Intelligent control system and method for fatigue test of automobile transmission shaft

Abstract

The invention discloses an intelligent control system and method for an automobile transmission shaft fatigue test, and relates to the technical field of transmission shaft fatigue testing. Calculating a rigidity abnormal coefficient, judging whether the transmission shaft is fatigued or not according to the rigidity abnormal coefficient, and if not, further calculating a fatigue trend coefficient to analyze whether the transmission shaft tends to be fatigued or not; and finally, according to the fatigue conclusion obtained through any analysis, early warning is given out in time. According to the invention, the rigidity data is collected in real time and the rigidity abnormal coefficient is calculated, so that early warning of the fatigue state of the transmission shaft is realized, and time and resource waste caused by thorough damage of a to-be-tested piece in a traditional test is avoided; by means of automatic comparison of the coefficient and a threshold value, fatigue judgment is more efficient, accurate and quantifiable; meanwhile, the fatigue trend coefficient is obtained by analyzing the rigidity abnormal change slope, the damage evolution trend can be pre-judged, the early warning opportunity can be advanced, and therefore the controllability of the test process and the overall test efficiency are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of drive shaft fatigue testing technology, specifically to an intelligent control system and method for testing the fatigue of automotive drive shafts. Background Technology

[0002] As a core transmission component between the powertrain and the drive axle, the automotive driveshaft is subjected to the rotational torque output by the engine and the alternating load of the road surface for a long time. It is extremely susceptible to fatigue damage due to cyclic stress. Fatigue failure may lead to driveshaft breakage, which directly affects driving safety and reliability. Therefore, conducting fatigue tests on the driveshaft by simulating the load spectrum under actual working conditions in the laboratory has become a key step in verifying product durability, optimizing design, and ensuring the quality of the entire vehicle.

[0003] However, existing fatigue tests cannot provide early warning of impending fatigue damage and must only be terminated after the drive shaft has completely broken. This process results in a significant waste of time and resources because it is impossible to stop worthless tests in advance. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent control system and method for fatigue testing of automotive drive shafts. This solves the problem that existing fatigue tests lack damage warning capabilities and can only end when the drive shaft completely breaks, resulting in low testing efficiency and high costs.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent control method for fatigue testing of automotive drive shafts, comprising the following specific steps: S1: Real-time acquisition of stiffness data and preprocessing; S2: Calculation of the preprocessed stiffness data to obtain a stiffness anomaly coefficient, and analysis of whether the drive shaft is fatigued based on the stiffness anomaly coefficient. If the analysis indicates that the drive shaft is fatigued, proceed to S4; if the analysis indicates that the drive shaft is not fatigued, proceed to S3; S3: Calculation of a fatigue trend coefficient based on the stiffness anomaly coefficient, and analysis of whether the drive shaft is trending towards fatigue based on the fatigue trend coefficient. If the analysis indicates that the drive shaft is trending towards fatigue, proceed to S4; if the analysis indicates that the drive shaft is not trending towards fatigue, return to S1; S4: Issue an alarm.

[0006] Furthermore, the stiffness anomaly coefficient is obtained as follows: a stiffness anomaly threshold is preset, and the difference between the real-time stiffness data value and the stiffness anomaly threshold is calculated to obtain the stiffness anomaly coefficient; ;in, Represents the stiffness anomaly coefficient. This represents the value of the real-time stiffness data. This represents the threshold for abnormal stiffness.

[0007] Furthermore, the method for analyzing whether the drive shaft is fatigued based on the stiffness anomaly coefficient is as follows: the stiffness anomaly coefficient is compared with zero. If the stiffness anomaly coefficient is greater than or equal to zero, it indicates that the drive shaft is fatigued; if the stiffness anomaly coefficient is less than zero, it indicates that the drive shaft is not fatigued.

[0008] Furthermore, the fatigue trend coefficient is obtained in the following way: under the time series, the slope of the change of stiffness anomaly coefficient is analyzed to obtain the fatigue trend coefficient.

[0009] Furthermore, the method for analyzing the slope of the change in stiffness anomaly coefficient is as follows: a two-dimensional coordinate system is established, with the horizontal axis of the coordinate system representing the time series and the vertical axis representing the stiffness anomaly coefficient. Coordinate points at different times are obtained and recorded as stiffness coordinate points. The slope of the stiffness coordinate point at the next time moment is calculated in turn with that of the stiffness coordinate point at the previous time moment. The stiffness slope is obtained in both cases, and the steepness trend of the stiffness slope is analyzed.

[0010] Furthermore, the method for obtaining the stiffness slope is as follows: ;in, Indicates the stiffness slope. The ordinate of the stiffness coordinate point at the next moment is represented by the following value. The ordinate of the stiffness coordinate point at the previous moment is represented by the ordinate. This represents the x-coordinate of the stiffness coordinate point at the next moment. This represents the x-coordinate of the stiffness coordinate point at the previous moment.

[0011] Furthermore, the method for analyzing the steepness trend of the stiffness slope is as follows: a preset detection time window and a fatigue trend coefficient are used, and the fatigue trend coefficient is initially assigned a value of zero. Within the detection time window, the next stiffness slope is compared with the previous stiffness slope. If the next stiffness slope is continuously less than the previous stiffness slope, the next stiffness slope within the detection time window is calculated with the previous stiffness slope, and the calculation result is assigned to the fatigue trend coefficient; otherwise, the comparison continues, and no value is assigned to the fatigue trend coefficient.

[0012] Furthermore, the method for calculating the next stiffness slope and the previous stiffness slope within the detection time window is as follows: within the detection time window, the difference between the next stiffness slope and the previous stiffness slope is calculated sequentially, and the results of the difference calculation are then summed.

[0013] Furthermore, the method for analyzing whether the drive shaft is approaching fatigue based on the fatigue trend coefficient is as follows: the fatigue trend coefficient is compared with zero. If the fatigue trend coefficient is greater than zero, it indicates that the drive shaft is approaching fatigue; if the fatigue trend coefficient is equal to zero, it indicates that the drive shaft is not approaching fatigue.

[0014] An intelligent control system for testing the fatigue of automotive driveshafts includes the following modules: a data acquisition module, a driveshaft fatigue analysis module, a fatigue trend analysis module, and an alarm module. The data acquisition module collects stiffness data in real time and performs preprocessing. The driveshaft fatigue analysis module calculates the preprocessed stiffness data to obtain a stiffness anomaly coefficient, analyzes whether the driveshaft is fatigued based on the stiffness anomaly coefficient, and if fatigue is detected, executes the alarm module; if no fatigue is detected, it executes the fatigue trend analysis module. The fatigue trend analysis module calculates a fatigue trend coefficient based on the stiffness anomaly coefficient, analyzes whether the driveshaft is trending towards fatigue based on the fatigue trend coefficient, and if fatigue is detected, executes the alarm module; if no fatigue is detected, it returns to the data acquisition module. The alarm module issues an alarm.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. By collecting stiffness data in real time and calculating the stiffness anomaly coefficient, the system can identify the fatigue state of the drive shaft before it completely breaks, thus issuing an early warning and effectively avoiding the waste of time and resources caused by waiting for the specimen to completely break in traditional tests.

[0016] 2. By adopting an automated comparison analysis based on stiffness anomaly coefficient and preset threshold, the traditional subjective judgment that relies on human experience or post-fracture observation is replaced, making the fatigue assessment process more efficient, accurate and quantifiable.

[0017] 3. By calculating the fatigue trend coefficient, the system can analyze the slope of the abnormal stiffness change, thereby determining whether the drive shaft is approaching fatigue. This prediction of the damage evolution trend allows the early warning time to be further advanced, enhancing the controllability of the test process.

[0018] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0019] Figure 1 This is a flowchart of the intelligent control method for fatigue testing of automotive drive shafts according to the present invention.

[0020] Figure 2 This is a structural diagram of the intelligent control system for testing the fatigue of automotive drive shafts according to the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0023] Example 1: like Figure 1 As shown, this embodiment of the invention provides an intelligent control method for fatigue testing of automotive drive shafts, including the following specific steps: S1: The torque and torsion angle are measured synchronously in real time by the torque sensor and the angle sensor, and the ratio of torque to torsion angle is calculated to obtain stiffness data. The stiffness data is then cleaned to remove redundant values ​​and improve the quality of stiffness data. S2: Calculate the stiffness data after data cleaning and processing to obtain the stiffness anomaly coefficient. Analyze whether the drive shaft is fatigued based on the stiffness anomaly coefficient. If the analysis shows that the drive shaft is fatigued, proceed to S4; if the analysis shows that the drive shaft is not fatigued, proceed to S3. S3: Calculate the fatigue trend coefficient based on the stiffness anomaly coefficient, and analyze whether the drive shaft is prone to fatigue based on the fatigue trend coefficient. If the analysis shows that the drive shaft is prone to fatigue, then execute S4; if the analysis shows that the drive shaft is not prone to fatigue, then return to S1. S4: Issue an alarm, record the fault log, and send a shutdown command to the test bench under the set conditions.

[0024] Example 2 differs from Example 1 in that: The stiffness anomaly coefficient is obtained as follows: The preset stiffness anomaly threshold is determined by conducting destructive tests on drive shaft samples of the same model and process until complete fracture during the development phase, recording the stiffness value of each sample at the last moment before fracture, taking the statistical average of these values ​​to determine the threshold, and calculating the difference between the real-time stiffness data value and the stiffness anomaly threshold to quantify the current safety margin and obtain the stiffness anomaly coefficient. ;in, This represents the stiffness anomaly coefficient, reflecting whether the drive shaft stiffness is abnormal. This represents the value of the real-time stiffness data. This represents the threshold for abnormal stiffness.

[0025] The method for analyzing whether a drive shaft is fatigued based on the stiffness anomaly coefficient is as follows: The stiffness anomaly coefficient is compared with zero. If the stiffness anomaly coefficient is greater than or equal to zero, it indicates that the drive shaft is fatigued, that is, the drive shaft structure has undergone functional fatigue. If the stiffness anomaly coefficient is less than zero, it indicates that the drive shaft is not fatigued.

[0026] The specific method for obtaining the fatigue trend coefficient is as follows: In the time series, the slope of the change in stiffness anomaly coefficient is analyzed to obtain the fatigue trend coefficient, that is, to focus on the change in the rate of safety margin consumption, aiming to capture the accelerated damage trend before failure.

[0027] The method for analyzing the slope of the change in stiffness anomaly coefficient is as follows: A two-dimensional coordinate system is established, with the horizontal axis representing the time series and the vertical axis representing the stiffness anomaly coefficient. Coordinate points at different times are obtained and denoted as stiffness coordinate points. The slopes of the stiffness coordinate points at the next and previous times are calculated sequentially to obtain the stiffness slopes, and the steepness trend of the stiffness slopes is analyzed. The continuous safety margin data is transformed into an instantaneous rate of change series to provide basic data for steepness trend analysis.

[0028] The method for obtaining the stiffness slope is as follows: ; in, The slope represents the stiffness, reflecting the accelerating trend of stiffness change. The ordinate of the stiffness coordinate point at the next moment is represented by the following value. The ordinate of the stiffness coordinate point at the previous moment is represented by the ordinate. This represents the x-coordinate of the stiffness coordinate point at the next moment. The x-coordinate of the stiffness coordinate point at the previous moment is represented; the average rate of change of the safety margin between adjacent time points is accurately calculated using the two-point method.

[0029] The method for analyzing the steepness trend of the stiffness slope is as follows: A preset detection time window and fatigue trend coefficient are set, with the initial value of the fatigue trend coefficient set to zero. Within the detection time window, the next stiffness slope is compared with the previous stiffness slope. If the next stiffness slope is continuously less than the previous stiffness slope, it indicates that the fatigue damage of the drive shaft is accelerating and the safety margin is being consumed faster and faster. In this case, the next stiffness slope within the detection time window is calculated with the previous stiffness slope, and the calculation result is assigned to the fatigue trend coefficient. Otherwise, the comparison continues, but the fatigue trend coefficient is not assigned a value.

[0030] The method for calculating the next stiffness slope and the previous stiffness slope within the detection time window is as follows: Within the detection time window, the difference between the next stiffness slope and the previous stiffness slope is calculated sequentially, and the results of the difference calculation are summed to accumulate and quantify the degree of continuous acceleration fatigue damage, thereby obtaining a comprehensive index of trend strength.

[0031] The method for analyzing whether a drive shaft is prone to fatigue based on the fatigue trend coefficient is as follows: The fatigue trend coefficient is compared with zero. If the fatigue trend coefficient is greater than zero, it indicates that the drive shaft is approaching fatigue. If the fatigue trend coefficient is equal to zero, it indicates that the drive shaft is not approaching fatigue.

[0032] Example 3: like Figure 2 As shown: An intelligent control system for testing the fatigue strength of automotive drive shafts includes the following specific modules: The data acquisition module is used to measure torque and torsion angle in real time synchronously using torque and angle sensors, and calculate the ratio of torque to torsion angle. The torque sensor should ideally be a strain gauge or phase difference sensor with good dynamic response and high accuracy, such as the 4500A series, and its range should cover the maximum test torque. The angle sensor can be a high-resolution photoelectric encoder or rotary transformer, such as the KUBLER series. Both sensors must be strictly synchronized for acquisition, and the sampling frequency is recommended to be no less than 1kHz to ensure that the dynamic changes in stiffness under alternating loads are captured, obtaining stiffness data. The stiffness data undergoes data cleaning processing: first, outlier removal is performed using a sliding window mid-range filter to eliminate abrupt jumps caused by signal interference; second, smoothing and noise reduction are performed using the Savitzky-Golay convolution smoothing algorithm to suppress high-frequency noise while preserving the trend; finally, the processed stiffness data is stored in a circular buffer according to timestamps for subsequent analysis modules to access. Drive shaft fatigue analysis module: This module calculates stiffness data from the cleaned data to obtain stiffness anomaly coefficients. Based on these coefficients, it analyzes whether the drive shaft is fatigued. If fatigue is detected, an alarm module is executed; otherwise, a fatigue trend analysis module is executed. Fatigue Trend Analysis Module: This module calculates the fatigue trend coefficient based on the stiffness anomaly coefficient. It then analyzes whether the drive shaft is trending towards fatigue based on the fatigue trend coefficient. If the analysis indicates that the drive shaft is trending towards fatigue, the alarm module is activated. If the analysis indicates that the drive shaft is not trending towards fatigue, the data acquisition module is returned. Alarm Module: At the hardware level, this module integrates a digital output interface and an Ethernet controller. At the software level, it is responsible for formatting alarm information and encapsulating it into standard Modbus TCP messages, sending them to the preset host computer IP address and port. It can also drive local indicator lights for status indication. To ensure communication reliability, this protocol has an acknowledgment and retransmission mechanism. Functionally, this module performs the following operations: Local log recording, writing key information such as alarm trigger time, trigger type (e.g., threshold trigger or trend trigger), current fatigue trend coefficient, and original stiffness data snapshot into non-volatile memory; Host computer communication, sending alarm messages to the host computer monitoring software via the Modbus TCP / IP industrial protocol, triggering interface highlighting, pop-up dialog boxes, and generating detailed reports; and sending digital output signals to the test bench control system according to preset strategies, instructing it to immediately execute a safety shutdown, thereby maximizing the protection of the test specimen and equipment and terminating invalid tests.

[0033] The preferred embodiments disclosed in this invention are merely illustrative examples of feasible implementation methods and are not intended to exhaust all technical details of the invention, nor do they constitute a limitation on the scope of protection of this invention. In practical applications, those skilled in the art can make appropriate adjustments, combinations, or substitutions to the methods or systems described in this embodiment based on specific production conditions, equipment configurations, and process requirements, without departing from the core concept of this invention, namely, achieving fatigue early warning through stiffness data monitoring. For example, the stiffness data acquisition method, such as using torque or angle sensors with different principles; the data processing algorithm, such as filtering algorithms or feature extraction methods; the control threshold, such as stiffness anomaly thresholds or trend coefficient thresholds; or the specific implementation form of the execution unit, such as alarm triggering mechanisms or communication protocols with the test bench, can all be reasonably changed and adapted according to the actual testing environment, accuracy requirements, or system integration needs.

[0034] Furthermore, the technical concepts disclosed in this invention have universal extensibility and adaptability. They are not only applicable to the specific scenarios described in the embodiments, but can also be applied in similar technical fields or related industrial processes through analogy, transplantation, or improvement. Any technical solution formed by making logically equivalent substitutions, reasonable adjustments to the order of steps, or recombination of module functions based on the principles, ideas, or framework disclosed in this specification should be considered to fall within the spirit and scope of this invention.

[0035] It should be further clarified that the specific descriptions and drawings in the patent documents are for the purpose of assisting in understanding the present invention only, and their details should not be interpreted as limitations on the claims. The true scope of protection of the present invention should be determined by the content of the claims recorded in the authorized text, and should cover all equivalent technical solutions that comply with the provisions of patent law under these claims. Any implementation method that has the same or similar function and achieves similar effects through reasonable changes in technical means under the guidance of the concept of the present invention falls within the scope of protection sought by the present invention.

[0036] Therefore, the descriptions in this specification are merely illustrative. Any adjustments to implementation methods, equivalent substitutions of technical features, or further applications based on the concept of this invention, as long as they do not depart from the overall technical approach described in this invention, should be included within the scope of protection of this invention. We encourage those skilled in the art to innovate and optimize based on their understanding of the core of this invention and in conjunction with specific practices, so as to jointly promote the progress and development of related technologies.

Claims

1. An intelligent control method for fatigue testing of automotive drive shafts, characterized in that: The specific steps include the following: S1: Real-time acquisition of stiffness data and preprocessing; S2: Calculate the preprocessed stiffness data to obtain the stiffness anomaly coefficient. Analyze whether the drive shaft is fatigued based on the stiffness anomaly coefficient. If the analysis shows that the drive shaft is fatigued, proceed to S4; if the analysis shows that the drive shaft is not fatigued, proceed to S3. S3: Calculate the fatigue trend coefficient based on the stiffness anomaly coefficient, and analyze whether the drive shaft is prone to fatigue based on the fatigue trend coefficient. If the analysis shows that the drive shaft is prone to fatigue, then execute S4; if the analysis shows that the drive shaft is not prone to fatigue, then return to S1. S4: Issue an alarm.

2. The intelligent control method for fatigue testing of automotive drive shafts according to claim 1, characterized in that: The stiffness anomaly coefficient is obtained as follows: A stiffness anomaly threshold is preset, and the difference between the real-time stiffness data value and the stiffness anomaly threshold is calculated to obtain the stiffness anomaly coefficient. ;in, Represents the stiffness anomaly coefficient. This represents the value of the real-time stiffness data. This represents the threshold for abnormal stiffness.

3. The intelligent control method for fatigue testing of automotive drive shafts according to claim 2, characterized in that: The method for analyzing whether the drive shaft is fatigued based on the stiffness anomaly coefficient is as follows: The stiffness anomaly coefficient is compared with zero. If the stiffness anomaly coefficient is greater than or equal to zero, it indicates that the drive shaft is fatigued; if the stiffness anomaly coefficient is less than zero, it indicates that the drive shaft is not fatigued.

4. The intelligent control method for fatigue testing of automotive drive shafts according to claim 3, characterized in that: The specific method for obtaining the fatigue trend coefficient is as follows: By analyzing the slope of the change in stiffness anomaly coefficient over time, fatigue trend coefficients are obtained.

5. The intelligent control method for fatigue testing of automotive drive shafts according to claim 4, characterized in that: The method for analyzing the slope of the change in the stiffness anomaly coefficient is as follows: A two-dimensional coordinate system is established, with the horizontal axis representing the time series and the vertical axis representing the stiffness anomaly coefficient. The coordinate points at different times are obtained and denoted as stiffness coordinate points. The slopes of the stiffness coordinate points at the next time moment and the previous time moment are calculated sequentially to obtain the stiffness slopes. The steepness trend of the stiffness slopes is then analyzed.

6. The intelligent control method for fatigue testing of automotive drive shafts according to claim 5, characterized in that: The method for obtaining the stiffness slope is as follows: ； in, Indicates the stiffness slope. The ordinate of the stiffness coordinate point at the next moment is represented by the following value. The ordinate of the stiffness coordinate point at the previous moment is represented by the ordinate. This represents the x-coordinate of the stiffness coordinate point at the next moment. This represents the x-coordinate of the stiffness coordinate point at the previous moment.

7. The intelligent control method for fatigue testing of automotive drive shafts according to claim 6, characterized in that: The method for analyzing the steepness trend of the stiffness slope is as follows: A preset detection time window and fatigue trend coefficient are set, with the initial value of the fatigue trend coefficient set to zero. Within the detection time window, the next stiffness slope is compared with the previous stiffness slope. If the next stiffness slope is continuously less than the previous stiffness slope, the next stiffness slope within the detection time window is calculated with the previous stiffness slope, and the calculation result is assigned to the fatigue trend coefficient; otherwise, the comparison continues, but the fatigue trend coefficient is not assigned a value.

8. The intelligent control method for fatigue testing of automotive drive shafts according to claim 7, characterized in that: The method for calculating the next stiffness slope and the previous stiffness slope within the detection time window is as follows: Within the detection time window, the difference between the next stiffness slope and the previous stiffness slope is calculated sequentially, and the results of the difference calculation are then summed.

9. The intelligent control method for fatigue testing of automotive drive shafts according to claim 8, characterized in that: The method for analyzing whether the drive shaft is prone to fatigue based on the fatigue trend coefficient is as follows: The fatigue trend coefficient is compared with zero. If the fatigue trend coefficient is greater than zero, it indicates that the drive shaft is approaching fatigue. If the fatigue trend coefficient is equal to zero, it indicates that the drive shaft is not approaching fatigue.

10. An intelligent control system for testing the fatigue strength of an automotive driveshaft, used to implement the intelligent control method for testing the fatigue strength of an automotive driveshaft as described in any one of claims 1-9, characterized in that, The intelligent control system for testing the fatigue of automotive drive shafts includes: a data acquisition module, a drive shaft fatigue analysis module, a fatigue trend analysis module, and an alarm module. Data acquisition module: used to acquire stiffness data in real time and perform preprocessing; Drive shaft fatigue analysis module: This module calculates the stiffness anomaly coefficient from the pre-processed stiffness data. Based on the stiffness anomaly coefficient, it analyzes whether the drive shaft is fatigued. If the analysis indicates that the drive shaft is fatigued, the alarm module is executed; if the analysis indicates that the drive shaft is not fatigued, the fatigue trend analysis module is executed. Fatigue Trend Analysis Module: This module calculates the fatigue trend coefficient based on the stiffness anomaly coefficient. It then analyzes whether the drive shaft is trending towards fatigue based on the fatigue trend coefficient. If the analysis indicates that the drive shaft is trending towards fatigue, the alarm module is activated. If the analysis indicates that the drive shaft is not trending towards fatigue, the data acquisition module is returned. Alarm module: Issues an alarm.

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