An online monitoring system for fatigue life of electric vehicle main reducer gears

Abstract

The present invention relates to the technical field of gear monitoring, and discloses an online monitoring system for the fatigue life of the gears of the main reducer of an electric vehicle. The system comprises: an acquisition unit pre-processes the gear torque set and the gear stress set to respectively determine a torque data set and a stress data set; a construction unit constructs a gear comprehensive load spectrum based on a first contact stress load spectrum and a second contact stress load spectrum; a processing unit obtains the life slope of the life curve, and determines whether to adjust the life slope based on the value of the life slope; when it is determined to adjust the life slope, an adjustment factor is determined based on the historical data set and a target life curve is determined based on the adjustment factor. The monitoring unit determines whether to issue a warning alarm for the main reducer gear based on the target life curve. The present invention effectively integrates and collaboratively analyzes multi-source data, thereby ensuring the reliability and safety of vehicle operation.

Description

Technical Field

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

[0002] In the vehicle transmission system of electric vehicles, the main reducer assumes the important function of reducing the speed and increasing the torque. As the vehicle operating conditions become increasingly complex, the load borne by the gears changes dynamically. The existing main reducer gear fatigue life monitoring is difficult to cope with the dynamic changes in load. When using multi-source monitoring data, there is a lack of data fusion mechanism, making it difficult to effectively integrate and collaboratively analyze multi-source data. Various types of data are often processed independently, and a comprehensive fatigue life monitoring system cannot be formed. As a result, it is difficult to issue 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 electric vehicle main reducer gear 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 gear of an electric vehicle, aiming to solve the problem that the existing main reducer gear fatigue life monitoring is difficult to cope with dynamic changes in load. When using multi-source monitoring data, there is a lack of data fusion mechanism, which makes it difficult to effectively integrate and collaboratively analyze multi-source data. Various types of data are often processed independently, and a comprehensive fatigue life monitoring cannot be formed, resulting in difficulty in issuing early warnings under complex working conditions, thereby affecting the reliability and safety of vehicle operation.

[0005] The present invention proposes an online monitoring system for fatigue life of electric vehicle main reducer gears, comprising:

[0006] a collection unit configured to collect vehicle resistance data and gear data of a final drive gear within a preset collection time period, and obtain a gear torque set and a gear stress set within the same preset collection time period, and pre-process the gear torque set and the gear stress set to respectively determine a torque data set and a stress data set;

[0007] a construction unit configured to determine a first contact stress load spectrum based on 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 based on the first contact stress load spectrum and the second contact stress load spectrum;

[0008] 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 a life slope of the life curve, determine whether to adjust the life slope according to a value of the life slope, and when it is determined that the life slope is to be adjusted, determine an adjustment factor according to a historical data set and determine a target life curve according to the adjustment factor;

[0009] The monitoring unit is configured to determine whether to issue a warning alarm to the main reducer gear according to the target life curve.

[0010] Furthermore, when the gear torque set and the gear stress set are preprocessed to respectively determine the torque data set and the stress data set, the method includes:

[0011] The acquisition unit pre-processes the gear torque set to determine the torque data set, and pre-processes the gear stress set to determine the stress data set;

[0012] The preprocessing includes processing missing values and data binning;

[0013] The missing values are filled using a preset filling algorithm;

[0014] The gear torque set and the gear stress set after processing the missing values are subjected to the data binning, and the data binning is performed using a preset binning algorithm.

[0015] Furthermore, when determining a first contact stress load spectrum based on 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, the method includes:

[0016] The construction unit builds a simulated environment and determines speed input data according to the CLTC-P road conditions;

[0017] Integrating the vehicle resistance data and gear data according to the simulated environment to determine a first m-file, and determining the first contact stress load spectrum based on the first m-file and the speed input data;

[0018] The torque data set and the stress data set are integrated according to the simulated environment to determine a second m-file, and the second contact stress load spectrum is determined based on the second m-file and the velocity input data.

[0019] Furthermore, when constructing a gear comprehensive load spectrum according to the first contact stress load spectrum and the second contact stress load spectrum, the method includes:

[0020] The construction unit combines the first contact stress load spectrum and the second contact stress load spectrum, and determines whether there is a non-overlapping area;

[0021] When the non-overlapping area does not exist, constructing the gear comprehensive load spectrum according to the merging result;

[0022] When the non-overlapping area exists, obtaining a first contact stress load value corresponding to a first contact stress load spectrum in a preset acquisition time period in the non-overlapping area, and obtaining a second contact stress load value corresponding to a second contact stress load spectrum in the preset acquisition time period in the non-overlapping area;

[0023] The average contact stress load value is determined according to the first contact stress load value and the second contact stress load value, and the average contact stress load value of all the non-overlapping areas is used as the area to be overlapped, and the gear comprehensive load spectrum is constructed according to the overlapping area and the area to be overlapped.

[0024] Furthermore, when the gear comprehensive load spectrum is divided based on the preset collection time period and the life curve is determined according to the division result, it includes:

[0025] The processing unit evenly divides the preset acquisition time period into a plurality of load time periods, and determines corresponding contact stress values according to the plurality of load time periods;

[0026] The average of all contact stress values corresponding to a load time period is used as the contact stress amplitude of the load time period, and the life curve is determined according to each load time period and the corresponding contact stress amplitude.

[0027] Furthermore, when determining the life curve according to each load time period and the corresponding contact stress amplitude, it includes:

[0028] The processing unit determines an SN curve based on the vehicle resistance data and gear data;

[0029] Determine the life value of the contact stress amplitude corresponding to each load time period according to the SN curve, and convert all life values into coordinate points;

[0030] The load time mean of each load time period is used as the X-axis coordinate value of the corresponding coordinate point, and the life value of each load time period is used as the Y-axis coordinate value of the corresponding coordinate point. The life coordinate system is determined according to the X-axis coordinate value and the Y-axis coordinate value, and the adjacent coordinate points are connected to determine the life curve.

[0031] Furthermore, 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, the method includes:

[0032] The processing unit obtains all life slopes in the life curve;

[0033] When there is no lifespan slope greater than zero, determining the lifespan curve as the target lifespan curve;

[0034] When there is a life slope greater than zero, it is determined that the life slope is to be adjusted, and the adjustment result and the life slope without adjustment are determined as the target life curve.

[0035] Furthermore, when determining the adjustment factor based on the historical data set and determining the target life curve based on the adjustment factor, it includes:

[0036] 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, wherein the historical data set includes a plurality of historical life slopes and a plurality of historical adjustment factors, and each historical life slope corresponds to a historical adjustment factor;

[0037] 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;

[0038] 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 of the historical adjustment factors corresponding to the historical life slopes is used as the adjustment factor;

[0039] When there is no historical life slope identical to the life slope to be adjusted in the historical data set, dividing the historical data set, and determining the adjustment factor according to the division result;

[0040] The life slope that needs to be adjusted is proportional to the adjustment factor.

[0041] Furthermore, when dividing the historical data set and determining the adjustment factor according to the division result, the method includes:

[0042] The processing unit divides the historical life slopes in the historical data set that are greater than the life slope that needs to be adjusted into a first historical life set, and divides the historical life slopes in the historical data set that are less than or equal to the life slope that needs to be adjusted into a second historical life set;

[0043] Obtaining a first historical life average value of the first historical life set, and obtaining a second historical life average value of the second historical life set;

[0044] dividing the historical life slopes in the first historical life set that are less than or equal to the first historical life average into a third historical life set, and dividing the historical life slopes in the second historical life set that are greater than or equal to the second historical life average into the third historical life set;

[0045] The average of the historical adjustment factors corresponding to the historical life slopes in the third historical life set is used as the adjustment factor.

[0046] Furthermore, when determining whether to issue a warning alarm for the main reducer gear according to the target life curve, the method includes:

[0047] The monitoring unit determines a target life value based on the target life curve;

[0048] When the target life value is greater than a preset target life threshold, determining not to issue the early warning alarm;

[0049] When the target life value is less than or equal to a preset target life threshold, it is determined that the early warning alarm is issued.

[0050] Compared with the prior art, the beneficial effect of the present invention is that it provides comprehensive and multi-dimensional data by collecting vehicle resistance data and gear data, obtaining gear torque sets and stress sets at the same time and pre-processing them. The first contact stress load spectrum is determined by combining 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. This effectively integrates data information of different dimensions and can comprehensively reflect the load condition of the gear in actual operation. It changes the previous situation where the fatigue life monitoring of the main reducer gear cannot cope with dynamic changes in load, and improves the adaptability and flexibility of the online monitoring system to complex working conditions. Based on the preset acquisition time period, the gear comprehensive load spectrum is divided to determine the life curve and life slope, and the life slope is dynamically adjusted according to the historical data set, realizing dynamic monitoring of the gear fatigue life. It determines whether to issue a warning alarm based on the target life curve, avoids the risk of accidents caused by gear fatigue failure, and provides reliable safety protection for the operation of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0052] Figure 1 This is a functional block diagram of an online monitoring system for fatigue life of electric vehicle main reducer gears provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying 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 to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0054] See Figure 1 As shown, in some embodiments of the present application, an online monitoring system for fatigue life of a gear of a main reducer of an electric vehicle includes:

[0055] The acquisition unit is configured to collect 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 pre-process the gear torque set and the gear stress set to respectively determine a torque data set and a stress data set.

[0056] The construction unit is configured to determine a first contact stress load spectrum based on vehicle resistance data and gear data, 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 based on the first contact stress load spectrum and the second contact stress load spectrum.

[0057] The processing unit is 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 a life slope of the life curve, determine 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.

[0058] The monitoring unit is configured to determine whether to issue a pre-warning alarm for the main reducer gear according to the target life curve.

[0059] Specifically, the acquisition unit acquires multi-source data in a preset acquisition time period, including vehicle resistance data, gear data of the main reducer gear, gear torque set and gear stress set. The preset acquisition time period is set to six times per minute. The vehicle resistance data reflects the external resistance encountered by the vehicle during driving, such as air resistance, road friction, etc. The gear data of the main reducer gear includes tooth width, gear pitch circle diameter, gear ratio, elastic coefficient, node area coefficient, usage coefficient, dynamic load coefficient, tooth load distribution coefficient and inter-tooth load distribution coefficient, etc. Among them, the elastic coefficient, node area coefficient, usage coefficient, dynamic load coefficient, tooth load distribution coefficient and The inter-tooth load distribution coefficient is derived from a large gear database. The gear torque set contains all gear torque data, while the gear stress set contains all gear stress data. The gear torque set and gear stress set are obtained using devices such as strain gauge torque sensors and piezoelectric stress sensors, respectively. However, data acquisition using these sensors can be subject to data loss due to environmental interference or sensor sensitivity issues. Preprocessing is used to remove this interference and determine the torque and stress data sets. This multi-dimensional data collection comprehensively reflects the gear's dynamic loads, enabling the system to adapt to changing loads in real time under complex operating conditions. The construction unit determines a first contact stress load spectrum based on vehicle drag and gear data, and a second contact stress load spectrum based on the torque and stress data sets. The first contact stress load spectrum reflects the gear's contact stress under external drag. The second contact stress load spectrum represents the stress variations in the gear from the perspective of internal power transmission and force. Combining these two load spectra creates a comprehensive gear load spectrum that comprehensively and accurately reflects the various loads experienced by the gear during actual operation, providing a complete load input for subsequent life analysis.

[0060] It is understood that the processing unit divides the gear's comprehensive load spectrum based on a preset acquisition time period (six times per minute), and determines the life curve by analyzing the load characteristics of each stage. The life slope reflects the trend of the gear's fatigue life under different load conditions. By monitoring the numerical changes in 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 adjust the life curve accordingly. This can obtain a target life curve that fits the actual working conditions, improving the flexibility and adaptability of the system. Using the target life curve as a benchmark, the working status 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.

[0061] In some embodiments of the present application, when the gear torque set and the gear stress set are preprocessed 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 processing missing values and data binning, the missing values are filled using a preset filling algorithm, the gear torque set and the gear stress set after processing the missing values are binned, and the data binning is performed using a preset binning algorithm.

[0062] Specifically, during the actual acquisition process of the acquisition unit, due to factors such as sensor failure and communication interference, missing values may appear in the gear torque set and gear stress concentration. The filling preset algorithm is based on the statistical characteristics of the data to perform filling. The filling preset algorithm includes mean, median or mode filling. You can select one of them according to the missing situation of the actual data. The data binning adopts the binning preset algorithm for binning. The binning preset algorithm adopts equal-width binning, that is, the data is divided into different intervals according to a fixed time width. For example, in the process of six acquisitions per minute, the data collected every 20 seconds can be divided into an interval. By processing missing values, the analysis deviation caused by incomplete data is avoided, the continuity and integrity of the data are guaranteed, and a good foundation is provided for subsequent data analysis. Data binning makes complex data intuitive and easy to handle. Different boxes can represent different working conditions or states, which improves the reliability and stability of the system for gear fatigue life monitoring.

[0063] In some embodiments of the present application, when determining a first contact stress load spectrum based on vehicle resistance data and gear data, and determining a second contact stress load spectrum based on a torque data set and a stress data set, it includes: a construction unit sets up a simulated environment, and determines speed input data based on the CLTC-P road condition, integrates the vehicle resistance data and gear data according to the simulated environment to determine a first m-file, determines a first contact stress load spectrum based on the first m-file and the speed input data, integrates the torque data set and the stress data set according to the simulated environment to determine a second m-file, and determines a second contact stress load spectrum based on the second m-file and the speed input data.

[0064] Specifically, the construction unit builds a simulation environment based on Matrix Laboratory (MATLAB). The Chinese Passenger Car Driving Cycle (CLTC-P) serves as a reference for electric vehicle speeds. CLTC-P, developed by the China Automotive Technology and Research Center and based on extensive test data, determines speed input data based on the CLTC-P road conditions. Vehicle resistance and gear data are integrated into the simulation environment to generate a first m-file. This file combines information such as the external resistance of the vehicle during driving and the gear's inherent characteristics. Using the speed input data and the first m-file, mechanical calculations within the simulation environment are performed to calculate the first contact stress load spectrum for the gear under different driving conditions. This first contact stress load spectrum reflects the contact stress of the gear under the influence of external resistance. Similarly, within the simulation environment, the torque and stress datasets are integrated to generate a second m-file. Based on this second m-file and the speed input data, a second contact stress load spectrum is generated. This second contact stress load spectrum represents the stress variations in the gear from the perspective of internal power transmission and force. The mechanical calculations involved in the simulation environment are based on the well-established and lengthy formula for gear contact stress, which will not be elaborated on here. By utilizing a simulated environment and CLTC-P road conditions, the load conditions of the vehicle during driving are 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 systematically and comprehensively understand the stress conditions of the gear and lays a data foundation for subsequent analysis and processing.

[0065] In some embodiments of the present application, when constructing a gear comprehensive load spectrum based on the first contact stress load spectrum and the second contact stress load spectrum, it includes: a construction unit merges the first contact stress load spectrum and the second contact stress load spectrum, and determines whether there is a non-overlapping area. When there is no non-overlapping area, the gear comprehensive load spectrum is constructed according to the merging result. When there is a non-overlapping area, the first contact stress load value corresponding to the first contact stress load spectrum in the preset acquisition time period in the non-overlapping area 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 area is obtained. The contact stress load average value is determined according to the first contact stress load value and the second contact stress load value, and the average value of all contact stress loads in the non-overlapping area is used as the area to be overlapped, and the gear comprehensive load spectrum is constructed according to the overlapping area and the area to be overlapped.

[0066] Specifically, the construction unit merges the first contact stress load spectrum and the second contact stress load spectrum. If an overlapping area appears during the merging process, the overlapping area will be retained. When a non-overlapping area appears, it indicates that the two contact stress load spectra cannot completely overlap. In the non-overlapping area, 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 area is obtained. The average contact stress load value is determined based on the first contact stress load value and the second contact stress load value, and the average contact stress load value of all contact stress loads in the non-overlapping area is used as the area to be overlapped. For example, in the time period from 3:21 to 3:22 in the non-overlapping area, the first contact stress load value of the first contact stress load spectrum is 300. MPa, 500MPa, 400MPa, 500MPa, 600MPa, 500MPa and the second contact stress load value of the second contact stress load spectrum is 600MPa, 500MPa, 400MPa, 400MPa, 300MPa, 500MPa. The average value of all contact stress loads in the time period of this non-overlapping area is 450MPa, 500MPa, 400MPa, 450MPa, 450MPa, 500MPa. According to the time period of this non-overlapping area and the average values of all contact stress loads, the existing areas to be overlapped and the overlapping areas are merged, so that the constructed gear comprehensive load spectrum can fully reflect the characteristics of gear contact stress, effectively integrate data from different sources, thereby improving the integrity and accuracy of the gear comprehensive load spectrum.

[0067] In some embodiments of the present application, when the comprehensive load spectrum of the gear is divided based on a preset acquisition time period and the life curve is determined 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, takes the average of all contact stress values corresponding to a load time period as the contact stress amplitude of the load time period, and determines the life curve according to each load time period and the corresponding contact stress amplitude.

[0068] In some embodiments of the present application, when determining a life curve based on each load time period and the corresponding contact stress amplitude, it includes: a processing unit determines an SN curve based on vehicle resistance data and gear data, determines the life value of the contact stress amplitude corresponding to each load time period according to the SN curve, and converts all life values into coordinate points, uses the load-time mean of each load time period as the X-axis coordinate value of the corresponding coordinate point, and uses the life value of each load time period as the Y-axis coordinate value of the corresponding coordinate point, determines a life coordinate system based on the X-axis coordinate value and the Y-axis coordinate value, and connects adjacent coordinate points to determine the life curve.

[0069] Specifically, the processing unit first evenly divides the preset collection time period into several load time periods, preferably three load time periods. The preset collection time period is evenly divided in order to more carefully analyze the stress conditions of the gears at different time stages. For example, in the time period from 4:30 to 4:31, the values of the comprehensive load spectrum are 550MPa, 500MPa, 600MPa, 750MPa, 550MPa, and 400MPa. This one-minute time period is evenly divided into three load time periods, and one of the load time periods in this time period is represented by 4:30:20. The average value of all contact stress values corresponding to it is (550MPa+500MPa) / 2=525MPa. Then, in the time period from 4:30:00 to 4:30:20, its contact stress amplitude is 525MPa. The SN curve is determined based on the vehicle resistance data and gear data. The SN curve reflects the fatigue life relationship of the material under different stress levels. Based on the SN curve, the corresponding life fatigue value for each contact stress amplitude can be determined. This life fatigue value is then used to measure the impact on gear life. When the gear is subjected to stress above the fatigue limit, each load cycle will cause a certain amount of damage to the gear, and this damage can accumulate. However, when the gear is subjected to stress at or above the fatigue limit, each load cycle may not cause damage. Therefore, the life fatigue value is calculated by subtracting the gear wear corresponding to the life fatigue value from the gear's total life. The gear wear corresponding to the life fatigue value is determined based on the gear's operating instructions and relevant experiments. A larger life value indicates a longer current gear life, while a smaller life value indicates a greater reduction in the gear's total life and a shorter gear life. By constructing a life coordinate system and connecting adjacent coordinate points, a life curve is formed. The life curve is determined by integrating multi-source data, improving the stability of gear monitoring.

[0070] In some embodiments of the present application, when obtaining the life slope of a life curve and determining whether to adjust the life slope based on the value of the life slope, it includes: a processing unit obtaining all life slopes in the life curve, and when there is no life slope greater than zero, determining the life curve as a target life curve; when there is a life slope greater than zero, determining to adjust the life slope, and determining the adjustment result and the unadjusted life slope as the target life curve.

[0071] In some embodiments of the present application, when determining an adjustment factor based on a historical data set and determining a target life curve based on the adjustment factor, it includes: a processing unit compares the life slope to be adjusted with the historical data set, and determines the adjustment factor based on the comparison result. The historical data set includes several historical life slopes and several historical adjustment factors. Each historical life slope corresponds to a historical adjustment factor. When there is a historical life slope that is the same as the life slope to be adjusted in the historical data set, 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 that is the same as the life slope to be adjusted in the historical data set, and the historical life slope is not unique, the average of the historical adjustment factors corresponding to each historical life slope is used as the adjustment factor. When there is no historical life slope that is the same as the life slope to be adjusted in the historical data set, the historical data set is divided, and the adjustment factor is determined based on the division result. The life slope to be adjusted is proportional to the adjustment factor.

[0072] Specifically, the life slope reflects the trend of gear fatigue life as a function of load and load duration. When all life slopes are less than zero, the life curve conforms to normal fatigue life variations and is therefore designated as the target life curve. However, if a life slope is greater than zero, this indicates an abnormal trend in the life curve—in other words, the gear fatigue life actually increases with increasing load duration—and therefore requires adjustment. The life slope to be adjusted is compared with a historical dataset. The historical dataset contains numerous historical life slopes and their corresponding historical adjustment factors. If a single historical life slope in the dataset matches the life slope to be adjusted, its corresponding historical adjustment factor is used as the current adjustment factor. If multiple historical life slopes match, the average of the corresponding historical adjustment factors is used to balance the impact of these factors. If no matching historical life slope exists in the historical dataset, the historical dataset is partitioned to ensure that the adjustment factor reflects the required life slope adjustment. By evaluating and adjusting the life slope, any abnormal trends in the life curve are eliminated, ensuring that the life curve truly reflects the variation of gear fatigue life with load duration.

[0073] It is understandable that by establishing a proportional relationship, when the life slope needs to be reduced, the life slope can be reduced according to the adjustment factor. Moreover, in the process of determining the adjustment factor, the historical data set is used to make full use of past empirical data. Whether it is directly matched or determined by dividing the historical data set, it can effectively combine the actual situation, so that the adjustment factor is consistent with the characteristics of the life slope, improving the stability of gear fatigue life monitoring, enhancing the system's ability to process life curves under various operating conditions, and improving the adaptability and reliability of the entire online monitoring system.

[0074] In some embodiments of the present application, when dividing the historical data set and determining the adjustment factor based on 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 that needs 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 that needs 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, divides 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 divides 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, and uses the average of the historical adjustment factors corresponding to each historical life slope in the third historical life set as the adjustment factor.

[0075] Specifically, in the historical data set, the first historical life set contains historical life slopes greater than the life slope that needs to be adjusted, while the second historical life set contains historical life slopes less than or equal to the life slope that needs to be adjusted. By calculating the average of these two sets, the central trend of the data in each set can be found. The historical life slopes in the first historical life set that are less than or equal to the first historical life average are numerically closer to the life slope that needs to be adjusted than 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 are also closer to the life slope that needs to be adjusted in magnitude and change trend. These two parts of data are determined as the third historical life set, so that the data in this set have a high degree of similarity in characteristics with the life slope that currently needs to be adjusted. The empirical laws of historical data are used to provide a basis for adjusting the life slope, ensuring that the adjustment factor matches the needs of the actual working conditions and avoiding adjustment deviations caused by using irrelevant or highly different historical data. The system can respond flexibly under different working conditions, effectively improving its adaptability to complex and changeable working conditions and ensuring the effectiveness of online monitoring of gear fatigue life.

[0076] In some embodiments of the present application, when determining whether to issue a warning alarm for the main reducer gear based on 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 that no warning alarm is issued, and when the target life value is less than or equal to the preset target life threshold, it is determined that a warning alarm is issued.

[0077] Specifically, the target life value is the minimum life value on the target life curve, while the preset target life value is dynamically set based on gear data and relevant experimental data. Comparing the target life value with the preset target life threshold enables quantitative monitoring of the final reducer gear condition, enabling timely detection of changes in gear fatigue status and improving the reliability of online monitoring.

[0078] In summary, the beneficial effects of the present invention are: by collecting automobile resistance data and gear data, and simultaneously obtaining the gear torque set and stress set and pre-processing, comprehensive and multi-dimensional data is provided. The first contact stress load spectrum is determined by combining the automobile resistance data and the gear data, and the second contact stress load spectrum is determined based on the torque data set and the stress data set, thereby constructing a comprehensive gear load spectrum, which effectively integrates data information of different dimensions and can comprehensively reflect the load condition of the gear in actual operation, changing the previous situation where the fatigue life monitoring of the main reducer gear 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. Based on the preset acquisition time period, the gear comprehensive load spectrum is divided to determine the life curve and life slope, and the life slope is dynamically adjusted according to the historical data set, thereby realizing dynamic monitoring of the gear fatigue life, and judging whether to issue a warning alarm based on the target life curve, thereby avoiding the risk of accidents caused by gear fatigue failure, and providing reliable safety protection for the operation of the vehicle.

[0079] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0081] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. An online monitoring system for fatigue life of electric vehicle main reducer gears, characterized in that: include: a collection unit configured to collect vehicle resistance data and gear data of a final drive gear within a preset collection time period, and obtain a gear torque set and a gear stress set within the same preset collection time period, and pre-process 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 based on 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 based on 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 a life slope of the life curve, determine whether to adjust the life slope according to a value of the life slope, and when it is determined that the life slope is to be adjusted, determine an adjustment factor according to a historical data set and determine a target life curve according to the adjustment factor; The monitoring unit is configured to determine whether to issue a warning alarm to the main reducer gear according to the target life curve.

2. The online monitoring system for fatigue life of the electric vehicle main reducer gear according to claim 1 is characterized in that: When the gear torque set and the gear stress set are preprocessed to respectively determine a torque data set and a stress data set, the method includes: The acquisition unit pre-processes the gear torque set to determine the torque data set, and pre-processes the gear stress set to determine the stress data set; The preprocessing includes processing missing values and data binning; The missing values are filled using a preset filling algorithm; The gear torque set and the gear stress set after processing the missing values are subjected to the data binning, and the data binning is performed using a preset binning algorithm.

3. The online monitoring system for fatigue life of the electric vehicle main reducer gear according to claim 2 is characterized in that: 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, the method includes: The construction unit builds a simulated environment and determines speed input data according to the CLTC-P road conditions; Integrating the vehicle resistance data and gear data according to the simulated environment to determine a first m-file, and determining the first contact stress load spectrum based on the first m-file and the speed input data; The torque data set and the stress data set are integrated according to the simulated environment to determine a second m-file, and the second contact stress load spectrum is determined based on the second m-file and the velocity input data.

4. The online monitoring system for fatigue life of the electric vehicle main reducer gear according to claim 3 is 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, the method includes: The construction unit combines the first contact stress load spectrum and the second contact stress load spectrum, and determines whether there is a non-overlapping area; When the non-overlapping area does not exist, constructing the gear comprehensive load spectrum according to the merging result; When the non-overlapping area exists, obtaining a first contact stress load value corresponding to a first contact stress load spectrum in a preset acquisition time period in the non-overlapping area, and obtaining a second contact stress load value corresponding to a second contact stress load spectrum in the preset acquisition time period in the non-overlapping area; The average contact stress load value is determined according to the first contact stress load value and the second contact stress load value, and the average contact stress load value of all the non-overlapping areas is used as the area to be overlapped, and the gear comprehensive load spectrum is constructed according to the overlapping area and the area to be overlapped.

5. The online monitoring system for fatigue life of the electric vehicle main reducer gear according to claim 4 is characterized in that: When the gear comprehensive load spectrum is divided based on the preset collection time period and the life curve is determined according to the division result, it includes: The processing unit evenly divides the preset acquisition time period into a plurality of load time periods, and determines corresponding contact stress values according to the plurality of load time periods; The average of all contact stress values corresponding to a load time period is used as the contact stress amplitude of the load time period, and the life curve is determined according to each load time period and the corresponding contact stress amplitude.

6. The online monitoring system for fatigue life of the electric vehicle main reducer gear according to claim 5 is 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 an SN curve based on the vehicle resistance data and gear data; Determine the life value of the contact stress amplitude corresponding to each load time period according to the SN curve, and convert all life values into coordinate points; The load time mean of each load time period is used as the X-axis coordinate value of the corresponding coordinate point, and the life value of each load time period is used as the Y-axis coordinate value of the corresponding coordinate point. The life coordinate system is determined according to the X-axis coordinate value and the Y-axis coordinate value, and the adjacent coordinate points are connected to determine the life curve.

7. The online monitoring system for fatigue life of the electric vehicle main reducer gear according to claim 6 is characterized in that: 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, the method includes: The processing unit obtains all life slopes in the life curve; When there is no lifespan slope greater than zero, determining the lifespan curve as the target lifespan curve; When there is a life slope greater than zero, it is determined that the life slope is to be adjusted, and the adjustment result and the life slope without adjustment are determined as the target life curve.

8. The online monitoring system for fatigue life of the electric vehicle main reducer gear according to claim 7 is characterized in that: When determining an adjustment factor based on a historical data set and determining a target life curve based on the adjustment factor, including: 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, wherein the historical data set includes a plurality of historical life slopes and a plurality 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 of the historical adjustment factors corresponding to the historical life slopes is used as the adjustment factor; When there is no historical life slope identical to the life slope to be adjusted in the historical data set, dividing the historical data set, and determining the adjustment factor according to the division result; The life slope that needs to be adjusted is proportional to the adjustment factor.

9. The online monitoring system for fatigue life of the electric vehicle main reducer gear according to claim 8, characterized in that: When the historical data set is divided and the adjustment factor is determined according to the division result, the following steps are included: The processing unit divides the historical life slopes in the historical data set that are greater than the life slope that needs to be adjusted into a first historical life set, and divides the historical life slopes in the historical data set that are less than or equal to the life slope that needs to be adjusted into a second historical life set; Obtaining a first historical life average value of the first historical life set, and obtaining a second historical life average value of the second historical life set; dividing the historical life slopes in the first historical life set that are less than or equal to the first historical life average into a third historical life set, and dividing the historical life slopes in the second historical life set that are greater than or equal to the second historical life average into the third historical life set; The average of the historical adjustment factors corresponding to the historical life slopes in the third historical life set is used as the adjustment factor.

10. The online monitoring system for fatigue life of the electric vehicle main reducer gear 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, the method includes: The monitoring unit determines a target life value based on the target life curve; When the target life value is greater than a preset target life threshold, determining not to issue the early warning alarm; When the target life value is less than or equal to a preset target life threshold, it is determined that the early warning alarm is issued.

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