A method and system for predicting the remaining life of a gearbox

Abstract

To solve the problems of inaccurate prediction of the remaining life of the gearbox and high costs, the present invention provides a method and system for predicting the remaining life of a gearbox, including the following steps: S1. Obtain the driving data of the current vehicle, where the driving data includes the gearbox model, the type of driving scenario, and the number of gear cycles of the gearbox in each gear; S2. Respectively obtain the standard values of the gear cycle times matching the gearbox model, the type of driving scenario, and the gear in each gear; S3. Input the number of gear cycles already completed and the standard value of the gear cycle times corresponding to each gear into the remaining life prediction model to obtain the remaining driving mileage in the corresponding gear; S4. Send an alarm signal when the remaining driving mileage in any one gear is less than or equal to zero; by predicting the number of gear cycles of different gears of the current vehicle through the remaining life prediction model and reflecting it in the form of the remaining driving mileage, the accuracy of the remaining life of the gearbox can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicles, and particularly relates to a method and system for predicting the remaining life of a transmission. Background Art

[0002] As an important part of the powertrain of a commercial vehicle, the life of the transmission directly affects the service life of the commercial vehicle. In the prior art, vibration and noise signals are often measured on a test bench, and the signals are processed to perform a preliminary life trend analysis on the transmission. This method is costly and cannot predict the remaining life of the transmission matched with the whole vehicle.

[0003] The present invention provides a method for predicting the remaining life of a transmission matched with each vehicle by using a large database of the whole vehicle, and proposes a corresponding prediction system. Summary of the Invention

[0004] In order to solve the problems of inaccurate prediction and high cost of the remaining life of the transmission, the present invention provides a method and system for predicting the remaining life of a transmission, and the technical solutions adopted are as follows:

[0005] A method for predicting the remaining life of a transmission, based on a database storing historical driving data information of all vehicles, where the historical driving data includes transmission models, driving scenario categories, and various driving parameters, and includes the following steps:

[0006] S1. Obtain the driving data of the current vehicle, where the driving data includes the transmission model, the driving scenario category, and the number of gear cycles of the transmission in each gear;

[0007] S2. Respectively obtain the standard values of the gear cycle times matching the transmission model, the driving scenario category, and the gear in each gear;

[0008] S3. Input the number of gear cycles already cycled and the standard value of the gear cycle times corresponding to each gear into the remaining life prediction model to obtain the remaining driving mileage corresponding to each gear. The remaining life prediction model is: the product of the percentage of the remaining gear cycle times to the standard value of the gear cycle times and the standard value of the total driving mileage. The remaining gear cycle times is the difference between the standard value of the gear cycle times and the number of gear cycles already cycled, and the standard value of the total driving mileage is a preset standard value;

[0009] S4. Send an alarm signal when the remaining driving mileage in any one gear is less than or equal to zero, where the remaining driving mileage represents the remaining life of the transmission.

[0010] Preferably, in step S2, obtaining the standard value of the gear cycle times is specifically obtained by the following steps:

[0011] S201. Obtain the historical driving data of all vehicles stored in the database, where the historical driving data includes the transmission model, driving scenario category, and various driving parameters;

[0012] S202. Divide all the vehicles into multiple categories according to the transmission model and the driving scenario category;

[0013] S203. Obtain the standard value of the gear cycle times corresponding to each gear of the vehicles in each category based on the various driving parameters of all the vehicles in each category.

[0014] Preferably, in step S203, it is specifically implemented by the following steps:

[0015] S2031. Establish a gear fatigue cumulative damage model, where the output value of the gear fatigue cumulative damage model is the gear cycle times corresponding to each vehicle in the database, and the gear fatigue cumulative damage model is: ,

[0016] where t is the sampling interval, N i is the transmission input speed at different times for each gear collected, T i is the transmission input torque at different times for each gear collected, T0 is 100% torque, m is the S-N slope, l is the driving mileage, and L is the preset standard value of the total driving mileage;

[0017] S2032. Input the various driving parameters corresponding to all the vehicles in each category into the gear fatigue cumulative damage model respectively to obtain multiple gear cycle times;

[0018] S2033. After performing data processing operations on all the above gear cycle times, obtain the standard value of the gear cycle times, and the standard value of the cycle times represents the cycle times of the vehicles in the corresponding category.

[0019] Preferably, in step S2032, the various driving parameters include the driving mileage, the maximum torque at each gear, the S-N curve slope, the sampling interval, and the gear, speed, and sampling torque obtained by sampling multiple times at the same sampling interval.

[0020] Preferably, in step S2033, the data processing operations include taking the maximum value, the value corresponding to 90% reliability, or taking the average value to obtain the standard value of the gear cycle times of the vehicles in each category in the database.

[0021] A transmission remaining life prediction system, comprising:

[0022] A database that stores the historical driving data of all vehicles, where the historical driving data includes the transmission model, driving scenario category, and various driving parameters;

[0023] A data acquisition unit, which acquires driving data of the current vehicle, and the driving data includes the transmission model, the driving scenario category, and the number of gear cycles at each gear position of the transmission;

[0024] A standard value acquisition unit, which respectively acquires the standard values of the number of gear cycles matching the transmission model, the driving scenario category, and the gear position at each gear position;

[0025] A prediction unit, for each gear position, inputs the number of gear cycles and the standard value of the number of gear cycles into the remaining life prediction model to obtain the remaining driving mileage at the corresponding gear position. The remaining life prediction model is: the product of the percentage of the remaining number of gear cycles to the standard value of the number of gear cycles and the standard value of the total driving mileage. The remaining number of gear cycles is the difference between the standard value of the number of gear cycles and the number of gear cycles that have occurred, and the standard value of the total driving mileage is a preset standard value;

[0026] An alarm unit, which issues an alarm signal when the remaining driving mileage at any gear position is less than or equal to zero;

[0027] A memory;

[0028] And a processor.

[0029] The beneficial effects of the present invention are as follows:

[0030] 1. By using the remaining life prediction model to predict the number of gear cycles at different gear positions of the current vehicle and reflecting it in the form of the remaining driving mileage, an alarm is issued when the remaining driving mileage at any gear position is less than or equal to zero, avoiding accidents;

[0031] 2. By establishing a gear fatigue cumulative damage model to calculate the standard values of the number of gear cycles at different gear positions for each vehicle category in the vehicle information stored in the database, classifying the vehicles by transmission model and driving scenario category, the obtained standard values of the number of gear cycles are more representative and the prediction results are more accurate;

[0032] 3. The vehicle information in the database can be updated in a timely manner according to the vehicle status to improve the accuracy of predicting the remaining life of the vehicle's transmission. Description of the Drawings

[0033] Figure 1 It is a flowchart of the method for predicting the remaining life of the transmission

[0034] Figure 2 It is a flowchart of generating the standard value of the number of gear cycles in step S2

[0035] Figure 3Flow chart for generating the standard value of the gear cycle times for the gear fatigue cumulative damage model in step S203

[0036] Figure 4 、 Figure 5 Structural diagram of the remaining life prediction system for the gearbox

[0037] Among them, 401 - database, 402 - data acquisition unit, 403 - standard value acquisition unit, 404 - prediction unit, 405 - alarm unit, 406 - memory, 407 - processor, 408 - input / output device. Specific implementation manner

[0038] Hereinafter, the exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0039] As Figure 1 shown, the remaining life prediction method for the gearbox, based on a database storing all vehicle historical driving data information, the historical driving data including the gearbox model, the driving scenario category, and various driving parameters, includes the following steps:

[0040] S1. Obtain the driving data of the current vehicle, the driving data including the gearbox model, the driving scenario category, and the number of cycles that the gears in each gear position of the gearbox have undergone.

[0041] In this step, obtain the driving data of the current vehicle from the database. The driving data may include various information such as the driving mileage, vehicle information, and usage duration, and filter out the gearbox model information of the current vehicle, the usage scenario information of the vehicle, and the number of gear cycles in each gear position.

[0042] For example, the driving data shown in Table 1 is the data of the gear rotation speed, torque, instantaneous fuel consumption, and total vehicle driving mileage in each gear position of a gearbox with the model T2000 and the driving scenario of a logistics vehicle.

[0043]

[0044] Table 1

[0045] Specifically, in step S1, the formula for calculating the number of cycles that the gears in each gear position of the gearbox have undergone is as follows:

[0046] (1)

[0047] In the formula: T iT is the input torque of the transmission at different moments for each gear, with the unit of Nm; T0 is the 100% torque, with the unit of Nm; N i is the input speed of the transmission at different moments for each gear; t is the sampling interval.

[0048] S2. Obtain the standard values of the number of gear cycles that match the transmission model, driving scenario category, and gear for each gear respectively.

[0049] In this step, obtain the corresponding transmission model and the standard values of the number of gear cycles for each gear under the driving scenario category corresponding to the current vehicle. This standard value can represent the number of gear cycles of the vehicle corresponding to this transmission model under this driving scenario category at a specific gear, that is, each gear of each transmission model under each driving scenario category corresponds to a standard value.

[0050] For example, the standard value of the number of gear cycles of the 1st gear of a logistics vehicle is 2.24e 5 times.

[0051] The standard number of cycles for each gear of some gears is shown in Table 2:

[0052]

[0053] Table 2

[0054] In this step S2, obtaining the standard values of the number of gear cycles is achieved through steps S201 - S203, as Figure 2 shown. Specifically:

[0055] S201. Obtain the historical driving data of all vehicles stored in the database. The historical driving data includes the transmission model, driving scenario category, and various driving parameters;

[0056] In this step, obtain the vehicle data through the vehicle's data acquisition system via the CAN bus, and combine the positioning information and driving speed and other relevant information obtained by the GPS sensor as the data source for big data analysis. The more vehicles, the better.

[0057] As shown in Table 3, part of the vehicle data in the database includes the T2000 transmission model.

[0058]

[0059] Table 3

[0060] S202. Divide all the vehicles into multiple categories according to the transmission model and the driving scenario category;

[0061] In this step, the vehicle market is divided into several categories according to the vehicle's usage scenarios, such as trucks, dump trucks, logistics vehicles, etc. (the market can also be segmented according to other conditions).

[0062] The usage scenarios of some transmission models are shown in Table 3:

[0063]

[0064] Table 4

[0065] S203. Obtain the standard value of the gear cycle times corresponding to each gear of the vehicles in this category according to the driving parameters of all the vehicles in each category.

[0066] Among them, the standard value of the gear cycle times is obtained from steps S2031 to S2033, as Figure 3 shown, specifically including the following steps:

[0067] S2031. Establish a gear fatigue cumulative damage model, and the output value of the gear fatigue cumulative damage model is the gear cycle times corresponding to each vehicle in the database;

[0068] Specifically, the gear fatigue cumulative damage model is:

[0069] (2)

[0070] In the formula, T i is the input torque of the transmission at different moments of each gear collected, with the unit of Nm; T o is the 100% torque, with the unit of Nm; N i is the input speed of the transmission at different moments of each gear collected; t is the sampling interval.

[0071] S2032. Input the driving parameters corresponding to all the vehicles in each category into the gear fatigue cumulative damage model respectively to obtain multiple gear cycle times;

[0072] Through the relevant vehicle parameters, an example file as shown in Table 5 can be calculated, that is, the gear cycle times of each gear under different usage scenarios of the vehicle.

[0073]

[0074] Table 5

[0075] S2033. After processing the above all gear cycle times through data processing operations, obtain the standard value of the gear cycle times, and the standard value of the cycle times characterizes the cycle times of the vehicles in the corresponding category;

[0076] In this step, the number of gear cycles of vehicles corresponding to different gearbox models under different driving scenario categories obtained in step S2033 at a specific gear is processed according to relevant knowledge of data statistics. The data processing methods include different methods such as the number of cycles obtained by taking the mean of the data, the number of cycles obtained by removing the maximum and minimum values and then taking the mean, the number of cycles corresponding to a 90% reliability, etc., which can be selected according to the actual situation.

[0077] The standard values of each number of cycles obtained after data processing are shown in Table VI:

[0078]

[0079] Table VI

[0080] S3. For each gear, input the number of gear cycles that have occurred and the standard value of the number of gear cycles into the remaining life prediction model to obtain the remaining driving mileage corresponding to that gear.

[0081] In this step, both the number of gear cycles that have occurred and the standard value of the number of gear cycles of the current vehicle at a certain gear obtained in steps S1 and S2 are input into the remaining life prediction model to output the remaining driving mileage corresponding to that gear. Among them, the remaining life prediction model is the product of the percentage of the remaining number of gear cycles to the standard value of the number of gear cycles and the standard value of the total driving mileage. The remaining number of gear cycles is the difference between the standard value of the number of gear cycles and the number of gear cycles that have occurred, and the standard value of the total driving mileage is a preset standard value.

[0082] The remaining life prediction model is expressed by the formula as follows:

[0083] (3)

[0084] In the formula: X is the standard value of the number of gear cycles, X i is the number of gear cycles that have occurred, X - X i is the remaining number of gear cycles, L is the standard value of the total driving mileage, and this standard value can be a set value or empirical data obtained from experiments. Through the above model, the output value represents the remaining driving mileage of the current vehicle.

[0085] Through calculation, it can be obtained that the remaining kilometers of the first gear of the dump truck's gearbox is 100 km, and the second gear is 136 km.

[0086] S4. When the remaining driving mileage at any gear is less than or equal to zero, an alarm signal is issued, where the remaining driving mileage represents the remaining life of the gearbox.

[0087] By the above steps, the remaining driving mileage under each gear can be obtained. When any remaining driving mileage is less than or equal to zero, continuing to drive may cause transmission failure or driving failure. At this time, an alarm or a warning message needs to be issued.

[0088] Figure 4 and Figure 5 A transmission remaining life prediction system is disclosed, which consists of a database 401, a data acquisition unit 402, a standard value acquisition unit 403, a prediction unit 404, an alarm unit 405, a memory 406, and a processor 407. The database 401 stores historical driving data of all vehicles, including the transmission models, driving scenario categories, and driving parameters of all vehicles. The data acquisition unit 402 is used to respectively acquire the standard values of the gear cycle times matching the transmission model, driving scenario category, and gear under each gear; for each gear, the prediction unit 404 inputs the number of gear cycles that have occurred and the standard value of the gear cycle times into the remaining life prediction model to obtain the remaining driving mileage under the corresponding gear; the alarm unit 405 is used to issue an alarm signal when the remaining driving mileage under any gear is less than or equal to zero, where the remaining driving mileage represents the remaining life of the transmission. The memory 406 and the processor 407 are connected by a bus, and the bus is also connected to an input / output device 408. The memory 406 includes a read-only memory (ROM) and a random access memory (RAM). The memory 406 stores various computer instructions and data required to execute the system functions. The processor 501 reads various computer instructions from the memory 406 to perform various appropriate actions and processes; the input / output device 408 includes an input part such as a keyboard and a mouse, an output part including a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker, a storage part including a hard disk, and a communication part including a network interface card such as a LAN card and a modem.

[0089] The memory 406 also stores the following computer instructions to complete the operations specified in the transmission remaining life prediction method of the embodiment of the present invention: acquire the driving data of the current vehicle, where the driving data includes the transmission model, driving scenario category, and the number of gear cycles that have occurred in each gear of the transmission; respectively acquire the standard values of the gear cycle times matching the transmission model, driving scenario category, and gear under each gear; for each gear, input the number of gear cycles that have occurred and the standard value of the gear cycle times into the remaining life prediction model to obtain the remaining driving mileage under the corresponding gear; issue an alarm signal when the remaining driving mileage under any gear is less than or equal to zero, where the remaining driving mileage represents the remaining life of the transmission.

[0090] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, several improvements can be made without departing from the principle of the present invention, and these improvements should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting the remaining life of a transmission, characterized in that, Based on a database storing all historical driving data information of vehicles, the historical driving data includes transmission model, driving scenario category, and various driving parameters, and the method includes the following steps: S1. Obtain the driving data of the vehicle, where the driving data includes transmission model, driving scenario category, and the number of gear cycles of the transmission in each gear position; S2. Obtain the standard values of gear cycle times corresponding to the transmission model, driving scenario category, and gear position in each gear position respectively; S3. Input the number of gear cycles already completed and the standard value of gear cycle times corresponding to each gear position into the remaining life prediction model to obtain the remaining driving mileage in the corresponding gear position. The remaining life prediction model is: the product of the percentage of the remaining gear cycle times to the standard value of gear cycle times and the standard value of total driving mileage. The remaining gear cycle times is the difference between the standard value of gear cycle times and the number of gear cycles already completed, and the standard value of total driving mileage is a preset standard value; S4. Send an alarm signal when the remaining driving mileage in any one gear position is less than or equal to zero, where the remaining driving mileage represents the remaining life of the transmission.

2. The method for predicting the remaining life of a gearbox according to claim 1, wherein In step S2, obtaining the standard value of gear cycle times is specifically obtained by the following steps: S201. Obtain the historical driving data of all vehicles stored in the database, where the historical driving data includes transmission model, driving scenario category, and various driving parameters; S202. Divide all the vehicles into multiple categories according to the transmission model and the driving scenario category; S203. Obtain the standard value of gear cycle times corresponding to each gear position of the vehicles in each category according to the various driving parameters of all vehicles in each category.

3. The method for predicting the remaining life of a gearbox according to claim 2, characterized in that, In step S203, the standard value of gear cycle times is specifically implemented by the following steps: S2031, Establish a gear fatigue cumulative damage model. The output value of the gear fatigue cumulative damage model is the number of gear cycles corresponding to each vehicle in the database. The gear fatigue cumulative damage model is , where t is the sampling interval, and N i is the input speed of the gearbox at different moments of each gear position collected, and T i is the input torque of the gearbox at different moments of each gear position collected, T0 is 100% torque, m is the S-N slope, l is the driving mileage, and L is the preset standard value of the total driving mileage; S2032. Input the various driving parameters corresponding to all vehicles in each category into the gear fatigue cumulative damage model respectively to obtain multiple gear cycle times; S2033. Obtain the standard value of gear cycle times after performing data processing operations on all the above gear cycle times. The standard value of cycle times represents the cycle times of the vehicles in the corresponding category.

4. The method for predicting the remaining life of the gearbox according to claim 3, wherein In step S2032, the various driving parameters include driving mileage, maximum torque in each gear position, S-N curve slope, sampling interval, and gear position, rotational speed, and sampling torque obtained by sampling multiple times at the same sampling interval.

5. The method for predicting the remaining life of a gearbox according to claim 3, wherein In step S2033, the data processing operations include taking the maximum value, the value corresponding to 90% reliability, or taking the average value to obtain the standard value of gear cycle times of vehicles in each category in the database.

6. A system for predicting the remaining life of a transmission, including: A database storing historical driving data of all vehicles, where the historical driving data includes transmission model, driving scenario category, and various driving parameters; A data acquisition unit for obtaining the driving data of the current vehicle, where the driving data includes transmission model, driving scenario category, and the number of gear cycles of the transmission in each gear position; A standard value acquisition unit that respectively acquires the standard values of the number of gear cycles matching the transmission model, the driving scenario category, and the gear position in each gear position; A prediction unit that, for each gear position, inputs the number of cycles the gear has completed and the standard value of the number of gear cycles into a remaining life prediction model to obtain the remaining driving mileage at the corresponding gear position. The remaining life prediction model is: the product of the percentage of the remaining number of gear cycles to the standard value of the number of gear cycles and the standard value of the total driving mileage. The remaining number of gear cycles is the difference between the standard value of the number of gear cycles and the number of cycles the gear has completed, and the standard value of the total driving mileage is a preset standard value; An alarm unit that issues an alarm signal when the remaining driving mileage in any one gear position is less than or equal to zero; A memory; And a processor.

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