Device and System for Predicting Service Life of Automobile Universal Joint

Abstract

The present invention relates to the field of data processing technology, specifically to a device and system for predicting the service life of an automotive universal joint, which enables personalized prediction of the universal joint's service life based on the user's driving habits. The device includes a driving style analysis model that analyzes driving habits in conjunction with historical data to identify the user's driving style; a personalized life prediction module for constructing a personalized life prediction model and adaptively selecting a life prediction model based on the driving style to predict the life of the universal joint; the personalized life prediction model includes a first prediction model, a second prediction model, and a third prediction model; and an abnormal mode detection module for monitoring the operating status of the universal joint. When an abnormality is detected, the personalized life prediction model is optimized and the life prediction result is dynamically corrected.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a device and system for predicting the service life of an automobile universal joint. Background Art

[0002] Universal joints are commonly used components in transmission systems, and their lifespan directly impacts driving safety and performance. However, in most cases, users only notice problems when a universal joint shows obvious signs of damage. However, even in these early stages of damage, its lifespan is significantly shortened.

[0003] The rapid development of computer technology has provided new approaches to calculating universal joint lifespan, including computer-aided design (CAD) and computer-aided engineering (CAE), driving innovation in calculation methods. However, traditional universal joint lifespan prediction methods are unable to tailor predictions to individual user driving habits, resulting in insufficient prediction accuracy and impacting driving safety.

[0004] Therefore, a device and system for predicting the service life of an automobile universal joint are proposed. Summary of the Invention

[0005] The present invention aims to provide a device and system for predicting the service life of automotive universal joints, enabling personalized prediction of the joint's service life based on the user's driving habits. The device includes a driving style analysis model that analyzes driving habits in conjunction with historical data to identify the user's driving style; a personalized life prediction module that constructs a personalized life prediction model and adaptively selects a life prediction model based on the driving style to predict the life of the universal joint; and an abnormal pattern detection module that monitors the operating status of the universal joint and, when an abnormality is detected, adjusts the parameters of the personalized life prediction model to dynamically correct the life prediction results. The system comprises a driving style analysis unit, a personalized life prediction unit, and an abnormal pattern detection unit.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A service life prediction device for an automobile universal joint, comprising:

[0008] Driving style analysis module, which is used to analyze driving habits based on historical data and identify the user's driving style, including: smooth, sporty, and heavy;

[0009] a personalized life prediction module, configured to construct a personalized life prediction model and adaptively select a life prediction model based on the driving style to predict the life of the universal joint; the personalized life prediction model includes: a first prediction model, a second prediction model, and a third prediction model; if the driving style is the stable type, the first prediction model is used to perform a first life prediction based on operating temperature, torque load, and speed; if the driving style is the sporty type, the second prediction model is used to perform a second life prediction based on torque shock, transient high temperature, and high-frequency vibration; if the driving style is the heavy-load type, the third prediction model is used to perform a third life prediction by monitoring load changes, axial angle changes, and lubrication loss;

[0010] The abnormal mode detection module is used to monitor the operating status of the universal joint and optimize the parameters of the personalized life prediction model when an abnormal situation is detected, and dynamically correct the result of the life prediction.

[0011] Preferably, the historical data includes: vehicle operation data, driving behavior data, vehicle operating condition data and maintenance record data;

[0012] The vehicle operation data includes: load, speed, axial angle and operating temperature;

[0013] The driving behavior data includes: acceleration, braking force, steering angle and driving time distribution;

[0014] The vehicle operating condition data includes: lubrication status, ambient humidity, temperature and road condition type;

[0015] The maintenance record data includes: lubricating oil replacement cycle, universal joint replacement record and abnormal maintenance record;

[0016] Analyze driving habits based on the historical data and identify the user's driving style. The specific process is as follows:

[0017] The historical data is processed using a driving style analysis model to analyze the user's driving habits and classify the user's driving style.

[0018] Preferably, the personalized lifespan prediction model includes: the first prediction model, the second prediction model and the third prediction model;

[0019] The first prediction model includes: a temperature influence layer, a torque load calculation layer, a speed analysis layer and a first life prediction layer;

[0020] The temperature impact layer evaluates the effect of temperature on life based on the long-term operating temperature of the universal joint and obtains a temperature impact factor;

[0021] The torque load calculation layer calculates the impact of long-term load on life to obtain a torque load impact factor;

[0022] The speed analysis layer evaluates the impact of long-term high-speed operation on life and obtains a speed impact factor;

[0023] The first life prediction layer calculates the first remaining life of the universal joint by combining the temperature influence factor, the torque load influence factor, and the speed influence factor. The calculation formula of the first remaining life is:

[0024] ;

[0025] in, is the first remaining life; is the theoretical maximum lifespan; is the temperature impact weight; is the temperature influencing factor; is temperature; is the torque load influence weight; is the torque load influencing factor; is the torque load; The speed influence weight; is the speed influencing factor; is the rotational speed; is the interactive effect function of temperature, torque load and speed.

[0026] Preferably, the second prediction model includes: a torque impact monitoring layer, a temperature mutation detection layer, a vibration analysis layer and a second life prediction layer;

[0027] The torque impact monitoring layer detects sudden torque impact and analyzes the impact of the sudden torque impact on life;

[0028] The sudden temperature change detection layer identifies instantaneous high temperature conditions caused by intense driving and calculates the accelerated impact of the instantaneous high temperature conditions on the lifespan;

[0029] The vibration analysis layer monitors the amplitude and frequency of high-frequency vibrations of the universal joint when it is running at a high frequency, and analyzes the effect of the high-frequency vibrations on the wear of the universal joint;

[0030] The second life prediction layer calculates a second remaining life prediction result by integrating the torque impact monitoring layer, the temperature mutation detection layer and the vibration analysis layer.

[0031] Preferably, the third prediction model includes: a load variation analysis layer, an axial angle monitoring layer, a lubrication loss assessment layer and a third life prediction layer;

[0032] The load change analysis layer analyzes the long-term high load conditions borne by the universal joint and evaluates the impact of high load conditions on the life of the universal joint;

[0033] The axial angle monitoring layer monitors the angle change of the universal joint and calculates the impact of the angle change on the working state of the universal joint under high load conditions;

[0034] The lubrication loss assessment layer evaluates the lubricating oil condition and loss of the universal joint, and estimates the impact on the life of the universal joint by evaluating the lubricating oil loss under high load, frequent starting and braking conditions;

[0035] The third life prediction layer combines data from the load variation analysis layer, the axial angle monitoring layer, and the lubrication loss assessment layer to calculate the third remaining life of the universal joint under heavy-load driving.

[0036] Preferably, the process of the abnormal pattern detection module includes: an abnormal data acquisition layer, an abnormal pattern recognition layer and a dynamic correction prediction layer;

[0037] The abnormal data acquisition layer monitors temperature, load, speed, vibration and lubrication status in real time through sensors and compares them with normal operating conditions;

[0038] The abnormal pattern recognition layer uses machine learning algorithms to detect abnormal events in a short period of time, mark them as abnormal patterns and trigger alarms; the abnormal patterns include: sudden impact, high temperature and overload;

[0039] When the abnormal pattern is detected, the dynamic correction prediction layer adjusts the parameters of the personalized life prediction model and dynamically corrects the life prediction value.

[0040] A service life prediction system for an automobile universal joint, comprising:

[0041] Driving style analysis unit, used to analyze driving habits based on historical data and identify the user's driving style, including: smooth, sporty, and heavy;

[0042] a personalized life prediction unit, configured to construct a personalized life prediction model and adaptively select a life prediction model to predict the life of the universal joint based on the driving style; the personalized life prediction model includes: a first prediction model, a second prediction model, and a third prediction model; if the driving style is the stable type, the first prediction model is used to perform a first life prediction based on operating temperature, torque load, and speed; if the driving style is the sporty type, the second prediction model is used to perform a second life prediction based on torque shock, transient high temperature, and high-frequency vibration; if the driving style is the heavy-load type, the third prediction model is used to perform a third life prediction by monitoring load changes, axial angle changes, and lubrication loss;

[0043] The abnormal mode detection unit is used to monitor the operating status of the universal joint and optimize the parameters of the personalized life prediction model when an abnormal situation is detected, and dynamically correct the result of the life prediction.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. This invention proposes a driving style analysis model that accurately identifies a user's driving style by combining historical data, effectively distinguishing the impact of different driving behaviors on universal joint lifespan. By utilizing dynamic data analysis technology and incorporating actual driving behavior (such as acceleration, braking force, and steering angle), it can more accurately analyze the actual wear of universal joints under different driving styles. This provides a basis for subsequent personalized lifespan prediction based on driving style, thereby improving the accuracy of lifespan predictions.

[0046] 2. This invention proposes a personalized lifespan prediction model. Based on the driving type data provided by the driving style analysis model, it selects an appropriate lifespan prediction model (e.g., different models for stable, sporty, and heavy-duty driving) to perform personalized lifespan prediction. For example, the driving habits of sporty drivers typically result in higher impact loads, while heavy-duty driving results in higher sustained loads. This model can customize the lifespan prediction method based on driving style, improving the accuracy of universal joint service life prediction.

[0047] 3. The present invention monitors the operating status of the universal joint in real time and dynamically corrects the parameters of the life prediction model when an abnormal situation is detected. By continuously monitoring the temperature, load, speed, vibration and lubrication status of the universal joint, combined with a machine learning algorithm, this module can accurately identify sudden abnormal modes (such as sudden impact, high temperature and overload) and adjust the prediction results in a timely manner. This real-time correction mechanism greatly improves the accuracy and reliability of the prediction, especially when dealing with unforeseen driving environments or unexpected events, and can prevent incorrect predictions and premature / late replacement operations. This method has dynamic adaptability and automatic adjustment capabilities, and can accurately predict the life of the universal joint under abnormal working conditions, thereby reducing maintenance costs, improving safety, and reducing the occurrence of maintenance accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic structural diagram of a device for predicting the service life of an automobile universal joint provided by an embodiment of the present invention;

[0049] Figure 2 A working principle diagram of a service life prediction system for an automobile universal joint provided by an embodiment of the present invention;

[0050] Figure 3A schematic diagram of the structure of a personalized prediction model provided by an embodiment of the present invention;

[0051] Figure 4 This is a diagram of the working principle of the abnormal pattern detection module provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] Universal joints are a common component in automotive transmission systems, primarily used to connect two non-coaxial shafts, allowing them to transmit power at different angles. Their lifespan directly impacts a vehicle's driving safety and performance. However, in most cases, owners only notice problems when the joint exhibits noticeable damage (such as unusual noise or vibration). However, even in these early stages of damage, the joint's lifespan is significantly shortened.

[0054] This invention proposes a device and system for predicting the service life of a vehicle universal joint. This system enables personalized prediction of the service life of the universal joint based on the user's driving habits, thereby improving the accuracy of predictions. To illustrate the effectiveness of the method of the present invention in providing personalized predictions of the service life of the universal joint based on the user's driving habits, thereby improving the accuracy of predictions, the following two examples illustrate the effectiveness of the invention.

[0055] Example 1

[0056] In the embodiment of the present application, the method proposed by the present invention is used to describe in detail the process of personalized prediction of the service life of the universal joint according to the different driving habits of the users. Figure 1 The content describes in detail the service life prediction process of the automobile universal joint; among them, Figure 1 This is a schematic diagram of the structure of the life prediction device proposed in the present invention, which includes: a driving style analysis module for analyzing driving habits in combination with historical data to identify the user's driving style; a personalized life prediction module for constructing a personalized life prediction model and adaptively selecting the life prediction model based on the driving style to predict the life of the universal joint; and an abnormal mode detection module for monitoring the operating status of the universal joint and, when an abnormality is detected, adjusting the parameters of the personalized life prediction model to dynamically correct the life prediction result. Figure 2The specific structure diagram of the system proposed by the present invention includes: a driving style analysis unit, a personalized life prediction unit and an abnormal mode detection unit. Figure 1 and Figure 2 The following describes the contents:

[0057] A device for predicting the service life of a car universal joint, such as Figure 1 Shown, including:

[0058] Driving style analysis module, which is used to analyze driving habits based on historical data and identify the user's driving style, including: smooth, sporty, and heavy;

[0059] The historical data includes: vehicle operation data, driving behavior data, vehicle operating condition data and maintenance record data;

[0060] The vehicle operation data includes: load, speed, axial angle and operating temperature;

[0061] The driving behavior data includes: acceleration, braking force, steering angle and driving time distribution;

[0062] The vehicle operating condition data includes: lubrication status, ambient humidity, temperature and road condition type;

[0063] The maintenance record data includes: lubricating oil replacement cycle, universal joint replacement record and abnormal maintenance record;

[0064] Analyze driving habits based on the historical data and identify the user's driving style. The specific process is as follows:

[0065] The historical data is processed using a driving style analysis model to analyze the user's driving habits and classify the user's driving style.

[0066] Specifically, the driving style analysis model includes: a feature extraction layer and a driving style classification layer;

[0067] The feature extraction layer extracts driving style features based on the historical data, including acceleration mean, acceleration variance, braking force distribution, axial angle change frequency, and long-term load conditions;

[0068] The driving style classification layer classifies the driving style using a machine learning algorithm (eg, K-means clustering, decision tree, or neural network).

[0069] Table 1 shows the driving style classification results (g is the acceleration due to gravity).

[0070] Table 1 Driving style classification results

[0071]

[0072] Table 1 shows that vehicles A and D have lower acceleration, braking force, and speed, and are therefore classified as stable driving. Vehicles B and E have higher acceleration and speed, and greater braking force, and are therefore classified as sporty driving. Vehicle C has a higher load, lower speed, but a larger axial angle change, and is therefore classified as heavy-load driving.

[0073] The embodiment of the present application uses a driving style analysis model, combined with vehicle operation data, driving behavior data, vehicle working condition data and maintenance record data, to comprehensively extract the key factors affecting the life of the universal joint, and uses the feature extraction layer to calculate features such as acceleration mean, acceleration variance, braking force distribution, axial angle change frequency and long-term load conditions, so as to accurately characterize the driver's driving habits. The driving style classification layer uses machine learning algorithms such as K-means clustering, decision trees or neural networks to intelligently classify driving styles, and can accurately distinguish between stable, sporty and heavy-load driving styles. The driving style analysis module can adapt to different driving environments more comprehensively and accurately, so that the personalized life prediction model can be adaptively adjusted according to different driving styles, improve the accuracy of universal joint life prediction, reduce the risk of abnormal wear, and thus improve the safety and reliability of vehicle operation.

[0074] Preferably, Figure 3 As shown, the personalized life prediction module is used to build a personalized life prediction model and adaptively select a life prediction model to predict the life of the universal joint according to the driving style; if the driving style is the stable type, a first life prediction is performed based on the operating temperature, torque load and speed using a first prediction model; if the driving style is the sporty type, a second life prediction is performed based on torque impact, instantaneous high temperature and high-frequency vibration using a second prediction model; if the driving style is the heavy-load type, a third life prediction is performed by monitoring load changes, axial angle changes and lubrication loss using a third prediction model;

[0075] The personalized lifespan prediction model includes: the first prediction model, the second prediction model and the third prediction model;

[0076] The first prediction model includes: a temperature influence layer, a torque load calculation layer, a speed analysis layer, and a first life prediction layer; the temperature influence layer evaluates the influence of temperature on life based on the long-term operating temperature of the universal joint to obtain a temperature influence factor; the torque load calculation layer calculates the influence of long-term load on life to obtain a torque load influence factor; the speed analysis layer evaluates the influence of long-term high-speed operation on life to obtain a speed influence factor; the first life prediction layer calculates the first remaining life of the universal joint by combining the temperature influence factor, the torque load influence factor, and the speed influence factor; the calculation formula for the first remaining life is:

[0077] ;

[0078] in, is the first remaining life; is the theoretical maximum lifespan; is the temperature impact weight; is the temperature influencing factor; is temperature; is the torque load influence weight; is the torque load influencing factor; is the torque load; The speed influence weight; is the speed influencing factor; is the rotational speed; is the interactive effect function of temperature, torque load and speed, which represents the interactive influence among temperature, torque load and speed; For normal temperature; is the high temperature threshold; is the standard torque load; is the standard speed; 、 、 、 、 and is the experience adjustment coefficient; is the interactive effect of temperature and torque load; is the interactive effect of temperature and rotation speed; is the interaction effect of torque load and speed.

[0079] The embodiment of the present application adaptively selects a suitable life prediction model based on the user's driving style to improve the accuracy of the universal joint life prediction. Among them, the first prediction model is a life prediction model based on a smooth driving style. Through the temperature influence layer, the torque load calculation layer and the speed analysis layer, the influence of temperature, load and speed on the universal joint life under a smooth driving state is calculated respectively, and the corresponding weight factors are used for quantification, so that the prediction results are more in line with the actual working conditions. At the same time, the model introduces the interaction effect function between temperature, torque load and speed to comprehensively evaluate the coupling between different factors, making up for the limitation of the traditional prediction method that only considers the influence of a single factor. By optimizing the calculation formula, this method can not only more accurately predict the first remaining life of the universal joint under a smooth driving state, but also provide more targeted maintenance suggestions for the vehicle, extend the service life of key components, and improve the operational reliability and safety of the entire vehicle.

[0080] Preferably, the second prediction model includes: a torque impact monitoring layer, a temperature mutation detection layer, a vibration analysis layer and a second life prediction layer;

[0081] The torque impact monitoring layer detects sudden torque impact and analyzes the impact of the sudden torque impact on life;

[0082] The sudden temperature change detection layer identifies instantaneous high temperature conditions caused by intense driving and calculates the accelerated impact of the instantaneous high temperature conditions on the lifespan;

[0083] The vibration analysis layer monitors the amplitude and frequency of high-frequency vibrations of the universal joint when it is running at a high frequency, and analyzes the effect of the high-frequency vibrations on the wear of the universal joint;

[0084] The second life prediction layer calculates a second remaining life prediction result by integrating the torque impact monitoring layer, the temperature mutation detection layer and the vibration analysis layer.

[0085] Specifically, the torque impact monitoring layer uses a high-precision torque sensor installed near the universal joint to detect sudden torque impacts caused by sudden acceleration, sudden braking, or sharp turns during driving in real time. When the sensor detects a torque peak exceeding a preset threshold, a data recording unit stores the data and uses a signal processing algorithm to filter out noise and extract the effective impact amplitude and duration information.

[0086] The temperature mutation detection layer monitors instantaneous temperature changes on the universal joint surface through a fast-response temperature sensor. When the vehicle experiences a rapid temperature rise during intense driving, the temperature mutation data is immediately captured and the accelerated impact of the instantaneous high temperature on universal joint material fatigue and lubricant performance is calculated using a temperature change rate algorithm.

[0087] The vibration analysis layer uses high-sampling-rate accelerometers and spectrum analysis technology to monitor the high-frequency vibration signals generated by the universal joint during high-speed operation. By extracting the vibration amplitude, frequency distribution, and duration, it assesses local wear and fatigue damage caused by continuous high-frequency vibration, and performs smoothing and feature extraction on the vibration data.

[0088] The second life prediction layer integrates the torque impact, temperature mutation and vibration data obtained by the torque impact monitoring layer, the temperature mutation detection layer and the vibration analysis layer, and uses a mathematical model based on fatigue accumulation theory (such as Miner's law) to calculate the second remaining life of the universal joint.

[0089] This embodiment proposes a second prediction model that accurately predicts universal joint life under aggressive driving conditions (sporty driving style) through the collaborative work of a torque shock monitoring layer, a temperature jump detection layer, a vibration analysis layer, and a second life prediction layer. The torque shock monitoring layer detects torque shocks caused by sudden acceleration, braking, and cornering in real time, extracts shock amplitude and duration, and accurately assesses their impact on the fatigue life of the universal joint. The temperature jump detection layer uses a fast-response temperature sensor to identify transient high temperatures caused by aggressive driving and calculates the acceleration effect of high temperatures on the universal joint's material properties and lubrication status using a temperature change rate algorithm. The vibration analysis layer employs a high-sampling-rate accelerometer and spectrum analysis technology to accurately identify the vibration characteristics of the universal joint during high-frequency operation, assess the impact of high-frequency vibration on localized wear, and perform data optimization processing. The second life prediction layer, based on fatigue accumulation theory, comprehensively analyzes the wear and tear of the universal joint caused by torque shock, temperature jump, and vibration to calculate the second remaining life. This model effectively improves the accuracy of universal joint life prediction under aggressive driving conditions, reduces the risk of accidental damage caused by unexpected operating conditions, and improves vehicle operation safety and scientific maintenance.

[0090] Preferably, the third prediction model includes: a load variation analysis layer, an axial angle monitoring layer, a lubrication loss assessment layer and a third life prediction layer;

[0091] The load change analysis layer analyzes the long-term high load conditions borne by the universal joint and evaluates the impact of high load conditions on the life of the universal joint;

[0092] The axial angle monitoring layer monitors the angle change of the universal joint and calculates the impact of the angle change on the working state of the universal joint under high load conditions;

[0093] The lubrication loss assessment layer evaluates the lubricating oil condition and loss of the universal joint, and estimates the impact on the life of the universal joint by evaluating the lubricating oil loss under high load, frequent starting and braking conditions;

[0094] The third life prediction layer combines data from the load variation analysis layer, the axial angle monitoring layer, and the lubrication loss assessment layer to calculate the third remaining life of the universal joint under heavy-load driving.

[0095] Specifically, the load variation analysis layer uses high-precision strain sensors installed on the drive shaft to monitor the load on the universal joint in real time. It records the torque changes of the vehicle under different load conditions and uses long-term load trend analysis methods to calculate the impact of high load on the life of the universal joint. Combined with vehicle driving data, it analyzes the fluctuation range and distribution characteristics of load changes to assess the additional stress impact caused by uneven load changes.

[0096] The axial angle monitoring layer uses an angle sensor and a gyroscope to measure the axial angle changes of the universal joint in real time. Based on statistical analysis methods, it analyzes the axial angle change trend under high-load conditions and evaluates the impact of the axial angle change on lubrication effect and friction and wear in combination with vehicle operating characteristics. The lubrication loss assessment layer detects the temperature, viscosity, impurity content and oxidation degree of the lubricating oil in real time, and evaluates the deterioration trend of the lubricating oil in combination with vehicle operating data, and analyzes the impact of the lubrication status on the service life of the universal joint.

[0097] The third life prediction layer integrates data from the load variation analysis layer, the axial angle monitoring layer, and the lubrication loss assessment layer to predict the third remaining life using big data statistical analysis and machine learning algorithms (such as regression analysis or deep neural networks). By performing pattern recognition and trend fitting on historical data, a nonlinear prediction model for universal joint life changes under heavy-duty driving styles is established. The third remaining life is calculated and maintenance recommendations are provided.

[0098] The third prediction model proposed in the embodiment of the present application is based on the working condition characteristics of the universal joint under the heavy-load driving style, and realizes accurate life prediction and maintenance recommendations. Through the load change analysis layer, the load condition of the universal joint is monitored in real time, and the impact of long-term high load on the life is evaluated, making the prediction result more targeted; the axial angle monitoring layer combines the data of the angle sensor and the gyroscope to analyze the angle change of the universal joint, evaluate its impact on the lubrication status and friction and wear, and improve the adaptability of the prediction model to different working conditions; the lubrication loss assessment layer evaluates the deterioration trend of the lubricant by monitoring various parameters of the lubricant, which helps to discover the risk of life shortening caused by insufficient lubrication in advance; the third life prediction layer combines the above multi-source data, adopts big data statistical analysis and machine learning algorithms, realizes accurate life prediction under heavy-load working conditions, and provides optimized maintenance strategies to avoid unnecessary losses or safety hazards caused by premature or late maintenance, thereby improving the service life and reliability of the universal joint.

[0099] Table 2 shows the improvement effect of the personalized life prediction model on the accuracy of universal joint service life prediction compared with the single prediction model.

[0100] Table 2. Improvement in prediction accuracy of personalized lifespan prediction model

[0101]

[0102] Preferably, Figure 4 As shown, the abnormal mode detection module is used to monitor the operating status of the universal joint and adjust the parameters of the personalized life prediction model when an abnormal situation is detected, and dynamically correct the result of the life prediction.

[0103] The process of the abnormal pattern detection module includes: abnormal data collection layer, abnormal pattern recognition layer and dynamic correction prediction layer;

[0104] The abnormal data acquisition layer monitors temperature, load, speed, vibration and lubrication status in real time through sensors and compares them with normal operating conditions;

[0105] The abnormal pattern recognition layer uses machine learning algorithms to detect abnormal events in a short period of time, mark them as abnormal patterns and trigger alarms; the abnormal patterns include: sudden impact, high temperature and overload;

[0106] When the abnormal pattern is detected, the dynamic correction prediction layer adjusts the parameters of the personalized life prediction model and dynamically corrects the life prediction value.

[0107] Specifically, the abnormal data acquisition layer monitors the operating temperature of the universal joint surface and interior through temperature sensors and compares it with the normal temperature range;

[0108] Use load sensors to measure the torque load of the universal joint under different working conditions to determine whether there is abnormal overload;

[0109] The rotation speed sensor monitors the rotation speed of the universal joint to determine whether there is abnormal rapid acceleration or deceleration;

[0110] Use acceleration sensors to detect high-frequency vibrations and identify the impact of sudden shocks on universal joints;

[0111] The lubrication state sensor measures the viscosity, temperature and impurity content of the lubricating oil and analyzes abnormal changes in the lubrication state;

[0112] The abnormal pattern recognition layer uses machine learning algorithms (such as support vector machines (SVMs), random forests, or deep learning neural networks) to analyze abnormal data in a short period of time, identify, and mark abnormal patterns;

[0113] When the acceleration sensor detects an instantaneous impact exceeding a set threshold (e.g., 50g, where g is the acceleration due to gravity), it is marked as a sudden impact abnormal mode;

[0114] When the temperature sensor detects that the temperature exceeds the normal range (such as over 120°C), it is marked as high temperature abnormal mode;

[0115] When the load sensor detects that the load exceeds the rated load continuously (such as more than 120% of the rated torque), it is marked as overload abnormal mode;

[0116] When the abnormal pattern is identified, an alarm is immediately generated and the abnormal data is transmitted to the dynamic correction prediction layer;

[0117] The dynamic correction prediction layer adjusts the parameters of the personalized life prediction model according to the abnormality type, such as:

[0118] If a transient torque shock is detected, the shock factor weight is increased in the life prediction model to increase the impact of shock damage on life;

[0119] If the temperature exceeds the normal range, the temperature impact factor is adjusted to increase the impact of temperature on life and reduce the remaining life prediction value;

[0120] If the universal joint is continuously overloaded, the life prediction value will be dynamically reduced, and the user is advised to reduce overload operations.

[0121] Table 3 shows the life prediction correction results of the abnormal mode detection module. The predicted life before correction is the theoretical life prediction value, and the predicted life after correction takes into account the influence of abnormal working conditions.

[0122] Table 3 Life prediction correction results

[0123]

[0124] The embodiment of the present application monitors the temperature, load, speed, vibration and lubrication status of the universal joint in real time and compares them with normal operating data, so as to accurately identify abnormal conditions such as sudden impact, high temperature or overload caused by violent driving. Once an abnormal pattern is detected, the system uses machine learning algorithms such as support vector machines, random forests or deep learning to quickly analyze and mark the abnormal data, and generate an alarm. At the same time, the abnormal information is transmitted to the dynamic correction prediction layer, which automatically adjusts the key parameters in the personalized life prediction model according to the abnormality type, such as increasing the impact factor weight, adjusting the temperature influence factor or reducing the load parameter, so as to correct the remaining life prediction value in real time. This technical solution greatly improves the accuracy and real-time performance of the universal joint life prediction, so that the prediction results can better reflect the actual working condition changes, and provide a scientific and timely decision-making basis for vehicle maintenance and fault prevention, effectively reducing the risk of damage caused by abnormal working conditions and improving the overall operational safety and economy of the vehicle.

[0125] The vehicle universal joint service life prediction device provided in the embodiments of the present application can combine a driving style analysis module, a personalized life prediction module, and an abnormal pattern detection module to achieve personalized prediction of universal joint service life based on the user's driving habits, thereby improving the accuracy of universal joint service life prediction. Through the driving style analysis module, the system can identify the driver's driving habits and select corresponding life prediction models based on different driving styles, thereby improving the targetedness and accuracy of the prediction. The personalized life prediction module combines key factors such as operating temperature, torque load, torque shock, high-frequency vibration, and lubrication loss to construct personalized life prediction models for stable, sporty, and heavy-duty driving styles, respectively, making the life prediction results more realistic. Finally, the abnormal pattern detection module monitors the operating status of the universal joint in real time and dynamically adjusts the parameters of the life prediction model in the event of sudden impact, high temperature, or overload abnormalities, ensuring that the personalized prediction results are accurately corrected as actual operating conditions change. This device effectively improves the accuracy and reliability of universal joint life prediction, providing a scientific basis for vehicle maintenance and fault warning, thereby reducing the risk of accidental damage and improving the safety and economy of vehicle operation.

[0126] Example 2

[0127] In Example 1, the device proposed in this invention successfully achieved personalized prediction of universal joint service life based on the user's driving habits, thereby improving the accuracy of universal joint service life prediction. To further verify the effectiveness of this invention, this example also predicted the service life of a universal joint for a certain universal joint company.

[0128] A service life prediction system for automobile universal joints, such as Figure 2 Shown, including:

[0129] Driving style analysis unit, used to analyze driving habits based on historical data and identify the user's driving style, including: smooth, sporty, and heavy;

[0130] The historical data includes: vehicle operation data, driving behavior data, vehicle operating condition data and maintenance record data;

[0131] The vehicle operation data includes: load, speed, axial angle and operating temperature;

[0132] The driving behavior data includes: acceleration, braking force, steering angle and driving time distribution;

[0133] The vehicle operating condition data includes: lubrication status, ambient humidity, temperature and road condition type;

[0134] The maintenance record data includes: lubricating oil replacement cycle, universal joint replacement record and abnormal maintenance record;

[0135] Analyze driving habits based on the historical data and identify the user's driving style. The specific process is as follows:

[0136] The historical data is processed using a driving style analysis model to analyze the user's driving habits and classify the user's driving style.

[0137] Preferably, the personalized life prediction unit is used to construct a personalized life prediction model and adaptively select a life prediction model to predict the life of the universal joint according to the driving style; if the driving style is the stable type, a first life prediction is performed based on the operating temperature, torque load and speed using a first prediction model; if the driving style is the sporty type, a second life prediction is performed based on torque impact, instantaneous high temperature and high-frequency vibration using a second prediction model; if the driving style is the heavy-load type, a third life prediction is performed by monitoring load changes, axial angle changes and lubrication loss using a third prediction model;

[0138] The personalized lifespan prediction model includes: the first prediction model, the second prediction model and the third prediction model;

[0139] The first prediction model includes: a temperature influence layer, a torque load calculation layer, a speed analysis layer and a first life prediction layer;

[0140] The temperature impact layer evaluates the effect of temperature on life based on the long-term operating temperature of the universal joint and obtains a temperature impact factor;

[0141] The torque load calculation layer calculates the impact of long-term load on life to obtain a torque load impact factor;

[0142] The speed analysis layer evaluates the impact of long-term high-speed operation on life and obtains a speed impact factor;

[0143] The first life prediction layer calculates the first remaining life of the universal joint by combining the temperature influence factor, the torque load influence factor, and the speed influence factor. The calculation formula of the first remaining life is:

[0144] ;

[0145] in, is the first remaining life; is the theoretical maximum lifespan; is the temperature impact weight; is the temperature influencing factor; is temperature; is the torque load influence weight; is the torque load influencing factor; is the torque load; The speed influence weight; is the speed influencing factor; is the rotational speed; is the interaction effect function of temperature, torque load and speed, which represents the interaction between temperature, torque load and speed.

[0146] Preferably, the second prediction model includes: a torque impact monitoring layer, a temperature mutation detection layer, a vibration analysis layer and a second life prediction layer;

[0147] The torque impact monitoring layer detects sudden torque impact and analyzes the impact of the sudden torque impact on life;

[0148] The sudden temperature change detection layer identifies instantaneous high temperature conditions caused by intense driving and calculates the accelerated impact of the instantaneous high temperature conditions on the lifespan;

[0149] The vibration analysis layer monitors the amplitude and frequency of high-frequency vibrations of the universal joint when it is running at a high frequency, and analyzes the effect of the high-frequency vibrations on the wear of the universal joint;

[0150] The second life prediction layer calculates a second remaining life prediction result by integrating the torque impact monitoring layer, the temperature mutation detection layer and the vibration analysis layer.

[0151] Preferably, the third prediction model includes: a load variation analysis layer, an axial angle monitoring layer, a lubrication loss assessment layer and a third life prediction layer;

[0152] The load change analysis layer analyzes the long-term high load conditions borne by the universal joint and evaluates the impact of high load conditions on the life of the universal joint;

[0153] The axial angle monitoring layer monitors the angle change of the universal joint and calculates the impact of the angle change on the working state of the universal joint under high load conditions;

[0154] The lubrication loss assessment layer evaluates the lubricating oil condition and loss of the universal joint, and estimates the impact on the life of the universal joint by evaluating the lubricating oil loss under high load, frequent starting and braking conditions;

[0155] The third life prediction layer combines data from the load variation analysis layer, the axial angle monitoring layer, and the lubrication loss assessment layer to calculate the third remaining life of the universal joint under heavy-load driving.

[0156] Preferably, the abnormal mode detection unit is used to monitor the operating status of the universal joint and adjust the parameters of the personalized life prediction model when an abnormal situation is detected, so as to dynamically correct the result of the life prediction.

[0157] The process of the abnormal pattern detection unit includes: an abnormal data acquisition layer, an abnormal pattern recognition layer and a dynamic correction prediction layer;

[0158] The abnormal data acquisition layer monitors temperature, load, speed, vibration and lubrication status in real time through sensors and compares them with normal operating conditions;

[0159] The abnormal pattern recognition layer uses machine learning algorithms to detect abnormal events in a short period of time, mark them as abnormal patterns and trigger alarms; the abnormal patterns include: sudden impact, high temperature and overload;

[0160] When the abnormal pattern is detected, the dynamic correction prediction layer adjusts the parameters of the personalized life prediction model and dynamically corrects the life prediction value.

[0161] Table 4 shows the service life prediction results of the universal joint proposed in this application.

[0162] Table 4 Universal joint service life prediction results

[0163]

[0164] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A service life prediction device for automobile universal joints, characterized in that: include: Driving style analysis module, which is used to analyze driving habits based on historical data and identify the user's driving style, including: smooth, sporty, and heavy; a personalized life prediction module, configured to construct a personalized life prediction model and adaptively select a life prediction model based on the driving style to predict the life of the universal joint; the personalized life prediction model includes: a first prediction model, a second prediction model, and a third prediction model; if the driving style is the stable type, the first prediction model is used to perform a first life prediction based on operating temperature, torque load, and speed; if the driving style is the sporty type, the second prediction model is used to perform a second life prediction based on torque shock, transient high temperature, and high-frequency vibration; if the driving style is the heavy-load type, the third prediction model is used to perform a third life prediction by monitoring load changes, axial angle changes, and lubrication loss; The abnormal mode detection module is used to monitor the operating status of the universal joint and optimize the personalized life prediction model when an abnormal situation is detected, and dynamically correct the result of the life prediction.

2. The service life prediction device for an automobile universal joint according to claim 1, characterized in that: The historical data includes: vehicle operation data, driving behavior data, vehicle operating condition data and maintenance record data; the vehicle operation data includes: load, speed, axial angle and operating temperature; the driving behavior data includes: acceleration, braking force, steering angle and driving time distribution; the vehicle operating condition data includes: lubrication status, ambient humidity, temperature and road condition type; the maintenance record data includes: lubricant replacement cycle, universal joint replacement record and abnormal maintenance record; The driving habits are analyzed in combination with the historical data to identify the user's driving style. The specific process is: using a driving style analysis model to process the historical data, analyze the user's driving habits, and classify the user's driving style.

3. The service life prediction device for an automobile universal joint according to claim 1, characterized in that: The first prediction model includes: a temperature influence layer, a torque load calculation layer, a speed analysis layer and a first life prediction layer; the temperature influence layer evaluates the influence of temperature on life based on the long-term operating temperature of the universal joint to obtain a temperature influence factor; the torque load calculation layer calculates the influence of long-term load on life to obtain a torque load influence factor; the speed analysis layer evaluates the influence of long-term high-speed operation on life to obtain a speed influence factor; the first life prediction layer combines the temperature influence factor, the torque load influence factor and the speed influence factor to calculate the first remaining life of the universal joint.

4. The service life prediction device for an automobile universal joint according to claim 1, characterized in that: The second prediction model includes: a torque impact monitoring layer, a temperature mutation detection layer, a vibration analysis layer, and a second life prediction layer; The torque shock monitoring layer detects sudden torque shocks and analyzes the impact of the sudden torque shocks on life; the temperature mutation detection layer identifies instantaneous high temperature conditions caused by intense driving and calculates the accelerated impact of instantaneous high temperature on life; the vibration analysis layer monitors the amplitude and frequency of high-frequency vibrations of the universal joint when running at high frequency, and analyzes the impact of the high-frequency vibrations on the wear of the universal joint; the second life prediction layer calculates the second remaining life prediction result by integrating the results of the torque shock monitoring layer, the temperature mutation detection layer and the vibration analysis layer.

5. The service life prediction device for an automobile universal joint according to claim 1, characterized in that: The third prediction model includes: a load change analysis layer, an axial angle monitoring layer, a lubrication loss assessment layer and a third life prediction layer; The load change analysis layer analyzes the long-term high load conditions borne by the universal joint and evaluates the impact of high load conditions on the life of the universal joint; the axial angle monitoring layer monitors the angle changes of the universal joint and calculates the impact of the angle changes on the working state of the universal joint under high load conditions; the lubrication loss assessment layer analyzes the lubricating oil state and loss of the universal joint, and estimates the impact on the life of the universal joint by evaluating the lubricating oil loss under high load, frequent starting and braking; the third life prediction layer combines the data of the load change analysis layer, the axial angle monitoring layer and the lubrication loss assessment layer to calculate the third remaining life of the universal joint under heavy load driving.

6. The service life prediction device for automobile universal joints according to claim 1, characterized in that: The process of the abnormal pattern detection module includes: abnormal data collection layer, abnormal pattern recognition layer and dynamic correction prediction layer; The abnormal data acquisition layer monitors temperature, load, speed, vibration and lubrication status in real time through sensors, and compares them with normal operating status; the abnormal pattern recognition layer uses a machine learning algorithm to detect abnormal events in a short period of time, mark them as abnormal patterns and trigger alarms; the abnormal patterns include: sudden impact, high temperature and overload; when the dynamic correction prediction layer detects the abnormal pattern, it adjusts the parameters of the personalized life prediction model and dynamically corrects the life prediction value.

7. A service life prediction system for automobile universal joints, characterized in that: include: Driving style analysis unit, used to analyze driving habits based on historical data and identify the user's driving style, including: smooth, sporty, and heavy; a personalized life prediction unit, configured to construct a personalized life prediction model and adaptively select a life prediction model to predict the life of the universal joint based on the driving style; the personalized life prediction model includes: a first prediction model, a second prediction model, and a third prediction model; if the driving style is the stable type, the first prediction model is used to perform a first life prediction based on operating temperature, torque load, and speed; if the driving style is the sporty type, the second prediction model is used to perform a second life prediction based on torque shock, transient high temperature, and high-frequency vibration; if the driving style is the heavy-load type, the third prediction model is used to perform a third life prediction by monitoring load changes, axial angle changes, and lubrication loss; The abnormal mode detection unit is used to monitor the operating status of the universal joint and optimize the parameters of the personalized life prediction model when an abnormal situation is detected, and dynamically correct the result of the life prediction.

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