehicle and the vehicle damping value c. Therefore, in the embodiments of the present application, offline analysis can be performed after data collection, and various data enhancement methods can be used to perform noise reduction processing on the collected data, thereby improving the accuracy of parameter identification and the stability of the method implementation.

[0046] For example, in combination with Figure 2 and Figure 3As shown, before identifying parameters using the batch least squares method in the embodiments of the present application, it is first necessary to construct a discrete-time model of the semi-active suspension system. For the semi-active suspension system in Figure 2 and Figure 3 , the following system motion equation can be obtained:

[0047]

[0048] Wherein, is the measured acceleration signal at the tire center, is the measured acceleration signal at the vehicle body center, c0 is the non-adjustable damping, k s is the spring in the suspension system, k t is the equivalent stiffness of the tire, m t is the unsprung mass, m s is the sprung mass, is the vertical vibration velocity of the vehicle body center, is the vertical vibration velocity of the wheel center, z s (t) is the vertical displacement of the vehicle body center, z t (t) is the vertical displacement of the wheel center, β is the control bandwidth of the semi-active system, representing the speed of system damping adjustment; c in is the desired damping value, c a is the actually acting damping value, and there is an adjustment lag between the two, which is represented by Equation (3).

[0049] During the monitoring process, set the damping c1 of the semi-active damper to c a (t) as the target monitoring value c1 and keep it constant. Taking the solenoid valve shock absorber as an example, this operation is to keep the position of the solenoid valve unchanged, thereby keeping the damping value constant. At this time, the total system damping c = c0 + c a (t) is a constant value.

[0050] When the continuous signal z(t) is sampled at the sampling frequency f0 (time step ΔT = 1 / f0), the first-order derivative and second-order derivative of the continuous signal can be estimated using the central difference method as follows:

[0051]

[0052] At this time, using the relationship between Equations (4) and (5), the continuous system differential equation of Equation (2) can be transformed into the following discrete system difference equation:

[0053] A s (z -1 )Z s (k) = A t (z -1 )Z t (k) + ζ(k) (6)

[0054] Among them, introduce the notations which respectively represent the vertical displacement at the center of the car body and the vertical displacement at the center of the wheel at the moment of kΔT; ζ(k) represents the measurement noise at kΔT; among them, the backward shift operator z -1 is defined as:

[0055] z -1 Z(k) = Z(k - 1) (7)

[0056] In equation (6), A s (z -1 ) and A t (z -1 ) are respectively the backward shift operator polynomials:

[0057]

[0058] where f0 is the sampling frequency.

[0059] In equations (8) and (9), the sprung mass m s and the total system damping c are unknown parameters that need to be identified, and the other parameters are known. In equation (6), the displacement signals Z s (k) and Z t (k) can be obtained by filtering and integrating the acceleration signals and . Since the operations involved are offline rather than online, there are already many mature processing methods. Construct the data vector (known observed data) and the parameter vector θ (including the identified parameters):

[0060]

[0061] Then equation (6) can be expressed as:

[0062]

[0063] where the constant matrix H is:

[0064]

[0065] Based on the data of the previously measured N observation points, set the estimated value of the unknown parameter vector θ as At this time, for the i-th observation point, the estimation error is:

[0066]

[0067] Construct the error vector ε N (unknown), the noise vector ζ N (unknown) and the observation vector Zs,N and Φ N (known) as follows:

[0068]

[0069] After completing the above preparatory work, the batch least squares method in step two can be implemented to obtain the sprung mass and the vehicle damping value. At this time, equation (14) can be expressed as:

[0070]

[0071] Furthermore, the above parameter identification problem can be transformed into the following batch least squares problem:

[0072]

[0073] where B = [1 0 0].

[0074] For the problem of equation (17), the parameters can be calculated using the batch least squares algorithm At this time, the sprung mass m s and the total system damping c can be calculated as follows:

[0075]

[0076] where m s is the sprung mass of the vehicle, k s is the spring in the semi-active suspension system, and are both parameters, c is the vehicle damping value, and f0 is the sampling frequency.

[0077] In step S103, based on the sprung mass, the load of the vehicle's semi-active structural system is determined in combination with the pre-determined target vehicle body parameter information, and the health state scoring result of the damper of the vehicle's semi-active structural system is generated using the target historical damper state detection data and the vehicle damping value.

[0078] It can be understood that the embodiments of the present application can determine the load of the vehicle's semi-active structural system based on the sprung mass in combination with the pre-determined target vehicle body parameter information. For example, the structure, size, and mass parameters of the vehicle body itself can be pre-determined in the laboratory or at the factory, and then the sprung mass when the vehicle is unloaded can be determined and set as m s0 , then the load of the vehicle at this time is Δm = m s -m s0 , and then the determination of the load is completed. Determining the vehicle body load can be an important parameter for the design of the semi-active control algorithm, reducing uncertainty and improving the performance of the algorithm.

[0079] Furthermore, as Figure 4As shown, if the start-up monitoring is set within a period of time after each start-up of the vehicle, a series of damping history data can be obtained. If the performance of the damper does not age, the total system damping in the current damping state should remain unchanged. However, in reality, due to the aging effects such as damper wear, the output damping of the damper is lower than the expected value, resulting in a continuous decrease in the total system damping. Therefore, the embodiments of the present application can combine the determined load of the semi-active structure system of the vehicle, the historical damper state detection data, and the identified damping value of the vehicle in the above steps to generate a health state score result of the damper of the semi-active structure system of the vehicle. For example, when the score is 90, it indicates a relatively high health level of the damper, and when it is lower than 60, it indicates a relatively low health level of the damper, effectively improving the real-time performance and accuracy of damper health monitoring.

[0080] Among them, in an embodiment of the present application, generating a health state score result of the damper of the semi-active structure system of the vehicle includes: comparing the target historical damper state detection data with the identified damping value of the vehicle to generate a comparison result; based on the comparison result, using the damping value of the vehicle and a preset allowable working damping value to generate a health state score result of the damper of the semi-active structure system.

[0081] In some embodiments, as Figure 4 shown, the embodiments of the present application can compare the historical damper state detection data with the identified damping value of the vehicle to generate a comparison result. Then, based on the comparison result, using the damping value of the vehicle and the allowable working damping value to generate a health state score result of the damper of the semi-active structure system. For example, it is set that the total system damping obtained by the system's first monitoring and identification formula (19) is c1, then the allowable working damping c of the system p can be set with reference to this damping coefficient. Since the system damping during the first operation can be considered as the state of a brand-new damper, for example, it is set as c p = αc1, where the proportionality coefficient 0 < α < 1 is set according to requirements. The larger the setting, the more stringent the performance requirements for the damper. At this time, the health state score Z of the damper score can be given as follows:

[0082]

[0083] It can be seen from formula (20) that when the system is brand-new, that is, c = c1, the health state of the damper is 100 points. When the damper ages and reaches the limit value of the allowable working damping, the health state of the damper is 60 points, in a critical state; when the damper ages and is lower than the limit value of the allowable working damping, the health state of the damper is lower than 60 points.

[0084] Among them, the allowable working damping c of the damper p can be set as a certain percentage value of the damping monitoring value c1 of the brand-new damper according to requirements.

[0085] Optionally, in one embodiment of the present application, after generating the health status score result of the semi-active structural system damper of the vehicle, it further includes: determining whether the health status score of the semi-active structural system damper is greater than or equal to a preset score; if the health status score is greater than or equal to the preset score, generating a health score report of the semi-active structural system damper and sending the health score report to a preset terminal; if the health status score is less than the preset score, generating a replacement suggestion report of the semi-active structural system damper and sending the replacement suggestion report to the preset terminal.

[0086] In some embodiments, the embodiment of the present application can determine whether the health status score of the semi-active structural system damper is greater than or equal to 60 points. When the health status score is greater than or equal to 60 points, a health score report of the semi-active structural system damper is generated. For example, when the current health status score of the semi-active structural system damper is 90 points, the health status is excellent, and a health score report without the need to replace the damper is generated and sent to the computer terminal of the relevant technical personnel; when the health status score is less than 60 points, a replacement suggestion report of the semi-active structural system damper is generated. For example, when the current health status score of the semi-active structural system damper is 58 points, the health status is poor, and a suggestion report for replacing the damper is generated and sent to the computer terminal of the relevant technical personnel. Thus, the embodiment of the present application can generate corresponding health reports or replacement suggestions in time before the damping performance drops to affect the suspension effect, improving the initiative, accuracy, and safety of vehicle maintenance.

[0087] For example, referring to an actual two-wheeler, the calculation parameters are selected: m s = 180 kg, m t = 15 kg, k s = 20 kN / m, k t = 100 kN / m, the adjustable damping coefficient c a ∈[400 N / (m / s), 2700 N / (m / s)], the non-adjustable damping coefficient c0 = 0 N / (m / s), the sensor sampling frequency is 100 Hz, the controller execution frequency is 100 Hz, and the system bandwidth β = 100.

[0088] As Figure 5 shown, the monitoring algorithm was tested on roads of grades B, C, and D respectively. Taking the results of road C as an example, the identified sprung mass was 181.5 kg, and the error from the true value of 180 kg was less than 5%, indicating the effectiveness of the embodiment of the present application. At the same time, the sprung mass of the vehicle when unloaded is m s0 = 110 kg, and it can be further known that the vehicle load Δm = m s - m s0= 71.5 kg; Set the damper monitoring status to the maximum damping value. The first monitored damping is c1 = 2700 N / (m / s), and the allowable working damping c p = 60% × c1 = 1620 N / (m / s). If the damping value of the damper drops to c = 2000 N / (m / s), at this time, the identified c = 2030.6 N / (m / s), and the error is less than 5%. At the same time, the health status score is Z score = 75.2 > 60, indicating that there is no need to replace the damper at this time, but at the same time, note that the damper is not very healthy.

[0089] For example, as Figure 6 shown, the working principle of the embodiment of the present application will be described in detail below with a specific embodiment.

[0090] Step S601: During vehicle operation, when starting monitoring, set the semi-active suspension damping to the damping value to be monitored, and keep this damping unchanged during data acquisition. At the same time, measure the values of two sensors arranged at the center of the vehicle body and the center of the tire during this period, and form an observation vector after signal processing.

[0091] Step S602: Use the batch least squares method to offline analyze the observation vector to identify the sprung mass and the vehicle damping value.

[0092] Step S603: Use the identified sprung mass to estimate the vehicle load condition in combination with the pre-determined vehicle body parameter information. At the same time, refer to the historical damper status monitoring data, judge the health status of the current damper, and give suggestions on whether to replace the suspension damper or the health status score of the damper.

[0093] According to the method for monitoring the load and the health status of the damper of the semi-active structure system proposed in the embodiment of the present application, when the vehicle is in the target monitoring working condition, based on the pre-set semi-active suspension damping value, the first target measurement value of the first target sensor at the wheel center of the vehicle and the second target measurement value of the second target sensor at the vehicle body center collected respectively are processed to obtain the first observation vector and the second observation vector, and then the target batch least squares method is used for offline analysis to identify the sprung mass and the vehicle damping value of the vehicle. Thus, based on the sprung mass and the vehicle damping value, the load of the semi-active structure system of the vehicle and the health status score result of the damper can be determined respectively, effectively improving the real-time performance and accuracy of the damper health monitoring. Therefore, the problems in the related art that the damper health monitoring method is difficult to adapt to complex working conditions, and the health status of the damper cannot be monitored in real time, reducing the real-time performance and accuracy of the damper health monitoring are solved.

[0094] Secondly, a device for monitoring the load and the health status of the damper of the semi-active structure system proposed in the embodiment of the present application will be described with reference to the accompanying drawings.

[0095] Figure 7 It is a block diagram of a device for monitoring the load and damper health status of a semi - active structure system according to an embodiment of the present application.

[0096] As Figure 7 shown, the device 10 for monitoring the load and damper health status of the semi - active structure system includes: a collection module 100, a processing module 200, and a generation module 300.

[0097] Specifically, the collection module 100 is configured to collect a first target measurement value of a first target sensor at the wheel center of the vehicle and a second target measurement value of a second target sensor at the vehicle body center based on a preset semi - active suspension damping value when the vehicle is in a target monitoring working condition.

[0098] The processing module 200 is configured to perform data processing on the first target measurement value and the second target measurement value respectively to obtain a first observation vector corresponding to the first target measurement value and a second observation vector corresponding to the second target measurement value, and offline analyze the first observation vector and the second observation vector by using the target batch least - squares method based on a pre - constructed discrete - time model to identify the sprung mass of the vehicle and the vehicle damping value.

[0099] The generation module 300 is configured to determine the load of the semi - active structure system of the vehicle based on the sprung mass in combination with pre - determined target vehicle body parameter information, and generate a health status score result of the semi - active structure system damper of the vehicle by using target historical damper state detection data and the vehicle damping value.

[0100] Optionally, in an embodiment of the present application, the generation module 300 includes: a comparison unit and a generation unit.

[0101] Among them, the comparison unit is configured to compare the target historical damper state detection data with the identified vehicle damping value to generate a comparison result.

[0102] The generation unit is configured to generate a health status score result of the semi - active structure system damper based on the comparison result by using the vehicle damping value and a preset allowable working damping value.

[0103] Optionally, in an embodiment of the present application, the device 10 of the embodiment of the present application further includes: a judgment module, a first processing module, and a second processing module.

[0104] Among them, the judgment module is configured to judge whether the health status score of the semi - active structure system damper is greater than or equal to a preset score after generating the health status score result of the semi - active structure system damper of the vehicle.

[0105] The first processing module is used to generate a health score report for the semi-active structural system damper of the vehicle and send the health score report to a preset terminal if the health status score is greater than or equal to a preset score after generating the health status score result of the semi-active structural system damper of the vehicle.

[0106] The second processing module is used to generate a replacement suggestion report for the semi-active structural system damper and send the replacement suggestion report to a preset terminal if the health status score is less than the preset score after generating the health status score result of the semi-active structural system damper of the vehicle.

[0107] Optionally, in an embodiment of the present application, the calculation formulas for the sprung mass of the vehicle and the vehicle damping value are as follows:

[0108]

[0109] where m s is the sprung mass of the vehicle, k s is the spring in the semi-active suspension system, and are both parameters, c is the vehicle damping value, and f0 is the sampling frequency of the sensor.

[0110] It should be noted that the foregoing explanation of the embodiments of the method for monitoring the load and damper health status of the semi-active structural system also applies to the device for monitoring the load and damper health status of the semi-active structural system in this embodiment, and will not be elaborated here.

[0111] The device for monitoring the load and damper health status of the semi-active structural system proposed according to the embodiments of the present application can, when the vehicle is in the target monitoring working condition, respectively process the first target measurement value of the first target sensor at the wheel center of the vehicle and the second target measurement value of the second target sensor at the vehicle body center collected based on the preset semi-active suspension damping value to obtain a first observation vector and a second observation vector, and then use the target batch least squares method for offline analysis to identify the sprung mass of the vehicle and the vehicle damping value, so that the health status score results of the load and damper of the semi-active structural system of the vehicle can be determined respectively based on the sprung mass and the vehicle damping value, effectively improving the real-time performance and accuracy of damper health monitoring. Thus, the problems in the related art that the damper health monitoring method is difficult to adapt to complex working conditions, cannot monitor the health status of the damper in real time, and reduces the real-time performance and accuracy of damper health monitoring are solved.

[0112] Figure 8 The structural schematic diagram of the electronic device provided by the embodiment of the present application. The electronic device may include:

[0113] A memory 801, a processor 802, and a computer program stored on the memory 801 and executable on the processor 802.

[0114] When the processor 802 executes the program, it implements the semi-active structure system load and damper health state monitoring method provided in the above embodiments.

[0115] Furthermore, the electronic device further includes:

[0116] A communication interface 803 for communication between the memory 801 and the processor 802.

[0117] The memory 801 is used to store a computer program executable on the processor 802.

[0118] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0119] If the memory 801, the processor 802, and the communication interface 803 are implemented independently, the communication interface 803, the memory 801, and the processor 802 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0120] Optionally, in a specific implementation, if the memory 801, the processor 802, and the communication interface 803 are integrated on a chip, the memory 801, the processor 802, and the communication interface 803 can communicate with each other through an internal interface.

[0121] The processor 802 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0122] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the above semi-active structural system load and damper health status monitoring method.

[0123] This embodiment also provides a computer program product, including a computer program, which is used to implement the above semi-active structural system load and damper health status monitoring method when the computer program is executed.

[0124] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0125] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0126] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or N executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0128] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0129] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0130] In addition, each functional unit in various embodiments of the present application may be integrated into a processing module, may exist separately as individual physical units, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0131] The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present application.

Claims

1. A method for monitoring the load and damper health status of a semi-active structural system, characterized in that Including the following steps: When the vehicle is in the target monitoring working condition, based on the preset semi-active suspension damping value, collect the first target measurement value of the first target sensor at the wheel center of the vehicle and the second target measurement value of the second target sensor at the vehicle body center; Perform data processing on the first target measurement value and the second target measurement value respectively to obtain the first observation vector corresponding to the first target measurement value and the second observation vector corresponding to the second target measurement value, and based on the pre-constructed discrete-time model, use the target batch least squares method to offline analyze the first observation vector and the second observation vector to identify the sprung mass of the vehicle and the vehicle damping value; Based on the sprung mass, determine the load of the semi-active structure system of the vehicle in combination with the pre-determined target vehicle body parameter information, and use the target historical damper state detection data and the vehicle damping value to generate the health status scoring result of the semi-active structure system damper of the vehicle.

2. The method according to claim 1, characterized in that, The generating the health status scoring result of the semi-active structure system damper of the vehicle includes: Compare the target historical damper state detection data with the identified vehicle damping value to generate a comparison result; Based on the comparison result, use the vehicle damping value and the preset allowable working damping value to generate the health status scoring result of the semi-active structure system damper.

3. The method according to claim 1, wherein After generating the health status scoring result of the semi-active structure system damper of the vehicle, it further includes: Judge whether the health status score of the semi-active structure system damper is greater than or equal to the preset score; If the health status score is greater than or equal to the preset score, generate the health score report of the semi-active structure system damper and send the health score report to the preset terminal; If the health status score is less than the preset score, generate the replacement suggestion report of the semi-active structure system damper and send the replacement suggestion report to the preset terminal.

4. The method according to claim 1, wherein The calculation formulas for the sprung mass of the vehicle and the vehicle damping value are as follows: where m s is the sprung mass of the vehicle, k s is the spring in the semi-active suspension system, and are both parameters, c is the damping value of the vehicle, and f0 is the sampling frequency of the sensor.

5. A semi-active structural system load and damper health monitoring device, characterized in that, Including: A collection module, used to collect the first target measurement value of the first target sensor at the wheel center of the vehicle and the second target measurement value of the second target sensor at the vehicle body center based on the preset semi-active suspension damping value when the vehicle is in the target monitoring working condition; A processing module, used to perform data processing on the first target measurement value and the second target measurement value respectively to obtain the first observation vector corresponding to the first target measurement value and the second observation vector corresponding to the second target measurement value, and based on the pre-constructed discrete-time model, use the target batch least squares method to offline analyze the first observation vector and the second observation vector to identify the sprung mass of the vehicle and the vehicle damping value; A generating module, used to determine the load of the semi-active structure system of the vehicle based on the sprung mass, in combination with the pre-determined target vehicle body parameter information, and use the target historical damper state detection data and the vehicle damping value to generate the health status scoring result of the semi-active structure system damper of the vehicle.

6. The device according to claim 5, wherein The generation module includes: A comparison unit for comparing the target historical damper state detection data with the identified vehicle damping value to generate a comparison result; A generation unit for generating a health status score result of the semi-active structural system damper based on the comparison result by using the vehicle damping value and a preset allowable working damping value.

7. The device according to claim 5, characterized in that, It further includes: A judgment module for judging whether the health status score of the semi-active structural system damper is greater than or equal to a preset score after generating the health status score result of the semi-active structural system damper of the vehicle; A first processing module for generating a health score report of the semi-active structural system damper and sending the health score report to a preset terminal if the health status score is greater than or equal to the preset score after generating the health status score result of the semi-active structural system damper of the vehicle; A second processing module for generating a replacement suggestion report of the semi-active structural system damper and sending the replacement suggestion report to the preset terminal if the health status score is less than the preset score after generating the health status score result of the semi-active structural system damper of the vehicle.

8. An electronic device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the semi-active structural system load and damper health status monitoring method according to any one of claims 1-4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used for implementing the semi-active structural system load and damper health status monitoring method according to any one of claims 1-4.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to be used for implementing the semi-active structural system load and damper health status monitoring method according to any one of claims 1-4.

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