Online monitoring system for fatigue life of main reducer gear of electric vehicle

Abstract

The invention relates to the technical field of gear monitoring, and discloses an on-line monitoring system for fatigue life of a main reducer gear of an electric vehicle, which comprises an acquisition unit, a data processing unit, a data processing unit and a data processing unit, the construction unit constructs a gear comprehensive load spectrum according to the first contact stress load spectrum and the second contact stress load spectrum, the processing unit obtains the life slope of the life curve, whether the life slope is adjusted or not is judged according to the numerical value of the life slope, and when it is judged that the life slope is adjusted, the gear comprehensive load spectrum is obtained. And determining an adjustment factor according to the historical data set and determining a target life curve according to the adjustment factor. And the monitoring unit judges whether to give an early warning to the main reducer gear or not according to the target life curve. According to the invention, effective integration and collaborative analysis are carried out on the multi-source data, so that the reliability and safety of vehicle operation are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of gear monitoring, and in particular, to an online monitoring system for the fatigue life of the main reducer gears of an electric vehicle. Background Art

[0002] In the vehicle transmission system of an electric vehicle, the main reducer undertakes the important functions of reducing speed and increasing torque. With the increasingly complex vehicle working conditions, the loads borne by the gears show dynamic changes. The existing fatigue life monitoring of the main reducer gears is difficult to cope with the dynamic changes of the loads. When using multi-source monitoring data, there is a lack of a data fusion mechanism, making it difficult to effectively integrate and synergistically analyze the multi-source data. Various types of data are often processed independently, unable to form a comprehensive fatigue life monitoring, resulting in difficulty in giving early warnings under complex working conditions, thus affecting the reliability and safety of vehicle operation.

[0003] Therefore, it is necessary to design an online monitoring system for the fatigue life of the main reducer gears of an electric vehicle to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes an online monitoring system for the fatigue life of the main reducer gears of an electric vehicle, aiming to solve the problems that the existing fatigue life monitoring of the main reducer gears is difficult to cope with the dynamic changes of the loads, and when using multi-source monitoring data, there is a lack of a data fusion mechanism, making it difficult to effectively integrate and synergistically analyze the multi-source data. Various types of data are often processed independently, unable to form a comprehensive fatigue life monitoring, resulting in difficulty in giving early warnings under complex working conditions, thus affecting the reliability and safety of vehicle operation.

[0005] The present invention proposes an online monitoring system for the fatigue life of the main reducer gears of an electric vehicle, comprising: An acquisition unit, configured to acquire the vehicle resistance data and the gear data of the main reducer gears during a preset acquisition time period, and obtain a gear torque set and a gear stress set during the same preset acquisition time period, and preprocess the gear torque set and the gear stress set to respectively determine a torque data set and a stress data set; A construction unit, configured to determine a first contact stress load spectrum according to the vehicle resistance data and the gear data, and determine a second contact stress load spectrum based on the torque data set and the stress data set, and construct a gear comprehensive load spectrum according to the first contact stress load spectrum and the second contact stress load spectrum; A processing unit, configured to divide the comprehensive gear load spectrum based on a preset acquisition time period, determine a life curve according to the division result, obtain the life slope of the life curve, judge whether to adjust the life slope according to the value of the life slope, and when it is determined to adjust the life slope, determine an adjustment factor according to a historical data set and determine a target life curve according to the adjustment factor; A monitoring unit, configured to judge whether to issue a warning alarm for the main reducer gear according to the target life curve.

[0006] Further, when preprocessing the gear torque set and the gear stress set to respectively determine a torque data set and a stress data set, it includes: The acquisition unit preprocesses the gear torque set to determine the torque data set, and preprocesses the gear stress set to determine the stress data set; The preprocessing includes handling missing values and data binning; The handling of missing values is filled using a preset algorithm; The gear torque set and the gear stress set after handling missing values are subjected to the data binning, and the data binning is performed using a preset binning algorithm.

[0007] Further, when determining a first contact stress load spectrum according to the vehicle resistance data and the gear data, and determining a second contact stress load spectrum based on the torque data set and the stress data set, it includes: The construction unit builds a simulation environment and determines speed input data according to the CLTC-P road condition; Integrate the vehicle resistance data and the gear data according to the simulation environment to determine a first m-file, and determine the first contact stress load spectrum based on the first m-file and the speed input data; Integrate the torque data set and the stress data set according to the simulation environment to determine a second m-file, and determine the second contact stress load spectrum based on the second m-file and the speed input data.

[0008] Further, when constructing a comprehensive gear load spectrum according to the first contact stress load spectrum and the second contact stress load spectrum, it includes: The construction unit merges the first contact stress load spectrum and the second contact stress load spectrum, and judges whether there is a non-overlapping region; When there is no such non-overlapping region, construct the comprehensive gear load spectrum according to the merging result; When there is the non - overlapping region, obtain the first contact stress load value of the first contact stress load spectrum corresponding to the preset acquisition time period in the non - overlapping region, and obtain the second contact stress load value of the second contact stress load spectrum corresponding to the preset acquisition time period in the non - overlapping region; Determine the average contact stress load according to the first contact stress load value and the second contact stress load value, and use the average contact stress load of the entire non - overlapping region as the region to be overlapped. Construct the comprehensive gear load spectrum according to the overlapping region and the region to be overlapped.

[0009] Further, when dividing the comprehensive gear load spectrum based on the preset acquisition time period and determining the life curve according to the division result, it includes: The processing unit evenly divides the preset acquisition time period into several load time periods, and determines the corresponding contact stress values according to the several load time periods; Take the mean of all contact stress values corresponding to a load time period as the contact stress amplitude of this load time period, and determine the life curve according to each load time period and the corresponding contact stress amplitude.

[0010] Further, when determining the life curve according to each load time period and the corresponding contact stress amplitude, it includes: The processing unit determines the S - N curve based on the vehicle resistance data and the gear data; Determine the life values corresponding to the contact stress amplitudes of each load time period according to the S - N curve, and convert all life values into coordinate points; Take the mean of the load times of each load time period as the X - axis coordinate value of the corresponding coordinate point, and take the life value of each load time period as the Y - axis coordinate value of the corresponding coordinate point. Determine the life coordinate system according to the X - axis coordinate value and the Y - axis coordinate value, and connect adjacent coordinate points to determine the life curve.

[0011] Further, when obtaining the life slope of the life curve and judging whether to adjust the life slope according to the value of the life slope, it includes: The processing unit obtains all the life slopes in the life curve; When there is no life slope greater than zero, determine the life curve as the target life curve; When there is a life slope greater than zero, determine that the life slope is to be adjusted, and determine the adjusted result and the unadjusted life slopes as the target life curve.

[0012] Further, when determining the adjustment factor according to the historical data set and determining the target life curve according to the adjustment factor, it includes: The processing unit compares the lifespan slope to be adjusted with the historical dataset, and determines the adjustment factor according to the comparison result. The historical dataset includes several historical lifespan slopes and several historical adjustment factors, and each historical lifespan slope corresponds to a historical adjustment factor; When there is a historical lifespan slope in the historical dataset that is the same as the lifespan slope to be adjusted and the historical lifespan slope is unique, the historical adjustment factor corresponding to this historical lifespan slope is used as the adjustment factor; When there is a historical lifespan slope in the historical dataset that is the same as the lifespan slope to be adjusted and the historical lifespan slope is not unique, the average value of the historical adjustment factors corresponding to each historical lifespan slope is used as the adjustment factor; When there is no historical lifespan slope in the historical dataset that is the same as the lifespan slope to be adjusted, the historical dataset is divided, and the adjustment factor is determined according to the division result; The lifespan slope to be adjusted is in a direct proportional relationship with the adjustment factor.

[0013] Further, when dividing the historical dataset and determining the adjustment factor according to the division result, it includes: The processing unit divides the historical lifespan slopes in the historical dataset that are greater than the lifespan slope to be adjusted into a first historical lifespan set, and divides the historical lifespan slopes in the historical dataset that are less than or equal to the lifespan slope to be adjusted into a second historical lifespan set; Obtain the first historical lifespan average value of the first historical lifespan set, and obtain the second historical lifespan average value of the second historical lifespan set; Divide the historical lifespan slopes in the first historical lifespan set that are less than or equal to the first historical lifespan average value into a third historical lifespan set, and divide the historical lifespan slopes in the second historical lifespan set that are greater than or equal to the second historical lifespan average value into the third historical lifespan set; The average value of the historical adjustment factors corresponding to each historical lifespan slope in the third historical lifespan set is used as the adjustment factor.

[0014] Further, when judging whether to issue a warning alarm for the main reducer gear according to the target lifespan curve, it includes: The monitoring unit determines the target lifespan value based on the target lifespan curve; When the target lifespan value is greater than the preset target lifespan threshold, it is determined not to issue the warning alarm; When the target lifespan value is less than or equal to the preset target lifespan threshold, it is determined to issue the warning alarm.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By collecting vehicle resistance data and gear data, and simultaneously obtaining a gear torque set and a stress set and performing preprocessing, comprehensive and multi-dimensional data are provided. Combining the vehicle resistance data and the gear data to determine a first contact stress load spectrum, and determining a second contact stress load spectrum based on the torque data set and the stress data set, thereby constructing a comprehensive gear load spectrum, effectively integrating data information of different dimensions, and being able to comprehensively reflect the load condition of the gear during actual operation, changing the situation that the fatigue life monitoring of the main reducer gear in the past could not cope with the dynamic change of the load, and improving the adaptability and flexibility of the online monitoring system to complex working conditions. Dividing the comprehensive gear load spectrum based on a preset acquisition time period to determine the life curve and the life slope, and dynamically adjusting the life slope according to the historical data set, realizing the dynamic monitoring of the gear fatigue life, and judging whether to issue a warning alarm according to the target life curve, avoiding the risk of accidents caused by gear fatigue failures, and providing a reliable safety guarantee for the operation of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 FIG. is a functional block diagram of an online monitoring system for the fatigue life of the main reducer gear of an electric vehicle provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the 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 fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in conjunction with the embodiments.

[0018] Refer to Figure 1 As shown, in some embodiments of the present application, an online monitoring system for the fatigue life of the main reducer gear of an electric vehicle includes: An acquisition unit, configured to acquire vehicle resistance data and gear data of the main reducer gear in a preset acquisition time period, and obtain a gear torque set and a gear stress set in the same preset acquisition time period, and perform preprocessing on the gear torque set and the gear stress set to respectively determine a torque data set and a stress data set.

[0019] A building unit, configured to determine a first contact stress load spectrum according to vehicle resistance data and gear data, and determine a second contact stress load spectrum based on a torque data set and a stress data set, and construct a gear comprehensive load spectrum according to the first contact stress load spectrum and the second contact stress load spectrum.

[0020] A processing unit, configured to divide the gear comprehensive load spectrum based on a preset acquisition time period, determine a life curve according to the division result, obtain the life slope of the life curve, judge whether to adjust the life slope according to the value of the life slope, and when it is determined to adjust the life slope, determine an adjustment factor according to a historical data set and determine a target life curve according to the adjustment factor.

[0021] A monitoring unit, configured to judge whether to issue a warning alarm for the main reducer gear according to the target life curve.

[0022] Specifically, an acquisition unit acquires multi-source data during a preset acquisition time period, including vehicle resistance data, gear data of the main reducer gear, a gear torque set, and a gear stress set. The preset acquisition time period is set to six times per minute. The vehicle resistance data reflects the external resistance situation during vehicle driving, such as air resistance, road surface friction, etc. The gear data of the main reducer gear includes tooth width, gear pitch diameter, tooth number ratio, elastic coefficient, node region coefficient, service factor, dynamic load factor, tooth load distribution coefficient in the tooth direction, and tooth load sharing coefficient, etc. Among them, the elastic coefficient, node region coefficient, service factor, dynamic load factor, tooth load distribution coefficient in the tooth direction, and tooth load sharing coefficient are obtained through a gear big database. The gear torque set contains all the torque data of the gear, and the gear stress set contains all the stress data of the gear. The gear torque set and the gear stress set are obtained through devices such as a strain type torque sensor and a piezoelectric stress sensor, etc. When using devices such as sensors for acquisition, data loss may be caused by environmental interference or sensor sensitivity and other problems. A preprocessing method is adopted to remove the interference to determine the torque data set and the stress data set. Acquiring multi-dimensional data can comprehensively reflect the stress situation of the gear under dynamic loads, enabling the system to adapt to the load changes under complex working conditions in real time. The building unit determines a first contact stress load spectrum through the vehicle resistance data and the gear data, and determines a second contact stress load spectrum based on the torque data set and the stress data set. The first contact stress load spectrum reflects the contact stress situation of the gear under external resistance. The second contact stress load spectrum shows the stress change of the gear from the perspective of internal power transmission and stress. Combining these two load spectra to construct a gear comprehensive load spectrum can comprehensively and accurately reflect various load situations borne by the gear during actual operation, providing a complete load input for subsequent life analysis. It can be understood that the processing unit divides the comprehensive gear load spectrum based on a preset acquisition time period (six times per minute), determines the life curve by analyzing the load characteristics of each stage, and the life slope reflects the change trend of the gear's fatigue life under different load conditions. By monitoring the numerical change of the life slope, it is possible to dynamically determine whether the life curve needs to be adjusted. When there are abnormal changes, the historical data set is used to determine the adjustment factor and the life curve is adjusted accordingly, so as to obtain the target life curve that fits the actual working conditions, improving the flexibility and adaptability of the system. Based on the target life curve, the working state of the main reducer gear is monitored in real time, avoiding safety accidents caused by gear failure and improving the reliability and safety of vehicle operation.

[0023] In some embodiments of the present application, when preprocessing the gear torque set and the gear stress set to respectively determine the torque data set and the stress data set, it includes: the acquisition unit preprocesses the gear torque set to determine the torque data set, and preprocesses the gear stress set to determine the stress data set. The preprocessing includes handling missing values and data binning. The missing values are filled using a preset filling algorithm, and the gear torque set and the gear stress set after handling the missing values are binned using a preset binning algorithm.

[0024] Specifically, during the actual acquisition process of the acquisition unit, due to factors such as sensor failures and communication interference, missing values may occur in the gear torque set and the gear stress set. The preset filling algorithm fills according to the statistical characteristics of the data. The preset filling algorithm includes mean, median, or mode filling. One of them can be specifically selected according to the actual missing situation of the data. The data binning is performed using a preset binning algorithm. The preset binning algorithm uses equal-width binning, that is: the data is divided into different intervals according to a fixed time width. For example: during the acquisition process of six times per minute, the data collected every 20s can be divided into an interval. By handling the missing values, the analysis deviation caused by incomplete data is avoided, ensuring the continuity and integrity of the data, providing a good basis for subsequent data analysis, and the data binning makes the complex data intuitive and easy to process. Different bins can represent different working conditions or states, improving the reliability and stability of the system for monitoring the gear fatigue life.

[0025] In some embodiments of the present application, when determining the first contact stress load spectrum according to the vehicle resistance data and gear data, and determining the second contact stress load spectrum based on the torque data set and stress data set, the following steps are included: The construction unit builds a simulation environment, determines the speed input data according to the CLTC-P road conditions, integrates the vehicle resistance data and gear data according to the simulation environment to determine the first m-file, determines the first contact stress load spectrum based on the first m-file and speed input data, integrates the torque data set and stress data set according to the simulation environment to determine the second m-file, and determines the second contact stress load spectrum based on the second m-file and speed input data.

[0026] Specifically, the construction unit builds a simulation environment based on Matrix Laboratory (abbreviated as Matlab), and uses the China Light-Duty Vehicle Test Cycle (abbreviated as CLTC-P) as the reference benchmark for the driving speed of electric vehicles. CLTC-P is developed under the leadership of the China Automotive Technology and Research Center and has extensive test data. The speed input data is determined according to the CLTC-P road conditions. The vehicle resistance data and gear data are integrated into the simulation environment to generate the first m-file, which comprehensively incorporates information such as the external resistance during vehicle driving and the characteristics of the gear itself. Through mechanical calculations in the simulation environment with the speed input data and the first m-file, the first contact stress load spectrum of the gear under different working conditions is calculated. The first contact stress load spectrum reflects the contact stress situation of the gear under the action of external resistance. Similarly, the torque data set and stress data set are integrated according to the simulation environment to determine the second m-file, and the second contact stress load spectrum is determined based on the second m-file and speed input data. The second contact stress load spectrum shows the stress changes of the gear from the perspective of internal power transmission and force. The mechanical calculations involved in the simulation environment are obtained according to the gear contact stress formula, which is mature and lengthy and will not be elaborated here. By using the simulation environment and CLTC-P road conditions, the load situation of the vehicle during driving is fully considered, so that the determined first contact stress load spectrum and second contact stress load spectrum can reflect the stress state of the gear under different working conditions, which helps to comprehensively understand the stress situation of the gear and lays a data foundation for subsequent analysis and processing.

[0027] In some embodiments of the present application, when constructing a gear comprehensive load spectrum according to the first contact stress load spectrum and the second contact stress load spectrum, it includes: the construction unit merges the first contact stress load spectrum and the second contact stress load spectrum, and determines whether there is a non - overlapping region. When there is no non - overlapping region, the gear comprehensive load spectrum is constructed according to the merging result. When there is a non - overlapping region, the first contact stress load value corresponding to the first contact stress load spectrum in the preset acquisition time period in the non - overlapping region is obtained, and the second contact stress load value corresponding to the second contact stress load spectrum in the preset acquisition time period in the non - overlapping region is obtained. The average contact stress load is determined according to the first contact stress load value and the second contact stress load value, and all the average contact stress loads in the non - overlapping region are used as the region to be overlapped. The gear comprehensive load spectrum is constructed according to the overlapping region and the region to be overlapped.

[0028] Specifically, the construction unit merges the first contact stress load spectrum and the second contact stress load spectrum. During the merging process, if an overlapping region appears, this overlapping region is retained. When a non - overlapping region appears, it indicates that the two contact stress load spectra cannot completely overlap. In the non - overlapping region, the first contact stress load value corresponding to the first contact stress load spectrum in the preset acquisition time period (six times per minute) is obtained, and the second contact stress load value corresponding to the second contact stress load spectrum in the preset acquisition time period in the non - overlapping region is obtained. The average contact stress load is determined according to the first contact stress load value and the second contact stress load value, and all the average contact stress loads in the non - overlapping region are used as the region to be overlapped. For example: in the time period from 3:21 to 3:22 in the non - overlapping region, the first contact stress load values of the first contact stress load spectrum are 300MPa, 500MPa, 400MPa, 500MPa, 600MPa, 500MPa, while the second contact stress load values of the second contact stress load spectrum are 600MPa, 500MPa, 400MPa, 400MPa, 300MPa, 500MPa. Then the average contact stress loads of this non - overlapping region time period are 450MPa, 500MPa, 400MPa, 450MPa, 450MPa, 500MPa. According to this non - overlapping region time period and these average contact stress loads as the region to be overlapped, the existing region to be overlapped and the overlapping region are merged, so that the constructed gear comprehensive load spectrum can comprehensively reflect the characteristics of the gear contact stress, effectively integrate data from different sources, and thus improve the integrity and accuracy of the gear comprehensive load spectrum.

[0029] In some embodiments of the present application, when dividing the comprehensive load spectrum of the gear based on a preset acquisition time period and determining the life curve according to the division result, it includes: The processing unit evenly divides the preset acquisition time period into several load time periods, determines the corresponding contact stress values according to the several load time periods, takes the mean value of all the contact stress values corresponding to a load time period as the contact stress amplitude of this load time period, and determines the life curve according to each load time period and the corresponding contact stress amplitude.

[0030] In some embodiments of the present application, when determining the life curve according to each load time period and the corresponding contact stress amplitude, it includes: The processing unit determines the S-N curve based on the vehicle resistance data and the gear data, determines the life values corresponding to the contact stress amplitudes of each load time period according to the S-N curve, converts all the life values into coordinate points, takes the mean value of the load time of each load time period as the X-axis coordinate value of the corresponding coordinate point, and takes the life value of each load time period as the Y-axis coordinate value of the corresponding coordinate point, determines the life coordinate system according to the X-axis coordinate value and the Y-axis coordinate value, and connects the adjacent coordinate points to determine the life curve.

[0031] Specifically, the processing unit first evenly divides the preset acquisition time period into several load time periods. Preferably, the number of load time periods is 3. The reason for evenly dividing the preset acquisition time period is to more meticulously analyze the stress conditions of the gear at different time stages. For example, in the time period from 4:30 to 4:31, the values of the comprehensive load spectrum are 550 MPa, 500 MPa, 600 MPa, 750 MPa, 550 MPa, and 400 MPa. If this one-minute time period is evenly divided into 3 load time periods, then one of the load time periods in this time period is represented as 4:30:20, and the average value of all contact stress values corresponding to it is (550 MPa + 500 MPa) / 2 = 525 MPa. Then, in the time period from 4:30:00 to 4:30:20, its contact stress amplitude is 525 MPa. Based on the vehicle resistance data and gear data, the S-N curve is determined. The S-N curve reflects the fatigue life relationship of the material under different stress levels. According to the S-N curve, the fatigue life value corresponding to each contact stress amplitude can be determined, and the impact on the gear life can be measured based on the fatigue life value. When the stress on the gear is higher than the fatigue limit, each load cycle will cause a certain amount of damage to the gear, and this damage can accumulate. When the stress on the gear is equal to or lower than the fatigue limit, each load cycle may not cause damage to the gear. Therefore, the life value can be obtained by subtracting the gear life wear corresponding to the fatigue life value from the total life of the gear. The gear life wear corresponding to the fatigue life value is determined according to the usage instructions of the gear and relevant experiments. The larger the life value, the greater the current life of the gear. When the life value is smaller, it indicates a higher attenuation of the total gear life and a smaller gear life. By constructing a life coordinate system and then connecting adjacent coordinate points to form a life curve. The life curve is determined by fusing multi-source data, which improves the stability of gear monitoring.

[0032] In some embodiments of the present application, when obtaining the life slope of the life curve and determining whether to adjust the life slope according to the value of the life slope, it includes: the processing unit obtains all the life slopes in the life curve. When there is no life slope greater than zero, the life curve is determined as the target life curve. When there is a life slope greater than zero, it is determined that the life slope is adjusted, and the adjustment result and the unadjusted life slope are determined as the target life curve.

[0033] In some embodiments of the present application, when determining the adjustment factor according to the historical data set and determining the target life curve according to the adjustment factor, it includes: the processing unit compares the life slope to be adjusted with the historical data set, and determines the adjustment factor according to the comparison result. The historical data set includes a number of historical life slopes and a number of historical adjustment factors, and each historical life slope corresponds to a historical adjustment factor. When there is a historical life slope in the historical data set that is the same as the life slope to be adjusted and the historical life slope is unique, the historical adjustment factor corresponding to the historical life slope is used as the adjustment factor. When there is a historical life slope in the historical data set that is the same as the life slope to be adjusted and the historical life slope is not unique, the average value of the historical adjustment factors corresponding to each historical life slope is used as the adjustment factor. When there is no historical life slope in the historical data set that is the same as the life slope to be adjusted, the historical data set is divided, and the adjustment factor is determined according to the division result. The life slope to be adjusted and the adjustment factor are in a proportional relationship.

[0034] Specifically, the life slope reflects the trend of the gear fatigue life value changing with the load and the load time. When all life slopes are not greater than zero, it means that the life curve conforms to the normal fatigue life change law, and at this time, the life curve is determined as the target life curve. When there is a situation where the life slope is greater than zero, it indicates that the life curve shows an abnormal trend, that is, as the load time increases, the gear fatigue life increases instead. Therefore, it is necessary to adjust this life slope. The life slope to be adjusted is compared with the historical data set. The historical data set records a large number of historical life slopes and their corresponding historical adjustment factors. If there is a unique historical life slope in the historical data set that is the same as the life slope to be adjusted, the corresponding historical adjustment factor is directly used as the current adjustment factor. If there are multiple identical historical life slopes, the average value of the corresponding historical adjustment factors is taken to balance the influence of various situations. When there is no matching historical life slope in the historical data set, the historical data set is divided to ensure that the adjustment factor can reflect the adjustment requirements of the life slope. By judging and adjusting the life slope, the abnormal trend that does not conform to the law in the life curve is eliminated, so that the life curve can truly reflect the change of the gear fatigue life with the load time.

[0035] It can be understood that by constructing a proportional relationship, when it is necessary to reduce the life slope, the life slope can be reduced according to the adjustment factor. Moreover, in the process of determining the adjustment factor, using the historical data set to determine it fully draws on past empirical data. Whether it is directly matching or determining the adjustment factor through the division of the historical data set, it can effectively combine the actual situation, make the adjustment factor match the characteristics of the life slope, improve the stability of the gear fatigue life monitoring, enhance the system's processing ability for the life curve under various working conditions, and improve the adaptability and reliability of the entire online monitoring system.

[0036] In some embodiments of the present application, when dividing the historical data set and determining the adjustment factor according to the division result, it includes: the processing unit divides the historical life slopes in the historical data set that are greater than the life slope to be adjusted into a first historical life set, divides the historical life slopes in the historical data set that are less than or equal to the life slope to be adjusted into a second historical life set, obtains the first historical life average value of the first historical life set, and obtains the second historical life average value of the second historical life set. The historical life slopes in the first historical life set that are less than or equal to the first historical life average value are divided into a third historical life set, and the historical life slopes in the second historical life set that are greater than or equal to the second historical life average value are divided into the third historical life set. The average value of the historical adjustment factors corresponding to the historical life slopes in the third historical life set is used as the adjustment factor.

[0037] Specifically, in the historical data set, the first historical life set contains historical life slopes greater than the life slope to be adjusted, while the second historical life set is the historical life slopes less than or equal to the life slope to be adjusted. By calculating the average values of these two sets, the central tendency of the data within each set can be found respectively. The historical life slopes in the first historical life set that are less than or equal to the first historical life average value are numerically closer to the life slope to be adjusted compared to other data in the first historical life set. Similarly, the historical life slopes in the second historical life set that are greater than or equal to the second historical life average value are also more similar in magnitude and change trend to the life slope to be adjusted. These two parts of data are determined as the third historical life set, making the data within this set highly similar in characteristics to the current life slope to be adjusted. Thus, it provides a basis for adjusting the life slope by using the empirical law of historical data, ensures that the adjustment factor matches the requirements of the actual working conditions, avoids adjustment deviations caused by using irrelevant or significantly different historical data, enables the system to flexibly respond under different working conditions, effectively improves the adaptability to complex and changeable working conditions, and guarantees the effectiveness of on-line monitoring of gear fatigue life.

[0038] In some embodiments of the present application, when determining whether to issue a warning alarm for the main reducer gear according to the target life curve, it includes: the monitoring unit determines the target life value based on the target life curve. When the target life value is greater than the preset target life threshold, it is determined not to issue a warning alarm. When the target life value is less than or equal to the preset target life threshold, it is determined to issue a warning alarm.

[0039] Specifically, the target life value is the minimum life value on the target life curve, and the preset target life value is dynamically set according to gear data and relevant experimental data. By comparing the target life value with the preset target life threshold, the quantitative monitoring of the main reducer gear state is realized, the state change of gear fatigue can be captured in time, and the reliability of on-line monitoring is improved.

[0040] In summary, the beneficial effects of the present invention are as follows: By collecting vehicle resistance data and gear data, and simultaneously obtaining and preprocessing the gear torque set and stress set, comprehensive and multi-dimensional data are provided. The first contact stress load spectrum is determined by combining the vehicle resistance data and gear data, and the second contact stress load spectrum is determined based on the torque data set and stress data set, thereby constructing a comprehensive gear load spectrum, effectively integrating data information of different dimensions, and being able to comprehensively reflect the load condition of the gear during actual operation, changing the situation that the fatigue life monitoring of the main reducer gear in the past could not cope with the dynamic change of the load, and improving the adaptability and flexibility of the on-line monitoring system to complex working conditions. The comprehensive gear load spectrum is divided based on the preset acquisition time period to determine the life curve and life slope, and the life slope is dynamically adjusted according to the historical data set, realizing the dynamic monitoring of the gear fatigue life. Whether to issue a warning alarm is judged based on the target life curve, avoiding the risk of accidents caused by gear fatigue failures, and providing a reliable safety guarantee for the operation of the vehicle.

[0041] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0043] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the procedures Figure 1 one or more procedures and / or blocks Figure 1 the functions specified in one or more blocks.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the procedures Figure 1 one or more procedures and / or blocks Figure 1 the functions specified in one or more blocks.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. An online monitoring system for the fatigue life of the main reducer gear of an electric vehicle, characterized in that, Including: A collection unit, configured to collect the vehicle resistance data and the gear data of the main reducer gear during a preset collection time period, obtain a gear torque set and a gear stress set during the same preset collection time period, and preprocess the gear torque set and the gear stress set to respectively determine a torque data set and a stress data set; A construction unit, configured to determine a first contact stress load spectrum according to the vehicle resistance data and the gear data, determine a second contact stress load spectrum based on the torque data set and the stress data set, and construct a gear comprehensive load spectrum according to the first contact stress load spectrum and the second contact stress load spectrum; A processing unit, configured to divide the gear comprehensive load spectrum based on a preset collection time period, determine a life curve according to the division result, obtain the life slope of the life curve, judge whether to adjust the life slope according to the value of the life slope, and when it is determined to adjust the life slope, determine an adjustment factor according to a historical data set and determine a target life curve according to the adjustment factor; A monitoring unit, configured to judge whether to issue a warning alarm for the main reducer gear according to the target life curve.

2. The on-line monitoring system for the fatigue life of the main reducer gear of an electric vehicle according to claim 1, characterized in that When preprocessing the gear torque set and the gear stress set to respectively determine a torque data set and a stress data set, it includes: The collection unit preprocesses the gear torque set to determine the torque data set, and preprocesses the gear stress set to determine the stress data set; The preprocessing includes handling missing values and data binning; The handling of missing values is filled using a preset algorithm; The gear torque set and the gear stress set after handling the missing values are subjected to the data binning, and the data binning is performed using a preset binning algorithm.

3. The on-line monitoring system for the gear fatigue life of the main reducer of an electric vehicle according to claim 2, wherein, When determining a first contact stress load spectrum according to the vehicle resistance data and the gear data, and determining a second contact stress load spectrum based on the torque data set and the stress data set, it includes: The construction unit builds a simulation environment and determines speed input data according to the CLTC-P road condition; Integrate the vehicle resistance data and the gear data according to the simulation environment to determine a first m-file, and determine the first contact stress load spectrum based on the first m-file and the speed input data; Integrate the torque data set and the stress data set according to the simulation environment to determine a second m-file, and determine the second contact stress load spectrum based on the second m-file and the speed input data.

4. The on-line monitoring system for the gear fatigue life of the main reducer of an electric vehicle according to claim 3, characterized in that, When constructing a gear comprehensive load spectrum according to the first contact stress load spectrum and the second contact stress load spectrum, it includes: The construction unit merges the first contact stress load spectrum and the second contact stress load spectrum, and judges whether there is a non-overlapping region; When there is no such non-overlapping region, construct the gear comprehensive load spectrum according to the merging result; When there is such a non-overlapping region, obtain the first contact stress load value of the first contact stress load spectrum corresponding to the preset collection time period in the non-overlapping region, and obtain the second contact stress load value of the second contact stress load spectrum corresponding to the preset collection time period in the non-overlapping region; Determine the average contact stress load value based on the first contact stress load value and the second contact stress load value, and use all the average contact stress load values in the non-coincident area as the area to be coincident. Construct the comprehensive gear load spectrum based on the coincident area and the area to be coincident.

5. The on-line monitoring system for the gear fatigue life of the main reducer of an electric vehicle according to claim 4, characterized in that, When dividing the comprehensive gear load spectrum based on a preset acquisition time period and determining the life curve according to the division result, it includes: The processing unit evenly divides the preset acquisition time period into several load time periods, and determines the corresponding contact stress values according to the several load time periods; Take the average value of all the contact stress values corresponding to a load time period as the contact stress amplitude of this load time period, and determine the life curve according to each load time period and the corresponding contact stress amplitude.

6. The on-line monitoring system for the fatigue life of the main reducer gear of an electric vehicle according to claim 5, characterized in that, When determining the life curve according to each load time period and the corresponding contact stress amplitude, it includes: The processing unit determines the S-N curve based on the vehicle resistance data and the gear data; Determine the life value corresponding to the contact stress amplitude of each load time period according to the S-N curve, and convert all the life values into coordinate points; Take the average load time of each load time period as the X-axis coordinate value of the corresponding coordinate point, and take the life value of each load time period as the Y-axis coordinate value of the corresponding coordinate point. Determine the life coordinate system according to the X-axis coordinate value and the Y-axis coordinate value, and connect adjacent coordinate points to determine the life curve.

7. The on-line monitoring system for the fatigue life of the main reducer gear of an electric vehicle according to claim 6, characterized in that, When obtaining the life slope of the life curve and judging whether to adjust the life slope according to the value of the life slope, it includes: The processing unit obtains all the life slopes in the life curve; When there is no life slope greater than zero, determine the life curve as the target life curve; When there is a life slope greater than zero, determine that the life slope is to be adjusted, and determine the adjusted result and the unadjusted life slopes as the target life curve.

8. The on-line monitoring system for the fatigue life of the main reducer gear of an electric vehicle according to claim 7, characterized in that, When determining the adjustment factor according to the historical data set and determining the target life curve according to the adjustment factor, it includes: The processing unit compares the life slope to be adjusted with the historical data set, and determines the adjustment factor according to the comparison result. The historical data set includes several historical life slopes and several historical adjustment factors, and each historical life slope corresponds to a historical adjustment factor; When there is a historical life slope in the historical data set that is the same as the life slope to be adjusted and the historical life slope is unique, take the historical adjustment factor corresponding to this historical life slope as the adjustment factor; When there is a historical life slope in the historical data set that is the same as the life slope to be adjusted and the historical life slope is not unique, take the average value of the historical adjustment factors corresponding to each historical life slope as the adjustment factor; When there is no historical life slope in the historical data set that is the same as the life slope to be adjusted, divide the historical data set, and determine the adjustment factor according to the division result; The life slope to be adjusted is in a proportional relationship with the adjustment factor.

9. The on-line monitoring system for the fatigue life of the main reducer gear of an electric vehicle according to claim 8, characterized in that, When dividing the historical data set and determining the adjustment factor according to the division result, it includes: The processing unit divides the historical life slopes in the historical dataset that are greater than the life slope to be adjusted into a first historical life set, and divides the historical life slopes in the historical dataset that are less than or equal to the life slope to be adjusted into a second historical life set; Obtain the first historical life average value of the first historical life set, and obtain the second historical life average value of the second historical life set; Divide the historical life slopes in the first historical life set that are less than or equal to the first historical life average value into a third historical life set, and divide the historical life slopes in the second historical life set that are greater than or equal to the second historical life average value into the third historical life set; Take the mean value of the historical adjustment factors corresponding to the historical life slopes in the third historical life set as the adjustment factor.

10. The on-line monitoring system for the fatigue life of the main reducer gears of an electric vehicle according to claim 9, characterized in that, When determining whether to issue a warning alarm for the main reducer gear according to the target life curve, it includes: The monitoring unit determines the target life value based on the target life curve; When the target life value is greater than the preset target life threshold, it is determined not to issue the warning alarm; When the target life value is less than or equal to the preset target life threshold, it is determined to issue the warning alarm.

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