Gear random fatigue load processing method and system based on GCM counting method

Abstract

The invention relates to the technical field of load spectrum analysis, and discloses a gear random fatigue load processing method and system based on a GCM counting method.The method comprises the steps that actual operation load data and actual operation environment data of a gear transmission system are collected, and torque is converted into instantaneous contact stress; dividing into a plurality of discrete cells based on discretization processing; carrying out clustering analysis on all discrete cells; constructing a two-dimensional gear counting matrix; the asymmetric cyclic stress is converted into equivalent symmetric cyclic stress, and the total service life of the gear is determined; comparing the equivalent symmetric cyclic stress with cyclic stress under a raindrop counting method to determine an adjustment coefficient, and determining an early warning threshold value according to the total service life of the gear and the adjustment coefficient; and comparing the total fatigue damage value with an early warning threshold value to determine an early warning grade. According to the method, joint statistics of the stress amplitude, the average stress and the environmental characteristics is achieved, and the defects of a raindrop counting method in the aspects of processing meshing discontinuous loads and lacking environmental adaptability are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of load spectrum analysis, and more particularly, to a method and system for processing random fatigue loads of gears based on the GCM counting method. Background Art

[0002] As a core component in a mechanical transmission system, a gear usually bears complex and variable random loads during its service life. Especially in application scenarios such as vehicles, wind power, and construction machinery, transmission gears operate under conditions of alternating multiple working conditions, non-constant torque, frequent start-stop, etc., which are prone to cause contact fatigue failure. Accurately predicting the fatigue life of gears under such random loads is an important basis for improving the reliability of gear systems and realizing intelligent maintenance.

[0003] In the prior art, the rainflow counting method is generally used to statistically analyze the stress loads of gears. This method is based on the peak-valley pairs of the stress-time curve, and by identifying complete cycles and statistically analyzing the stress amplitude and frequency, combined with the material S-N curve for fatigue life estimation. The rainflow counting method is applicable to components with continuous and regular stress changes. However, for a gear pair with intermittent characteristics during the meshing process, the actual force on the gear during meshing is in a periodic intermittent state, while the rainflow counting method treats the stress as a continuous waveform, which is likely to overestimate the fatigue damage frequency and lead to deviation in life assessment; and it does not introduce the actual influence of environmental variables such as temperature and humidity on the fatigue strength of materials, resulting in a lack of environmental adaptability in life prediction; it is unable to effectively separate and model the stress effects under different temperature and humidity environments, and it is difficult to achieve working condition-sensitive life prediction and hierarchical early warning.

[0004] Therefore, it is necessary to design a method and system for processing random fatigue loads of gears based on the GCM counting method to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a method for processing random fatigue loads of gears based on the GCM counting method, aiming to solve the problems of ignoring the discreteness of the gear meshing process, not considering operating environment factors, and lacking a partition modeling mechanism for multi-condition fatigue damage in the current situation.

[0006] On the one hand, the present invention proposes a method for processing random fatigue loads of gears based on the GCM counting method, including: Collect the actual operating load data and actual operating environment data of the gear transmission system. The actual operating load data is a torque-time change curve, and the actual operating environment data includes temperature and humidity-time change curves. Convert the torque in the torque-time change curve into the instantaneous contact stress on the gear tooth surface; Based on discretization processing, the continuous contact stress is divided into a number of discrete small intervals, and each of the discrete small intervals corresponds to a time segment and corresponds to a temperature and humidity data; Perform cluster analysis on all the discrete small intervals according to the temperature and humidity data to determine a number of cluster sets; Collect the gear rotation speed and obtain the number of gear rotations within each of the cluster sets according to the gear rotation speed, record it as the occurrence frequency of this stress level, and construct a two-dimensional gear counting matrix, where the two-dimensional gear counting matrix includes stress amplitude, mean stress, and corresponding frequency; Convert the asymmetric cyclic stress into an equivalent symmetric cyclic stress using the Goodman life curve, and determine the total gear life according to the S-N curve of the gear material and the equivalent symmetric cyclic stress; Compare the equivalent symmetric cyclic stress with the cyclic stress under the rainflow counting method, determine an adjustment coefficient according to the comparison result, and determine a warning threshold according to the total gear life and the adjustment coefficient; Obtain the total fatigue damage value under the action of all levels of equivalent stress according to the two-dimensional gear counting matrix, compare the total fatigue damage value with the warning threshold, and determine the warning level according to the comparison result.

[0007] Further, when converting the torque in the torque-time change curve into the instantaneous contact stress on the gear tooth surface, it includes: ; wherein, represents the instantaneous contact stress on the tooth surface at time t, represents the circumferential force, represents the pitch diameter, represents the contact tooth width, represents the contact stress size coefficient, represents the elastic influence coefficient, represents the helix angle correction coefficient, represents the contact ratio correction coefficient, represents the application load coefficient, represents the dynamic load coefficient, represents the load distribution coefficient in the tooth direction.

[0008] Further, when dividing the continuous contact stress into a number of discrete small intervals based on discretization processing, it includes: The time interval of the discretization processing is inversely proportional to the sampling frequencies of the torque sensor, temperature sensor, and humidity sensor.

[0009] Further, when performing cluster analysis on all the discrete small intervals according to the temperature and humidity data to determine a number of cluster sets, it includes: Extract the temperature and humidity characteristics of each discrete small interval to form a two-dimensional point set; Determine the initial neighborhood radius through the k-distance graph and set MinPts to 4; determine ε through the k-distance graph method; Randomly select an unclassified point, find all the points within its ε neighborhood. If the number of points in the neighborhood is greater than or equal to MinPts, form a new class and continuously expand it; if the number of points in the neighborhood is less than MinPts, mark the point as an isolated point until all points are classified or marked.

[0010] Further, after performing clustering analysis on all the discrete small intervals according to the temperature and humidity data and determining several clustering sets, it further includes: If the number of instantaneous contact stresses in the clustering set is at least two, determine the representative instantaneous contact stress of the clustering set based on kernel density estimation; ; Wherein, represents the representative instantaneous contact stress, represents the number of instantaneous contact stresses in the clustering set, represents the smoothing bandwidth, represents the value of a certain frequency point to be estimated, represents the i-th instantaneous contact stress in the clustering set.

[0011] Further, the smoothing bandwidth is calculated through the following formula: ; Wherein, represents the smoothing bandwidth, represents the standard deviation of the data in the clustering set, represents the data skewness, represents the data kurtosis, represents the adjustment coefficient.

[0012] Further, when converting the asymmetric cyclic stress to the equivalent symmetric cyclic stress by using the Goodman fatigue life curve, it includes: ; Wherein, represents the representative instantaneous contact stress of the clustering set, represents the mean stress of the clustering set, represents the tensile ultimate strength of the gear material, represents the corrected equivalent symmetric stress.

[0013] Further, when comparing the equivalent symmetric cyclic stress with the cyclic stress under the rainflow counting method, determining an adjustment coefficient according to the comparison result, and determining a warning threshold according to the total gear life and the adjustment coefficient, it includes: Obtaining a ratio coefficient according to the equivalent symmetric cyclic stress and the cyclic stress under the rainflow counting method, where the ratio coefficient is the ratio of the equivalent symmetric cyclic stress to the cyclic stress under the rainflow counting method; When the ratio coefficient is less than or equal to 1, determining an adjustment coefficient according to the ratio coefficient, where the adjustment coefficient is directly proportional to the ratio coefficient, and the value range of the adjustment coefficient is (0.7, 0.9); When the ratio coefficient is greater than 1, determining an adjustment coefficient according to the ratio coefficient, where the adjustment coefficient is directly proportional to the ratio coefficient, and the value range of the adjustment coefficient is (0, 0.7).

[0014] Further, when comparing the total fatigue damage value with the warning threshold and determining a warning level according to the comparison result, it includes: Obtaining the remaining life of the gear according to the total fatigue damage value and the total gear life, and obtaining a life difference according to the remaining life of the gear and the warning threshold, where the life difference is the difference between the remaining life of the gear and the warning threshold; Determining a warning level according to the life difference, where the warning level is inversely proportional to the life difference.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: Through the gear random fatigue load processing method based on the GCM counting method, combining the actual operating load with environmental factors (temperature, humidity), using discretization and clustering analysis means to perform environmental perception modeling on stress segments under different working conditions, realizing the joint statistics of stress amplitude, mean stress and environmental characteristics, and inversely calculating the fatigue frequency under each clustering working condition through the gear speed, establishing a multi-dimensional gear counting matrix, thus overcoming the deficiencies of the rainflow counting method in dealing with meshing intermittent loads and lacking environmental adaptability; correcting the asymmetric cyclic stress through the Goodman life curve, evaluating the total gear life in combination with the S-N curve, and then forming an adjustment coefficient by comparing with the results of the rain method, realizing the dynamic and refined setting of the warning threshold, making the fatigue damage assessment more in line with the actual working conditions, and having higher reliability and intelligent warning capabilities.

[0016] On the other hand, the present application also provides a gear random fatigue load processing system based on the GCM counting method for applying the above gear random fatigue load processing method based on the GCM counting method, including: The acquisition unit is configured to acquire the actual operating load data and actual operating environment data of the gear transmission system. The actual operating load data is a torque-time change curve, and the actual operating environment data includes temperature and humidity-time change curves. The torque in the torque-time change curve is converted into the instantaneous contact stress of the gear tooth surface; The first processing unit is configured to divide the continuous contact stress into a number of discrete small intervals based on discretization processing. Each discrete small interval corresponds to a time segment and corresponds to a temperature and humidity data; The analysis unit is configured to perform clustering analysis on all the discrete small intervals according to the temperature and humidity data to determine a number of clustering sets; The second processing unit acquires the gear rotation speed and obtains the number of gear rotations within each clustering set according to the gear rotation speed, records it as the occurrence frequency of the stress level of the clustering set, and constructs a two-dimensional gear counting matrix. The two-dimensional gear counting matrix includes stress amplitude, mean stress, and corresponding frequency; The third processing unit is configured to convert the asymmetric cyclic stress into an equivalent symmetric cyclic stress by using the Goodman life curve, determine the total gear life according to the S-N curve of the gear material and the equivalent symmetric cyclic stress; compare the equivalent symmetric cyclic stress with the cyclic stress under the rainflow counting method, determine the adjustment coefficient according to the comparison result, and determine the warning threshold according to the total gear life and the adjustment coefficient; The warning unit is configured to obtain the total fatigue damage value under the action of all levels of equivalent stress according to the two-dimensional gear counting matrix, compare the total fatigue damage value with the warning threshold, and determine the warning level according to the comparison result.

[0017] It can be understood that the above-mentioned gear random fatigue load processing method and system based on the GCM counting method have the same beneficial effects and will not be elaborated here. Brief Description of the Drawings

[0018] 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. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 It is a flowchart of the gear random fatigue load processing method based on the GCM counting method provided by the embodiment of the present invention; Figure 2 It is a functional block diagram of the gear random fatigue load processing system based on the GCM counting method provided by the embodiment of the present invention. Detailed Embodiments

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be 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. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0020] In some embodiments of the present application, referring to Figure 1 as shown, a method for processing random fatigue loads of gears based on the GCM counting method includes: S100: Collect the actual operating load data and actual operating environment data of the gear transmission system. The actual operating load data is a torque-time change curve, and the actual operating environment data includes temperature and humidity-time change curves. Convert the torque in the torque-time change curve into the instantaneous contact stress on the gear tooth surface.

[0021] S200: Based on discretization processing, divide the continuous contact stress into several discrete small intervals. Each discrete small interval corresponds to a time segment and corresponds to a temperature and humidity data.

[0022] S300: Perform cluster analysis on all discrete small intervals according to the temperature and humidity data to determine several cluster sets.

[0023] S400: Collect the gear rotation speed and obtain the number of gear rotations within each cluster set according to the gear rotation speed, record it as the occurrence frequency of the stress level of the cluster set, and construct a two-dimensional gear counting matrix. The two-dimensional gear counting matrix includes stress amplitude, mean stress, and corresponding frequency.

[0024] S500: Convert the asymmetric cyclic stress into an equivalent symmetric cyclic stress using the Goodman life curve, and determine the total life of the gear according to the S-N curve of the gear material and the equivalent symmetric cyclic stress. Compare the equivalent symmetric cyclic stress with the cyclic stress under the rainflow counting method, determine the adjustment coefficient according to the comparison result, and determine the warning threshold according to the total life of the gear and the adjustment coefficient.

[0025] S600: Obtain the total fatigue damage value under the action of all levels of equivalent stress according to the two-dimensional gear counting matrix, compare the total fatigue damage value with the warning threshold, and determine the warning level according to the comparison result.

[0026] Specifically, in step S100, the actual operating load data and operating environment data of the gear transmission system are collected. The operating load data is a torque-time change curve, and the environment data includes temperature-time and humidity-time change curves. Through the gear contact stress conversion model, the time-varying torque is converted into the instantaneous contact stress of the gear tooth surface, forming the contact stress time history. In step S200, the contact stress time series is discretized. The continuous stress history is divided into multiple small intervals, and each small interval is associated with an independent time segment, and then bound to the temperature and humidity data at the corresponding time points, preparing the data basis for subsequent environmental clustering. In step S300, a two-dimensional environmental feature vector is constructed according to the temperature and humidity data corresponding to each time segment. The density-based clustering analysis algorithm (such as DBSCAN) is used to cluster all small intervals, and a clustering set with similar environmental characteristics is identified. Each set represents a typical operating environment condition. In step S400, the rotational speed information of the gear is collected in real time, and the number of gear rotations within each clustering set is calculated according to the rotational speed and the length of each time segment. The clustering result is combined with the contact stress amplitude and the mean stress to construct a two-dimensional gear count matrix (Gear Count Matrix, GCM), where the matrix contains each stress amplitude, mean stress and their corresponding frequencies. In step S500, the Goodman life curve is used to correct the asymmetric stress cycle, convert it into an equivalent symmetric cycle stress, and determine the total life of the gear under the current load spectrum based on the constructed equivalent stress and the S-N curve (stress-life curve) of the gear material. At the same time, the equivalent stress obtained by the GCM method is compared with the stress cycle parameters obtained by the traditional rainflow counting method, the differences between the two are analyzed, and an adjustment coefficient is obtained to correct the environmental or modeling deviation in the life assessment result, and then the fatigue damage warning threshold is determined. In step S600, the fatigue damage values under each stress level are statistically calculated according to the two-dimensional gear count matrix, and summed up according to the Miner linear cumulative damage theory to obtain the total fatigue damage value. The total damage value is compared with the adjusted warning threshold, and then the fatigue risk level of the current gear state is determined, and the corresponding warning result is output to realize the monitoring of the fatigue deterioration trend and the management of the remaining life.

[0027] It can be understood that by combining the load data actually collected during the gear service process with environmental information such as temperature and humidity, and using the clustering analysis and the rotational speed-based frequency statistics method, a two-dimensional gear count matrix adaptable to multiple working conditions is constructed, which truly reflects the stress evolution characteristics under different environments. Through the adjustment coefficient obtained by the Goodman life correction, the comparison between the GCM and the rainflow counting method, the dynamic correction and adaptive optimization of the fatigue life assessment model are realized, and the life prediction accuracy and warning rationality of the gear system under random loads and variable working conditions are improved.

[0028] In some embodiments of the present application, when converting the torque in the torque-time change curve into the instantaneous contact stress of the gear tooth surface, it includes: 。

[0029] Among them, represents the instantaneous contact stress of the tooth surface at time t, represents the circumferential force, represents the pitch diameter, represents the contact tooth width, represents the contact stress size factor, represents the elastic influence coefficient, represents the helix angle correction coefficient, represents the contact ratio correction coefficient, represents the applied load coefficient, represents the dynamic load coefficient, represents the load distribution coefficient in the tooth direction.

[0030] It can be understood that, among them, , T(t) represents the torque at time t. The contact stress size factor reflects the influence of gear size on stress distribution; the elastic influence coefficient is related to parameters such as the elastic modulus and Poisson's ratio of the meshing gear pair; the helix angle correction coefficient is used to correct the deviation of the meshing state of helical gears (this value is 1 for spur gears); the contact ratio correction coefficient represents the adjustment effect of the actual number of meshing teeth on the contact area; the applied load coefficient represents the correction caused by additional load fluctuations under actual working conditions; the dynamic load coefficient reflects the influence of fluctuations during the dynamic transmission of the load; the load distribution coefficient in the tooth direction is used to correct the uniformity of the load distribution along the tooth width. In this embodiment, by combining structural geometry, material properties, and load correction coefficients, the contact stress time history borne during the actual meshing process of the gear can be more realistically reconstructed. Compared with the traditional method of simplifying torque into a constant stress input, it can improve the physical accuracy and time resolution of the stress spectrum, provide more realistic basic data for subsequent fatigue life prediction, and further improve the accuracy and applicability of the entire GCM counting method processing system under complex and variable load conditions. It is applicable to gear fatigue modeling scenarios in typical high-load and variable-load environments such as automotive final drives and wind power main shaft gearboxes.

[0031] In some embodiments of the present application, when dividing the continuous contact stress into several discrete small intervals based on discretization processing, it includes: The time interval of the discretization processing is inversely proportional to the sampling frequencies of the torque sensor, temperature sensor, and humidity sensor.

[0032] Specifically, during the operation of the gear system, the contact stress is a function derived from the torque change, and its continuous time history needs to be discretized and divided into multiple small segments for subsequent stress frequency statistics and environmental data association. In implementation, to ensure that the contact stress change in the discrete interval can be approximately regarded as constant, and to ensure that each small interval accurately corresponds to a set of real-time temperature and humidity data. The discretization time step of the stress spectrum is adjusted according to the sampling capacity of various sensors, so that the sampled load-environmental data has a consistent time resolution, which is convenient for constructing an accurate "stress-temperature-humidity" three-dimensional joint data matrix, providing a unified data structure support for subsequent clustering analysis and counting modeling.

[0033] It can be understood that driving the discrete time interval with the sensor sampling frequency can effectively coordinate the synchronization accuracy of load data and environmental data in the time dimension, avoid time deviation or stress-environment pairing errors caused by inconsistent sampling rates of different sensors, and thus improve the consistency of data processing and modeling accuracy.

[0034] In some embodiments of the present application, cluster analysis is performed on all discrete cells according to temperature and humidity data to determine a number of cluster sets, including: The temperature and humidity characteristics of each discrete interval are extracted to form a two-dimensional point set.

[0035] The initialization neighborhood radius is determined by the k-distance graph, and MinPts is determined to be 4. ε is determined by the k-distance graph method.

[0036] Randomly select an unclassified point and find all the points in its ε neighborhood. If the number of points in the neighborhood is greater than or equal to MinPts, a new class is formed and continuously expanded. If the number of points in the neighborhood is less than MinPts, the point is marked as an isolated point until all points are classified or marked.

[0037] In some embodiments of the present application, after cluster analysis is performed on all discrete small intervals based on temperature and humidity data to determine several cluster sets, it also includes: if the number of instantaneous contact stresses in the cluster set is at least two, determining the characterizing instantaneous contact stress of the cluster set based on kernel density estimation.

[0038] .

[0039] in, represents the instantaneous contact stress, represents the number of instantaneous contact stresses in the cluster set, represents the smoothing bandwidth, Indicates the value of a frequency point to be estimated. represents the i-th instantaneous contact stress in the cluster set.

[0040] In some embodiments of the present application, the smoothing bandwidth is obtained by the following formula: .

[0041] Wherein, represents the smoothing bandwidth, represents the standard deviation of the data in the clustering set, represents the data skewness, represents the data kurtosis, represents the adjustment coefficient.

[0042] Specifically, . . Wherein, represents the mean value of the data in the clustering set.

[0043] It can be understood that the minimum point number parameter MinPts = 4 is set; the Euclidean distance from each point to its 4th nearest neighbor is calculated, and the 4th neighbor distances of all points are sorted in ascending order to form a k-distance curve; the neighborhood radius ε is determined according to the "inflection point position" of the curve. Subsequently, the clustering process is performed: a non-classified point is randomly selected; if the number of points included in its ε-neighborhood is ≥ MinPts, then this point is used as a core point to create a new cluster and recursively expand this cluster; if the number of neighborhood points < MinPts, then this point is marked as an isolated point; this process is repeated until all points are classified or marked, and finally several clustering sets are output. Through the density-based clustering method and the kernel density estimation model, the influence of different temperature and humidity environments on the gear stress spectrum can be effectively distinguished, and statistically representative contact stress characteristics can be extracted under each clustering environment, avoiding stress estimation deviation caused by the interference of extreme values or outliers, and realizing more stable and accurate environmental condition stress modeling. When facing complex non-Gaussian distributed stress data, the flexibility and adaptability of stress density estimation are improved through the adaptive bandwidth control mechanism.

[0044] In some embodiments of the present application, when converting the asymmetric cyclic stress into an equivalent symmetric cyclic stress by using the Goodman life curve, it includes: .

[0045] Wherein, represents the representative instantaneous contact stress of the clustering set, represents the mean stress of the clustering set, represents the ultimate tensile strength of the gear material, represents the corrected equivalent symmetric stress.

[0046] In some embodiments of the present application, when comparing the equivalent symmetric cyclic stress with the cyclic stress under the rainflow counting method and determining the adjustment coefficient according to the comparison result, and determining the warning threshold according to the total gear life and the adjustment coefficient, it includes: obtaining a ratio coefficient according to the equivalent symmetric cyclic stress and the cyclic stress under the rainflow counting method, where the ratio coefficient is the ratio of the equivalent symmetric cyclic stress to the cyclic stress under the rainflow counting method.

[0047] Specifically, when the ratio coefficient is less than or equal to 1, the adjustment coefficient is determined according to the ratio coefficient. The adjustment coefficient is directly proportional to the ratio coefficient, and the value range of the adjustment coefficient is (0.7, 0.9). When the ratio coefficient is greater than 1, the adjustment coefficient is determined according to the ratio coefficient. The adjustment coefficient is directly proportional to the ratio coefficient, and the value range of the adjustment coefficient is (0, 0.7).

[0048] It can be understood that when the ratio coefficient is less than or equal to 1, it indicates that the stress predicted by the rainflow counting method is greater and the result is more conservative, and the adjustment coefficient can appropriately relax the warning threshold. When the ratio coefficient is greater than 1, it indicates that the stress predicted by the GCM method is greater and the life is shorter, and it is necessary to improve the warning sensitivity and set the adjustment coefficient to tighten the warning threshold. By correcting the asymmetric load stress to the equivalent symmetric stress, it is ensured that the stress parameters used for fatigue life estimation are unified in the effective domain of the S-N curve, and the physical rationality of stress-life matching is improved. On this basis, the comparative analysis with the result obtained by the rainflow counting method is introduced, and the dynamic adjustment coefficient is determined based on the stress ratio, so that the final warning threshold can take into account the differences and respective advantages of the two load modeling methods, and realize a more adaptive and working condition sensitive life warning mechanism. The accuracy of fatigue assessment of gears under multiple load sources and multiple working conditions is improved, and the response ability of the warning strategy to the real damage evolution process is enhanced.

[0049] In some embodiments of the present application, when comparing the total fatigue damage value with the warning threshold and determining the warning level according to the comparison result, it includes: obtaining the remaining life of the gear according to the total fatigue damage value and the total gear life, obtaining the life difference according to the remaining life of the gear and the warning threshold, where the life difference is the difference between the remaining life of the gear and the warning threshold. The warning level is determined according to the life difference, and the warning level is inversely proportional to the life difference.

[0050] It is understandable that the life difference is obtained by comparing the remaining life with the set fatigue warning threshold. The fatigue warning threshold is usually calculated by historical experience, safety margin or dynamic adjustment coefficient, which represents the minimum remaining life required to trigger the warning. On this basis, the warning level is divided according to the life difference, and it is preferred to set the warning level inversely proportional to the life difference. By introducing the life difference, a dynamic and quantitative safety margin evaluation mechanism is established between the fatigue damage accumulation and the life threshold. It enhances the perception sensitivity and response flexibility of the gear fatigue risk, which can effectively extend the gear service life and reduce the risk of failure downtime.

[0051] In the above embodiment, the gear random fatigue load processing method based on the GCM counting method is used to combine the actual operating load with environmental factors (temperature, humidity), and the stress fragments under different working conditions are modeled by environmental perception using discretization and cluster analysis methods, so as to achieve the joint statistics of stress amplitude, average stress and environmental characteristics, and infer the fatigue frequency under each clustering working condition through the gear speed, and establish a multi-dimensional gear counting matrix, thereby overcoming the shortcomings of the raindrop counting method in dealing with meshing intermittent loads and lack of environmental adaptability. The asymmetric cyclic stress is corrected by the Goodman life curve, and the total life of the gear is evaluated in combination with the SN curve, and then the adjustment coefficient is formed by comparing with the raindrop method results, so as to achieve dynamic and refined warning threshold setting, so that the fatigue damage assessment is more in line with the actual working conditions, and has higher reliability and intelligent warning capabilities.

[0052] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a gear random fatigue load processing system based on the GCM counting method, which is used to apply the above-mentioned gear random fatigue load processing method based on the GCM counting method, including: The acquisition unit is configured to acquire actual operating load data and actual operating environment data of the gear transmission system. The actual operating load data is a torque-time variation curve. The actual operating environment data includes a temperature and humidity-time variation curve. The torque in the torque-time variation curve is converted into the instantaneous contact stress of the gear tooth surface.

[0053] The first processing unit is configured to divide the continuous contact stress into a plurality of discrete small intervals based on discretization processing, each discrete small interval corresponds to a time segment and corresponds to a temperature and humidity data.

[0054] The analysis unit is configured to perform cluster analysis on all discrete cells according to the temperature and humidity data to determine a number of cluster sets.

[0055] The second processing unit collects the gear rotation speed and obtains the number of gear rotations within each clustering set based on the gear rotation speed, records it as the occurrence frequency of the stress level of the clustering set, and constructs a two-dimensional gear counting matrix. The two-dimensional gear counting matrix includes stress amplitude, mean stress, and the corresponding frequency.

[0056] The third processing unit is configured to convert the asymmetric cyclic stress into an equivalent symmetric cyclic stress using the Goodman life curve, and determine the total gear life based on the S-N curve of the gear material and the equivalent symmetric cyclic stress. Compare the equivalent symmetric cyclic stress with the cyclic stress under the rainflow counting method, determine the adjustment coefficient according to the comparison result, and determine the warning threshold based on the total gear life and the adjustment coefficient.

[0057] The warning unit is configured to obtain the total fatigue damage value under the action of all levels of equivalent stress according to the two-dimensional gear counting matrix, compare the total fatigue damage value with the warning threshold, and determine the warning level according to the comparison result.

[0058] It can be understood that through the gear random fatigue load processing method based on the GCM counting method, the actual operating load is combined with environmental factors (temperature, humidity), and the stress segments under different working conditions are modeled for environmental perception by means of discretization and clustering analysis, realizing the joint statistics of stress amplitude, mean stress, and environmental characteristics, and inversely deducing the fatigue frequency under each clustering working condition through the gear rotation speed, establishing a multi-dimensional gear counting matrix, thus overcoming the deficiencies of the rainflow counting method in dealing with meshing intermittent loads and lacking environmental adaptability. The asymmetric cyclic stress is corrected by the Goodman life curve, and the total gear life is evaluated in combination with the S-N curve. Then, by comparing with the results of the rainflow method to form an adjustment coefficient, a dynamic and refined warning threshold is set, making the fatigue damage assessment more in line with the actual working conditions and having higher reliability and intelligent warning capabilities.

[0059] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. 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.

[0060] This 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 flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. 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 implementing the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or in one or more blocks.

[0061] These computer program instructions can 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 generate a manufactured article including instruction means that implement the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or in one or more blocks.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or in one or more blocks.

[0063] 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 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 without departing 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. A method for processing random fatigue loads of gears based on the GCM counting method, characterized in that, Including: Collecting the actual operating load data and actual operating environment data of the gear transmission system, where the actual operating load data is a torque-time change curve, and the actual operating environment data includes temperature and humidity-time change curves, and converting the torque in the torque-time change curve into the instantaneous contact stress of the gear tooth surface; Based on discretization processing, dividing the continuous contact stress into several discrete small intervals, and each of the discrete small intervals corresponds to a time segment and corresponds to a temperature and humidity data; Performing cluster analysis on all the discrete small intervals according to the temperature and humidity data to determine several cluster sets; Collecting the gear speed and obtaining the number of gear rotations within each of the cluster sets according to the gear speed, recording it as the occurrence frequency of the stress level of the cluster set, and constructing a two-dimensional gear counting matrix, where the two-dimensional gear counting matrix includes stress amplitude, mean stress, and corresponding frequency; Converting the asymmetric cyclic stress into an equivalent symmetric cyclic stress using the Goodman life curve, and determining the total gear life according to the S-N curve of the gear material and the equivalent symmetric cyclic stress; comparing the equivalent symmetric cyclic stress with the cyclic stress under the rainflow counting method, determining an adjustment coefficient according to the comparison result, and determining an early warning threshold according to the total gear life and the adjustment coefficient; Obtaining the total fatigue damage value under the action of all levels of equivalent stress according to the two-dimensional gear counting matrix, comparing the total fatigue damage value with the early warning threshold, and determining the early warning level according to the comparison result.

2. The method for processing random fatigue load of gears based on the GCM counting method according to claim 1, wherein When converting the torque in the torque-time change curve into the instantaneous contact stress of the gear tooth surface, it includes: ； Among them, represents the instantaneous contact stress of the tooth surface at time t, represents the circumferential force, represents the pitch diameter, represents the contact tooth width, represents the contact stress size factor, represents the elastic influence coefficient, represents the helix angle correction coefficient, represents the contact ratio correction coefficient, represents the application load factor, represents the dynamic load factor, represents the load distribution coefficient in the tooth direction.

3. The method for processing random fatigue load of gears based on the GCM counting method according to claim 1, characterized in that, When dividing the continuous contact stress into several discrete small intervals based on discretization processing, it includes: The time interval of the discretization processing is inversely proportional to the sampling frequencies of the torque sensor, temperature sensor, and humidity sensor.

4. The gear random fatigue load processing method based on the GCM counting method according to claim 1, wherein When performing cluster analysis on all the discrete small intervals according to the temperature and humidity data to determine several cluster sets, it includes: Extracting the temperature and humidity characteristics of each of the discrete small intervals to form a two-dimensional point set; Determining the initial neighborhood radius through the k-distance graph and determining MinPts as 4; determining ε through the k-distance graph method; Randomly selecting an unclassified point, finding all the points within its ε neighborhood. If the number of points within the neighborhood is greater than or equal to MinPts, forming a new class and continuously expanding; if the number of points within the neighborhood is less than MinPts, marking the point as an isolated point until all points are classified or marked.

5. The gear random fatigue load processing method based on the GCM counting method according to claim 4, characterized in that After performing cluster analysis on all the discrete small intervals according to the temperature and humidity data to determine several cluster sets, it further includes: If the number of instantaneous contact stresses in the cluster set is at least two, determining the representative instantaneous contact stress of the cluster set based on kernel density estimation; ； Among them, represents the instantaneous contact stress,[ represents the number of instantaneous contact stresses in the clustering set,[ represents the smoothing bandwidth,[ represents the value of a certain frequency point to be estimated,[ represents the i-th instantaneous contact stress in the clustering set.[ 6. The method for processing random fatigue load of gears based on the GCM counting method according to claim 5, wherein, The smoothing bandwidth is calculated through the following formula: ； Among them, represents the smoothing bandwidth, represents the standard deviation of the data in the clustering set, represents the data skewness, represents the data kurtosis, represents the adjustment coefficient.

7. The method for processing random fatigue load of gears based on the GCM counting method according to claim 6, wherein When converting the asymmetric cyclic stress into an equivalent symmetric cyclic stress using the Goodman life curve, it includes: ； Among them, represents the instantaneous contact stress of the clustering set, represents the average stress of the clustering set, represents the tensile ultimate strength of the gear material, represents the corrected equivalent symmetric stress.

8. The method for processing random fatigue load of gears based on the GCM counting method according to claim 7, characterized in that, When comparing the equivalent symmetric cyclic stress with the cyclic stress under the rainflow counting method and determining the adjustment coefficient according to the comparison result, and determining the warning threshold according to the total gear life and the adjustment coefficient, it includes: Obtaining a ratio coefficient based on the equivalent symmetric cyclic stress and the cyclic stress under the rainflow counting method, where the ratio coefficient is the ratio of the equivalent symmetric cyclic stress to the cyclic stress under the rainflow counting method; When the ratio coefficient is less than or equal to 1, determining the adjustment coefficient according to the ratio coefficient, where the adjustment coefficient is directly proportional to the ratio coefficient, and the value range of the adjustment coefficient is (0.7, 0.9); When the ratio coefficient is greater than 1, determining the adjustment coefficient according to the ratio coefficient, where the adjustment coefficient is directly proportional to the ratio coefficient, and the value range of the adjustment coefficient is (0, 0.7).

9. The method for processing the random fatigue load of gears based on the GCM counting method according to claim 8, wherein, When comparing the total fatigue damage value with the warning threshold and determining the warning level according to the comparison result, it includes: Obtaining the remaining life of the gear based on the total fatigue damage value and the total gear life, and obtaining the life difference based on the remaining life of the gear and the warning threshold, where the life difference is the difference between the remaining life of the gear and the warning threshold; Determining the warning level according to the life difference, where the warning level is inversely proportional to the life difference.

10. A gear random fatigue load processing system based on the GCM counting method, which is used to apply the gear random fatigue load processing method based on the GCM counting method according to any one of claims 1-9, and is characterized in that, It includes: A collection unit configured to collect the actual operating load data and actual operating environment data of the gear transmission system. The actual operating load data is a torque-time change curve, and the actual operating environment data includes temperature and humidity-time change curves, and converting the torque in the torque-time change curve into the instantaneous contact stress of the gear tooth surface; A first processing unit configured to divide the continuous contact stress into a number of discrete small intervals based on discretization processing, and each discrete small interval corresponds to a time segment and corresponds to a temperature and humidity data; An analysis unit configured to perform clustering analysis on all the discrete small intervals according to the temperature and humidity data to determine a number of clustering sets; A second processing unit collects the gear rotation speed and obtains the number of gear rotations within each clustering set according to the gear rotation speed, records the occurrence frequency of the stress level of the clustering set, and constructs a two-dimensional gear counting matrix, where the two-dimensional gear counting matrix includes stress amplitude, mean stress, and corresponding frequency; A third processing unit configured to convert the asymmetric cyclic stress into an equivalent symmetric cyclic stress by using the Goodman life curve, and determine the total gear life according to the S-N curve of the gear material and the equivalent symmetric cyclic stress; compare the equivalent symmetric cyclic stress with the cyclic stress under the rainflow counting method, determine the adjustment coefficient according to the comparison result, and determine the warning threshold according to the total gear life and the adjustment coefficient; A warning unit configured to obtain the total fatigue damage value under the action of all levels of equivalent stress according to the two-dimensional gear counting matrix, compare the total fatigue damage value with the warning threshold, and determine the warning level according to the comparison result.

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