A method, device and system for monitoring heat dissipation of a gear reducer

Abstract

The present invention relates to the field of temperature monitoring technology, and specifically to a method, device and system for monitoring heat dissipation of a gear reducer. The data similarity interval and cutoff point of each data point are dynamically determined from the temperature time series data to improve the accuracy of heat dissipation monitoring. Then, the temperature difference and interval overlap are comprehensively analyzed within the data similarity interval of each data point to calculate the initial abnormality degree value. Then, the initial abnormality degree value is corrected by using the difference between the local temperature change at the cutoff point and the local temperature change at the data point, as well as the cutoff of the data similarity interval, to obtain an updated abnormality degree value. Abnormal data points are screened out based on the updated abnormality degree value and the local temperature discreteness, and these data are used for model training to obtain an abnormality recognition model with high sensitivity and accuracy. Finally, the model is used to monitor the temperature of the gear reducer in real time to achieve accurate monitoring of the heat dissipation. This method improves the accuracy of the monitoring results.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature monitoring, and in particular to a heat dissipation monitoring method, device and system for a gear reducer. Background Art

[0002] As a crucial component of mechanical equipment, the stability of a gear reducer's operating state has a crucial impact on the performance of the entire mechanical system. During operation, the gear reducer's temperature fluctuates due to factors such as friction, load variations, and the operating environment. Abnormally high temperatures can cause performance degradation, damage, or even lead to safety accidents. Therefore, real-time monitoring of the gear reducer's temperature and the timely detection of temperature anomalies are crucial to ensuring its proper operation.

[0003] In recent years, with the rapid development of sensor and data processing technologies, intelligent monitoring methods based on the Internet of Things have gradually become a research hotspot. These methods not only monitor reducer temperature changes in real time, but also enable fault prediction and early warning through data analysis and cloud computing, providing a scientific basis for equipment maintenance and management.

[0004] When the existing technology uses sensor technology to monitor the heat dissipation of the gear reducer, a fixed temperature threshold is usually set. When the temperature data collected by the temperature sensor exceeds the temperature threshold, it is determined that the heat dissipation of the gear reducer is abnormal. However, due to the complex operating conditions of the gear reducer, the normal temperature range under different working conditions will also be different. Therefore, the temperature change of the gear reducer under different working conditions has a certain pattern and trend. Therefore, using a fixed temperature threshold is difficult to adapt to the temperature changes under different working conditions, which will affect the accuracy of the gear reducer heat dissipation monitoring results. Summary of the Invention

[0005] In order to solve the technical problem that the operating conditions of the gear reducer are complex, the normal temperature range under different working conditions will also be different. Therefore, the change of the gear reducer temperature under different working conditions has a certain pattern and trend; therefore, using a fixed temperature threshold is difficult to adapt to the temperature changes under different working conditions, thereby affecting the accuracy of the gear reducer heat dissipation monitoring results, the purpose of the present invention is to provide a gear reducer heat dissipation monitoring method, device and system, and the technical solutions adopted are as follows:

[0006] Get the temperature time series data of the gear reducer;

[0007] In the temperature time series data, the cutoff point corresponding to each data point is determined according to the fluctuation and change trend of the temperature value in the local range of each data point, thereby obtaining the data similarity interval corresponding to each data point;

[0008] Within the data similarity interval corresponding to each data point, the difference in temperature values ​​and the overlap of the data similarity intervals of all data points are analyzed to obtain the initial abnormality degree value of each data point; based on the difference in temperature value changes in the data similarity interval of each data point and the data similarity interval of the corresponding cutoff point, and combined with the cutoff of the data similarity interval of the data point, the initial abnormality degree value of each data point is corrected to obtain the updated abnormality degree value of each data point;

[0009] Based on the updated abnormality degree values ​​of all data points and the discreteness of the temperature values ​​in the data similarity intervals of all data points, abnormal data points are screened out from all data points; the model is trained according to the temperature changes of the abnormal data points, so that the temperature condition of the gear reducer at the current moment is monitored based on the trained abnormality recognition model.

[0010] Furthermore, the method for obtaining the cutoff point includes:

[0011] In the temperature time series data, any data point is selected as a point to be measured;

[0012] In terms of time series, the system starts from the point to be measured and traverses forward in sequence. During each traversal, the temperature fluctuations and change trends of all data points between the data point to be traversed and the point to be measured are analyzed to obtain the merging index corresponding to the data point to be traversed. If the merging index corresponding to the data point to be traversed meets the interval judgment condition, the data point to be traversed is merged into the data similarity interval of the data point to be measured and the traversal is continued; if the interval judgment condition is not met, the traversal is stopped and the data point to be traversed at this time is used as the cutoff point corresponding to the point to be measured.

[0013] The interval judgment condition is: the merging index is greater than a preset merging threshold.

[0014] Furthermore, the method for obtaining the combined index includes:

[0015] Get the timing curve of temperature timing data;

[0016] In each traversal process, the data point between the data point to be traversed and the previous data point of the data point to be measured in time sequence is taken as the target point, the absolute value of the temperature difference between each target point and the point to be measured is taken as the temperature deviation factor, and the average of all temperature deviation factors is taken as the temperature deviation value corresponding to the data point to be traversed;

[0017] Obtaining the temperature slope value at the data point to be traversed on the timing curve;

[0018] The product of the temperature slope value at the data point to be traversed and the temperature deviation value corresponding to the data point to be traversed is negatively correlated and normalized to obtain the value as the merging index corresponding to the data point to be traversed.

[0019] Furthermore, the method for obtaining the data similarity interval includes:

[0020] In terms of time series, the data points between the first data point after the cutoff point time series corresponding to the test point and the test point form a data similarity interval corresponding to the test point.

[0021] Furthermore, the method for obtaining the initial abnormality degree value includes:

[0022] In the data similarity interval corresponding to the point to be tested, the number of data points that are repeated at any moment in the data similarity interval of each data point and the data similarity interval of the point to be tested is taken as the overlapping length corresponding to each data point, and the difference between the length of the data similarity interval of the point to be tested and the overlapping length corresponding to each data point is taken as the length difference factor, and the mean of all length difference factors is taken as the initial abnormality factor of the point to be tested;

[0023] The difference between the maximum temperature value and the minimum temperature value in the data similarity interval corresponding to the test point is taken as the temperature range;

[0024] The product of the initial abnormality factor and the temperature extreme difference is normalized to a value obtained by normalizing the value, which is used as the initial abnormality degree value corresponding to the point to be measured.

[0025] Furthermore, the method for obtaining the updated abnormality degree value includes: for any data point, using the number of times the cutoff point corresponding to the data point is used as the cutoff point as a first adjustment factor;

[0026] Calculate the mean of all temperature values ​​in the data similarity interval of the data point as the first temperature eigenvalue;

[0027] The mean of all temperature values ​​in the data similarity interval of the cutoff point corresponding to the data point is used as the second temperature characteristic value;

[0028] Performing negative correlation mapping on the absolute value of the difference between the first temperature characteristic value and the second temperature characteristic value and normalizing the result as a second adjustment factor;

[0029] Normalizing the product of the first adjustment factor and the second adjustment factor as the adjustment coefficient corresponding to the initial abnormality value of the data point;

[0030] The product of the adjustment coefficient and the initial abnormality level value of the data point is normalized to a value obtained by the normalization, and the value is used as the updated abnormality level value of the data point.

[0031] Furthermore, the method for obtaining abnormal data points includes:

[0032] In the data similarity interval corresponding to each data point, the variance of all temperature values ​​is used as the discrete factor corresponding to each data point;

[0033] Constructing a coordinate system based on the discrete factors of the data points and the updated abnormality degree values, and mapping all data points to the coordinate system;

[0034] In the coordinate system, a preset radius and a preset minimum number are set, and cluster analysis is performed on all data points based on the DBSCAN algorithm to obtain all clusters;

[0035] In each cluster, the Euclidean norm of the mean of the discrete factors of all data points and the mean of the updated anomaly values ​​of all data points is calculated; the product of the Euclidean norm and the distribution density of the data points is normalized and used as the anomaly coefficient corresponding to each cluster;

[0036] Among all clusters, the data point in the cluster with the largest abnormal coefficient is regarded as an abnormal data point.

[0037] Furthermore, the model is trained according to the temperature change of the abnormal data point, thereby monitoring the temperature of the gear reducer at the current moment based on the trained abnormality recognition model, including:

[0038] In the temperature time series data, determining a preset neighborhood of each abnormal data point;

[0039] In the preset neighborhood of each abnormal data point, the variance of the temperature slope values ​​at all data points is calculated as the variation characteristic value of each abnormal data point;

[0040] Input the changing characteristic values ​​of all abnormal data points into the SVM algorithm and train it to obtain a trained anomaly recognition model;

[0041] In the preset neighborhood corresponding to the data point at the current moment, the change feature value of the data point at the current moment is calculated and the change feature value is input into the trained anomaly recognition model to output the monitoring result.

[0042] A heat dissipation monitoring device for a gear reducer includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, any one of the steps of a heat dissipation monitoring method for a gear reducer is implemented.

[0043] A heat dissipation monitoring system for a gear reducer, the system comprising:

[0044] A data acquisition module is used to obtain the temperature time series data of the gear reducer;

[0045] An interval partitioning module is used to determine the cutoff point corresponding to each data point in the temperature time series data according to the fluctuation and change trend of the temperature value in the local range of each data point, so as to obtain the data similarity interval corresponding to each data point;

[0046] The abnormality degree analysis module is used to analyze the difference in temperature values ​​within the data similarity interval corresponding to each data point and the overlap of the data similarity intervals of all data points to obtain the initial abnormality degree value of each data point; based on the difference in temperature value changes between the data similarity interval of each data point and the data similarity interval of the corresponding cutoff point, and combined with the cutoff of the data similarity interval of the data point, the initial abnormality degree value of each data point is corrected to obtain the updated abnormality degree value of each data point;

[0047] The heat dissipation monitoring module is used to filter out abnormal data points from all data points based on the updated abnormality degree values ​​of all data points and the discreteness of the temperature values ​​in the data similarity intervals of all data points; the model is trained according to the temperature changes of the abnormal data points, so as to monitor the temperature conditions of the gear reducer at the current moment based on the trained abnormality recognition model.

[0048] The present invention has the following beneficial effects:

[0049] First, the temperature time series data of the gear reducer is obtained. Since the temperature change of the gear reducer under different working conditions may be different, and the temperature change of the gear reducer during operation is relatively linear, the present invention dynamically determines the data similarity interval and cutoff point of each data point in the temperature time series data through the local temperature fluctuation and change trend of each data point, thereby preliminarily improving the accuracy of subsequent heat dissipation monitoring; then, within the data similarity interval corresponding to each data point, the difference in temperature values ​​and the overlap of data similarity intervals are comprehensively analyzed to more comprehensively capture the trend and pattern of temperature change, thereby calculating the initial abnormality value of each data point. Furthermore, since the local fluctuation of the temperature value at the cutoff point corresponding to each data point can reflect the abnormality of the cutoff point, thereby reflecting the abnormality degree of each data point, the present invention uses the difference between the local temperature change at the cutoff point corresponding to each data point and the local temperature change at each data point, as well as the cutoff of the data similarity interval of all data points, to correct the initial abnormality value of each data point, thereby obtaining an updated abnormality value for each data point. Next, based on the updated anomaly level of the data point and the local dispersion of the temperature values, anomalous data points are screened out. Since these anomalous data points are screened based on the local variation pattern of the temperature values, they are more accurate. Therefore, by using the temperature variation of anomalous data points for model training, the resulting anomaly recognition model will have greater sensitivity and accuracy. Finally, using the trained anomaly recognition model to monitor the current temperature of the gear reducer, more accurate monitoring results can be obtained, enabling real-time monitoring of the gear reducer's heat dissipation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 A flow chart of a method for monitoring heat dissipation of a gear reducer provided by one embodiment of the present invention;

[0052] Figure 2 A schematic structural diagram of a heat dissipation monitoring device for a gear reducer provided by one embodiment of the present invention;

[0053] Figure 3 This is a system block diagram of a heat dissipation monitoring system for a gear reducer provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0054] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method, device, and system for monitoring heat dissipation in a gear reducer according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0055] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0056] The following describes in detail a method, device and system for monitoring heat dissipation of a gear reducer provided by the present invention with reference to the accompanying drawings.

[0057] See also Figure 1 , which shows a method flow chart of a heat dissipation monitoring method for a gear reducer provided by one embodiment of the present invention, the method comprising the following steps:

[0058] Step S1: Acquire the temperature time series data of the gear reducer.

[0059] In modern industrial production, gear reducers are widely used in mechanical transmission systems. The reliability of their performance is directly related to the operating stability and production efficiency of the entire equipment. With the increase of working load, gear reducers are prone to generate excessively high temperatures during long-term operation, leading to lubricant degradation, increased gear wear, and even equipment failure. Therefore, real-time monitoring of the heat dissipation of gear reducers is particularly important.

[0060] Given that the operating conditions of the gear reducer are relatively complex and the normal temperature range under different operating conditions may vary, in an embodiment of the present invention, the heat dissipation of the gear reducer is monitored without relying on a fixed temperature threshold. Instead, the temperature change pattern and trend of the gear reducer are analyzed to identify abnormal data points, and then an abnormality recognition model is constructed to monitor the temperature of the gear reducer at the current moment.

[0061] First, it is necessary to obtain the temperature time series data of the gear reducer. The intelligent sensing system can be used to collect the temperature value of the gear reducer: install the temperature sensor at a suitable position that can accurately reflect the temperature change of the gear reducer, and then collect the data from the temperature sensor.

[0062] It should be noted that the frequency of data acquisition can be set to 50Hz, the horizontal axis of the collected temperature time series data is time, and the vertical axis is the temperature value; the appropriate position in the embodiment of the present invention can be set to the bearing housing, oil pan, bottom or top of the gear reducer, etc., and the specific position can be adjusted according to the implementation scenario and is not limited here; the collection length of the temperature time series data can be set to 10 minutes before the current time series, and the specific length can be adjusted according to the implementation scenario and is not limited here.

[0063] Step S2: In the temperature time series data, the cutoff point corresponding to each data point is determined according to the fluctuation and change trend of the temperature value in the local range of each data point, so as to obtain the data similarity interval corresponding to each data point.

[0064] Because the normal temperature range of the gear reducer will be different under different operating conditions, when monitoring the heat dissipation of the gear reducer, it is possible to determine whether the temperature is abnormal based on the long-term temperature change. Moreover, because the temperature change of the gear reducer during operation is relatively linear, when the temperature is abnormal, the data with the most reference value is usually the data that is relatively close in time series. Therefore, in an embodiment of the present invention, the fluctuation and change trend of the temperature value in the local range of each data point in the temperature time series data are analyzed to identify the characteristics of the temperature change, thereby determining a turning point of the temperature change in the local range as the cutoff point, thereby dividing the data similarity interval corresponding to each data point, that is, the interval where the temperature value fluctuates within a certain range and the change is relatively similar.

[0065] Preferably, in one embodiment of the present invention, the method for obtaining the cutoff point includes:

[0066] In order to facilitate subsequent explanation and illustration, a data point is selected as the measured point in the temperature time series data, and the method for obtaining the cutoff point corresponding to the measured point is described to explain the process of the cutoff point acquisition method.

[0067] In terms of time series, the system starts from the point to be measured and traverses forward in sequence. During each traversal, the temperature fluctuations and change trends of all data points between the traversed data point and the point to be measured are analyzed to obtain the merging index corresponding to the traversed data point. If the merging index corresponding to the traversed data point meets the interval judgment condition, it means that when the traversed data point is incorporated into the data similarity interval of the point to be measured, the fluctuations and change trends of all temperature values ​​in the data similarity interval will not change significantly. Therefore, the traversal data point is incorporated into the data similarity interval of the point to be measured, and the traversal is continued to find the cutoff point. If the interval judgment condition is not met, it means that the temperature value of the traversed data point does not conform to the temperature value change pattern in the data similarity interval of the point to be measured, then the traversal is stopped and the traversed data point at this time is used as the cutoff point corresponding to the point to be measured. Among them, the interval judgment condition is: the merging index is greater than the preset merging threshold.

[0068] The method for obtaining the combined index includes: firstly obtaining a time series curve of temperature time series data.

[0069] Then, in each traversal process, the data point between the data point to be traversed and the previous data point of the data point to be measured in the time series is taken as the target point, and the absolute value of the difference in temperature values ​​between each target point and the point to be measured is taken as the temperature deviation factor. The larger the temperature deviation factor, the more dissimilar the temperature values ​​between the target point and the point to be measured. The average of all temperature deviation factors is taken as the temperature deviation value corresponding to the data point to be traversed. The larger the temperature deviation value, the lower the similarity of the temperature values ​​in the data similarity interval corresponding to the point to be measured will be if the data point to be traversed is added to the data similarity interval of the point to be measured. Therefore, the possibility of the data point to be traversed being the cutoff point will be higher.

[0070] Then, the temperature slope value at the data point to be traversed is obtained on the timing curve. The slope value at the data point to be traversed represents the instantaneous temperature change at the data point to be traversed. Similarly, the larger the value, the higher the degree of instantaneous temperature change at the data point to be traversed, and the higher the possibility of the data point to be traversed as a cutoff point, that is, the lower the possibility of being incorporated into the data similarity interval of the test point.

[0071] Finally, the product of the temperature slope value at the data point to be traversed and the temperature deviation value corresponding to the data point to be traversed is negatively correlated and normalized to achieve logical relationship correction, and the value after negative correlation mapping and normalization is used as the merging index corresponding to the data point to be traversed. At this time, the larger the merging index, the more likely the data point to be traversed should be incorporated into the data similarity interval of the test point. Conversely, the smaller the merging index, the more likely the data point to be traversed should be used as the cutoff point of the data similarity interval of the test point.

[0072] It should be noted that the negative correlation mapping and normalization method in the embodiment of the present invention can adopt the exp(-x) function, where exp() represents an exponential function with the natural constant e as the base, and x represents the independent variable; the preset merging threshold is set to 0.61, and the specific value can be adjusted according to the actual scenario, and is not limited here.

[0073] Based on the above process, the cutoff point corresponding to each data point in the temperature time series data can be obtained, and the cutoff point represents the end position of the data similarity interval division of each data point. Therefore, the data similarity interval of each data point can be determined based on the cutoff point.

[0074] Preferably, the method for obtaining data similarity intervals includes:

[0075] Taking the test point as an example, the data points between the first data point after the cutoff point and the test point (including the test point) are combined into a data similarity interval corresponding to the test point. In this case, the temperature values ​​in the data similarity interval of each data point have a relatively consistent change pattern.

[0076] Here, an example is given to illustrate the process of obtaining the above cutoff point and data similarity interval: the length of the temperature time series data is 10, and the data point to be traversed is selected as data point 9. Then, in the first traversal, data point 8 is used as the first data point to be traversed, and data point 8 is also used as the target point. The merging index corresponding to the data point to be traversed 8 is calculated. If the merging index is greater than the preset merging threshold, then data point 8 and the data point to be traversed 9 together constitute the data similarity interval of the data point to be traversed 9; then the second traversal process is performed, and the data point to be traversed at this time is data point 7, and the target point is data point 8 and the data point to be traversed 7. The merging index of the data point to be traversed is calculated. The merging index corresponding to the traversed data point 7 is calculated. If the merging index is still greater than the preset merging threshold at this time, the data points 7, 8 and the point to be tested 9 together constitute the data similarity interval of the point to be tested 9; then the third traversal process is performed. At this time, the data point to be traversed is data point 6, and the target point is data points 7, 8 and the data point to be traversed 6. The merging index corresponding to the data point to be traversed 6 is calculated. If the merging index is less than or equal to the preset merging threshold at this time, the data point to be traversed 6 is used as the cutoff point of the point to be tested 9. At the same time, the data similarity interval of the point to be tested 9 is obtained, which is composed of data points 7, 8 and the point to be tested 9.

[0077] Step S3: Within the data similarity interval corresponding to each data point, analyze the difference in temperature values ​​and the overlap of the data similarity intervals of all data points to obtain the initial abnormality degree value of each data point; based on the difference in temperature value changes between the data similarity interval of each data point and the data similarity interval of the corresponding cutoff point, and combined with the cutoff of the data similarity interval of the data point, correct the initial abnormality degree value of each data point to obtain an updated abnormality degree value of each data point.

[0078] In step S2, the data similarity interval of each data point can be obtained, and the temperature values ​​in the data similarity interval all have relatively similar change patterns. Therefore, due to this connection between the states of temperature values ​​that are close in time, the overlap of the similarity intervals of the data point can be analyzed in the data similarity interval of a certain data point as an indicator for quantifying the initial abnormality value of the data point; at the same time, the difference in temperature values ​​in the data similarity interval can also be used as an indicator for evaluating whether the data point is abnormal or not. Furthermore, since the division of the data similarity interval depends on the fluctuation and change trend of the temperature value in the local range, the temperature change of the data similarity interval of the cutoff point and the cutoff of the data similarity interval of all data points can also be used as an indicator for evaluating the abnormality of the data point. Therefore, the initial abnormality value of the data point is corrected using the above-mentioned characteristics to obtain the updated abnormality value of each data point. The updated abnormality value can more accurately describe the abnormality of each data point.

[0079] Preferably, in one embodiment of the present invention, the method for obtaining the initial abnormality degree value includes:

[0080] Select any data point as the test point. Within the data similarity interval corresponding to the test point, take the number of data points that are repeated in the data similarity interval of each data point and the data similarity interval of the test point as the overlap length corresponding to each data point. The longer the overlap length, the higher the similarity between the test point and the data similarity interval of each data point in the interval, which may indicate that the test point is within a normal temperature fluctuation range. Here, an example is given to illustrate the method of obtaining the overlap length: for example, the test point is data point 9, and the data points in its data similarity interval are data points 5, 6, 7, 8, and 9 respectively. For data point 8, if the data points in its data similarity interval are data points 4, 5, 6, 7, and 8 respectively, then the data points that are repeated in the data similarity interval of data point 8 and the data similarity interval of the test point 9 are data points 5, 6, 7, and 8, and the corresponding overlap length is 4.

[0081] Then, the difference between the length of the data similarity interval of the test point and the overlapping length corresponding to each data point is taken as the length difference factor, and the mean of all length difference factors is taken as the initial anomaly factor of the test point; the data points in the data similarity interval of the test point can be regarded as the similar data set of the test point. When the corresponding length difference factor between the test point and the data points in the similar data set is larger, it means that the degree of deviation between the test point and its similar data is larger, then the larger the initial anomaly factor, the greater the difference between the test point and its similar data set, and the higher the degree of anomaly of the test point.

[0082] Then, the difference between the maximum temperature value and the minimum temperature value in the data similarity interval corresponding to the test point is taken as the temperature range. The larger the temperature range, the more severe the temperature fluctuation in the data similarity interval corresponding to the test point is, which also suggests that the test point may be in an abnormal temperature change situation.

[0083] Finally, the product of the initial anomaly factor and the temperature extremes is normalized to obtain the value of the initial anomaly degree corresponding to the test point. A larger initial anomaly degree value indicates a higher degree of anomaly at the test point. Normalization is a well-known technique for those skilled in the art, and the normalization function can be linear normalization or standard normalization. The specific normalization method is not limited here.

[0084] When calculating the initial abnormality degree value, it mainly depends on the difference in temperature values ​​in the data similarity interval and the overlap of the data similarity intervals of the data points; further, the change in temperature value between each data point and the corresponding cutoff point can also reflect the abnormality of the data point. Therefore, in an embodiment of the present invention, based on the difference in temperature value change between the data similarity interval of each data point and the data similarity interval of the corresponding cutoff point, and combined with the cutoff conditions of the data similarity intervals of all data points, the initial abnormality degree value of each data point is corrected to obtain an updated abnormality degree value for each data point.

[0085] Preferably, in one embodiment of the present invention, the method for obtaining the updated abnormality degree value includes:

[0086] For any data point, the number of times the cutoff point corresponding to the data point is used as the cutoff point is counted, and the number is used as the first adjustment factor; when a data point is used as the cutoff point multiple times, it is considered that the degree of abnormality of this cutoff point is higher. Therefore, when the first adjustment factor corresponding to the cutoff point of a data point is larger, it is considered that the degree of abnormality of the cutoff point corresponding to the data point is larger.

[0087] Then, the mean of all temperature values ​​in the data similarity interval of the data point is calculated as the first temperature characteristic value. By calculating the mean, some random fluctuations can be smoothed out, so that the first temperature characteristic value more accurately reflects the average temperature state of the gear reducer within the local time range of the data point.

[0088] Similarly, the mean of all temperature values ​​in the data similarity interval of the cutoff point corresponding to the data point is taken as the second temperature characteristic value, which reflects the average temperature state of the gear reducer within the local time range of the cutoff point corresponding to the data point.

[0089] When the deviation between the first temperature characteristic value and the second temperature characteristic value is large, it indicates that the similarity between the average temperature state at the data point and the corresponding cutoff point in the local range is low; conversely, when the deviation between the first temperature characteristic value and the second temperature characteristic value is small, it is considered that the similarity between the average temperature state at the data point and the corresponding cutoff point in the local range is high; the absolute value of the difference between the first temperature characteristic value and the second temperature characteristic value is negatively correlated and normalized to the value obtained as the second adjustment factor. In this case, the larger the second adjustment factor, the more similar the average temperature change state between the data point and the corresponding cutoff point is. Given that the cutoff point is a point that differs from the data point, the larger the second adjustment factor, the greater the degree of abnormality of the data point. Among them, the negative correlation mapping and normalization method can use the exp(-x) function, where exp() represents an exponential function with the natural constant e as the base, and x represents the independent variable.

[0090] Next, the first and second adjustment factors are comprehensively analyzed. Since a larger first adjustment factor indicates a more abnormal cutoff point, and a larger second adjustment factor indicates a more consistent change in the local temperature state between a data point and the corresponding cutoff point, the product of the first and second adjustment factors is normalized and used as the adjustment coefficient corresponding to the initial abnormality value of the data point. The larger the adjustment coefficient corresponding to the data point, the higher the degree of abnormality.

[0091] Finally, the product of the adjustment coefficient and the initial anomaly value of the data point is normalized to obtain the updated anomaly value for the data point. A larger updated anomaly value indicates a higher degree of temperature anomaly at that data point. Normalization is a technique well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0092] Step S4: Based on the updated abnormality degree values ​​of all data points and the discreteness of the temperature values ​​in the data similarity intervals of all data points, abnormal data points are screened from all data points; the model is trained according to the temperature changes of the abnormal data points, so as to monitor the temperature condition of the gear reducer at the current moment based on the trained abnormality recognition model.

[0093] Based on the above steps, the updated abnormality degree value of each data point can be obtained as an indicator for evaluating the abnormality of a single data point. In an embodiment of the present invention, by further analyzing the updated abnormality degree values ​​of all data points and combining the discreteness of the temperature values ​​in the data similarity intervals of all data points, outlier abnormal data points can be identified more accurately; then, an abnormality recognition model is trained based on the temperature changes of the identified outlier abnormal data points, and finally, the temperature condition of the gear reducer at the current moment is monitored based on the trained abnormality recognition model.

[0094] Preferably, in one embodiment of the present invention, the method for obtaining abnormal data points includes:

[0095] In the data similarity interval corresponding to each data point, the variance of all temperature values ​​is used as the discrete factor corresponding to each data point. The discrete factor is used to measure the degree of dispersion of the temperature values ​​in the data similarity interval of the data point, and the larger the value, the greater the degree of dispersion.

[0096] A coordinate system is constructed based on the discrete factor of the data point and the updated abnormality value. The discrete factor is used as the horizontal coordinate and the updated abnormality value is used as the vertical coordinate to construct the coordinate system, and all data points are mapped to the coordinate system. At this time, each data point can be represented by the coordinate of (discrete factor, updated abnormality value).

[0097] In this coordinate system, a preset radius and a preset minimum number are set, and cluster analysis is performed on all data points based on the DBSCAN algorithm to obtain all clusters. Through cluster analysis, data points with the same state or pattern can be grouped into the same category. It should be noted that the preset radius and the preset minimum number are 3, and the specific values ​​can be adjusted according to the implementation scenario and are not limited here. The DBSCAN algorithm is well known, and the specific process is not detailed here.

[0098] Then, in each cluster, the Euclidean norm between the mean of the discrete factors of all data points and the mean of the updated abnormality values ​​of all data points is calculated. The larger the Euclidean norm, the higher the temperature discreteness of the data points in the cluster, and the higher the abnormality, so the possibility that the data point in the cluster is the final abnormal data point is higher; since the distribution density of the data points in each cluster can reflect the distribution characteristics between the data points, the distribution density of all data points in each cluster is calculated. In an embodiment of the present invention, the distribution density is calculated as follows: in the coordinate system, the average value of the distance between all data points in each cluster is used as the distribution density, wherein the distance of the data point quality inspection is calculated based on the coordinates of the two data point quality inspections. The larger the distribution density, the more discrete the distribution of the data points in the cluster, which means that it is more likely to be an outlier data point and the higher the abnormality.

[0099] Next, the product of the Euclidean norm and the distribution density of the data points in each cluster is normalized to obtain the value corresponding to each cluster as the anomaly coefficient. A larger anomaly coefficient indicates a higher degree of anomaly for the data points in the cluster. Normalization is a technique well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0100] Finally, among all clusters, the data point in the cluster with the largest abnormal coefficient is regarded as an abnormal data point.

[0101] Based on the above process, all abnormal data points in the temperature time series data can be obtained. Since the temperature time series data is a period of data before the current time series, the abnormality recognition model can be trained based on the temperature change of the abnormal data points, and the abnormality recognition model can be used to monitor the temperature of the gear reducer at the current moment, thereby improving the accuracy of the heat dissipation monitoring results.

[0102] Preferably, in one embodiment of the present invention, a model is trained according to the temperature change of abnormal data points, so that the temperature condition of the gear reducer at the current moment is monitored based on the trained abnormality recognition model, including:

[0103] First, the preset neighborhood of each abnormal data point is determined.

[0104] Then, in the preset neighborhood of each abnormal data point, the variance of the temperature slope values ​​at all data points is calculated as the variation characteristic value of each abnormal data point. The variation characteristic value quantifies the temperature change fluctuation of the data points around each abnormal data point. This feature is the key feature for identifying abnormal patterns.

[0105] Then, the changing characteristic values ​​of all abnormal data points are input into the SVM algorithm and trained so that the SVM algorithm can learn the characteristics of the abnormal data points, thereby obtaining a trained anomaly recognition model.

[0106] Finally, in the preset neighborhood corresponding to the data point at the current moment, the change characteristic value of the data point at the current moment is calculated, and the change characteristic value is input into the trained anomaly recognition model to output the monitoring result.

[0107] When the monitoring result is abnormal, an early warning is required to notify the staff to inspect and repair the gear reducer; when the monitoring result is normal, no early warning is required.

[0108] It should be noted that the preset neighborhood is to take each abnormal data point as the starting point and take the previous 5 data points in time series. The specific number can be adjusted according to the implementation scenario and is not limited here. The training process of the SVM algorithm is a well-known technology and will not be described in detail here.

[0109] In summary, first, the temperature time series data of the gear reducer is obtained. Since the temperature change of the gear reducer under different working conditions may be different, and the temperature change of the gear reducer during operation is relatively linear, the present invention dynamically determines the data similarity interval and cutoff point of each data point in the temperature time series data through the local temperature fluctuation and change trend of each data point, thereby preliminarily improving the accuracy of subsequent heat dissipation monitoring; then, within the data similarity interval corresponding to each data point, the difference in temperature values ​​and the overlap of data similarity intervals are comprehensively analyzed to more comprehensively capture the trend and pattern of temperature change, thereby calculating the initial abnormality value of each data point. Furthermore, since the local fluctuation of the temperature value at the cutoff point corresponding to each data point can reflect the abnormality of the cutoff point, thereby reflecting the abnormality degree of each data point, the present invention uses the difference between the local temperature change at the cutoff point corresponding to each data point and the local temperature change at each data point, as well as the cutoff of the data similarity interval of all data points, to correct the initial abnormality value of each data point, thereby obtaining an updated abnormality value for each data point. Next, based on the updated anomaly level of the data point and the local dispersion of the temperature values, anomalous data points are screened out. Since these anomalous data points are screened based on the local variation pattern of the temperature values, they are more accurate. Therefore, by using the temperature variation of anomalous data points for model training, the resulting anomaly recognition model will have greater sensitivity and accuracy. Finally, using the trained anomaly recognition model to monitor the current temperature of the gear reducer, more accurate monitoring results can be obtained, enabling real-time monitoring of the gear reducer's heat dissipation.

[0110] An embodiment of the present invention also provides a heat dissipation monitoring device for a gear reducer, including a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, the steps of a heat dissipation monitoring method for a gear reducer are implemented.

[0111] See also Figure 2, which shows a structural schematic diagram of a heat dissipation monitoring device for a gear reducer provided by an embodiment of the present invention, including a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected via the bus 202; wherein the memory 201 may include a high-speed random access memory, the bus 202 may be an ISA bus, a PCI bus or an EISA bus, etc., and the processor 200 may be an integrated circuit chip with signal processing capabilities; the memory 201 stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps in a heat dissipation monitoring method for a gear reducer are implemented.

[0112] The embodiment of the present invention also provides a heat dissipation monitoring system for a gear reducer, see Figure 3 , which shows a system block diagram of a heat dissipation monitoring system for a gear reducer, the system includes: a data acquisition module 301, used to obtain the temperature time series data of the gear reducer; an interval division module 302, used to determine the cutoff point corresponding to each data point in the temperature time series data according to the fluctuation and change trend of the temperature value in the local range of each data point, thereby obtaining the data similarity interval corresponding to each data point; an abnormality degree analysis module 303, used to analyze the difference in temperature values ​​and the overlap of the data similarity intervals of all data points in the data similarity interval corresponding to each data point, and obtain the initial abnormality degree value of each data point Based on the difference in the temperature value changes between the data similarity interval of each data point and the data similarity interval of the corresponding cutoff point, and combined with the cutoff of the data similarity interval of the data point, the initial abnormality value of each data point is corrected to obtain the updated abnormality value of each data point; the heat dissipation monitoring module 304 is used to screen abnormal data points from all data points based on the updated abnormality values ​​of all data points and the discreteness of the temperature values ​​in the data similarity intervals of all data points; the model is trained according to the temperature changes of the abnormal data points, so as to monitor the temperature condition of the gear reducer at the current moment based on the trained abnormality recognition model.

[0113] It should be noted that the system provided in the above embodiment is merely an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the gear reducer heat dissipation monitoring system and the gear reducer heat dissipation monitoring method provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0114] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A heat dissipation monitoring method for a gear reducer, characterized in that: The method comprises: Get the temperature time series data of the gear reducer; In the temperature time series data, the cutoff point corresponding to each data point is determined according to the fluctuation and change trend of the temperature value in the local range of each data point, thereby obtaining the data similarity interval corresponding to each data point; Within the data similarity interval corresponding to each data point, the difference in temperature values ​​and the overlap of the data similarity intervals of all data points are analyzed to obtain the initial abnormality degree value of each data point; based on the difference in temperature value changes in the data similarity interval of each data point and the data similarity interval of the corresponding cutoff point, and combined with the cutoff of the data similarity interval of the data point, the initial abnormality degree value of each data point is corrected to obtain the updated abnormality degree value of each data point; Based on the updated abnormality degree values ​​of all data points and the discreteness of the temperature values ​​in the data similarity intervals of all data points, abnormal data points are screened from all data points; the model is trained according to the temperature changes of the abnormal data points, and the temperature condition of the gear reducer at the current moment is monitored based on the trained abnormality recognition model; The method for obtaining the update abnormality degree value includes: For any data point, the number of times the cutoff point corresponding to the data point is used as the cutoff point is used as the first adjustment factor; Calculate the mean of all temperature values ​​in the data similarity interval of the data point as the first temperature eigenvalue; The mean of all temperature values ​​in the data similarity interval of the cutoff point corresponding to the data point is used as the second temperature characteristic value; Performing negative correlation mapping on the absolute value of the difference between the first temperature characteristic value and the second temperature characteristic value and normalizing the result as a second adjustment factor; Normalizing the product of the first adjustment factor and the second adjustment factor as the adjustment coefficient corresponding to the initial abnormality value of the data point; Normalizing the product of the adjustment coefficient and the initial abnormality level value of the data point to obtain a value as the updated abnormality level value of the data point; The method for obtaining abnormal data points includes: In the data similarity interval corresponding to each data point, the variance of all temperature values ​​is used as the discrete factor corresponding to each data point; Constructing a coordinate system based on the discrete factors of the data points and the updated abnormality degree values, and mapping all data points to the coordinate system; In the coordinate system, a preset radius and a preset minimum number are set, and cluster analysis is performed on all data points based on the DBSCAN algorithm to obtain all clusters; In each cluster, the Euclidean norm of the mean of the discrete factors of all data points and the mean of the updated anomaly values ​​of all data points is calculated; the product of the Euclidean norm and the distribution density of the data points is normalized and used as the anomaly coefficient corresponding to each cluster; Among all clusters, the data point in the cluster with the largest abnormal coefficient is regarded as an abnormal data point.

2. The heat dissipation monitoring method of a gear reducer according to claim 1, characterized in that: The method for obtaining the cutoff point comprises: In the temperature time series data, any data point is selected as a point to be measured; In terms of time series, the system starts from the point to be measured and traverses forward in sequence. During each traversal, the temperature fluctuations and change trends of all data points between the data point to be traversed and the point to be measured are analyzed to obtain the merging index corresponding to the data point to be traversed. If the merging index corresponding to the data point to be traversed meets the interval judgment condition, the data point to be traversed is merged into the data similarity interval of the data point to be measured and the traversal is continued; if the interval judgment condition is not met, the traversal is stopped and the data point to be traversed at this time is used as the cutoff point corresponding to the point to be measured. The interval judgment condition is: the merging index is greater than a preset merging threshold.

3. The heat dissipation monitoring method of a gear reducer according to claim 2, characterized in that: The method for obtaining the combined index includes: Get the timing curve of temperature timing data; In each traversal process, the data point between the data point to be traversed and the previous data point of the data point to be measured in time sequence is taken as the target point, the absolute value of the temperature difference between each target point and the point to be measured is taken as the temperature deviation factor, and the average of all temperature deviation factors is taken as the temperature deviation value corresponding to the data point to be traversed; Obtaining the temperature slope value at the data point to be traversed on the timing curve; The product of the temperature slope value at the data point to be traversed and the temperature deviation value corresponding to the data point to be traversed is negatively correlated and normalized to obtain the value as the merging index corresponding to the data point to be traversed.

4. The heat dissipation monitoring method of a gear reducer according to claim 2, characterized in that: The method for obtaining the data similarity interval includes: In terms of time series, the data points between the first data point after the cutoff point time series corresponding to the test point and the test point form a data similarity interval corresponding to the test point.

5. The heat dissipation monitoring method of a gear reducer according to claim 2, characterized in that: The method for obtaining the initial abnormality degree value includes: In the data similarity interval corresponding to the point to be tested, the number of data points that are repeated at any moment in the data similarity interval of each data point and the data similarity interval of the point to be tested is taken as the overlapping length corresponding to each data point, and the difference between the length of the data similarity interval of the point to be tested and the overlapping length corresponding to each data point is taken as the length difference factor, and the mean of all length difference factors is taken as the initial abnormality factor of the point to be tested; The difference between the maximum temperature value and the minimum temperature value in the data similarity interval corresponding to the test point is taken as the temperature range; The product of the initial abnormality factor and the temperature extreme difference is normalized to a value obtained by normalizing the value, which is used as the initial abnormality degree value corresponding to the point to be measured.

6. The heat dissipation monitoring method of a gear reducer according to claim 3, characterized in that: The model is trained according to the temperature change of the abnormal data point, thereby monitoring the temperature of the gear reducer at the current moment based on the trained abnormality recognition model, including: In the temperature time series data, determining a preset neighborhood of each abnormal data point; In the preset neighborhood of each abnormal data point, the variance of the temperature slope values ​​at all data points is calculated as the variation characteristic value of each abnormal data point; Input the changing characteristic values ​​of all abnormal data points into the SVM algorithm and train it to obtain a trained anomaly recognition model; In the preset neighborhood corresponding to the data point at the current moment, the change feature value of the data point at the current moment is calculated and the change feature value is input into the trained anomaly recognition model to output the monitoring result.

7. A heat dissipation monitoring device for a gear reducer, characterized in that: It includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, the steps of a heat dissipation monitoring method for a gear reducer as described in any one of claims 1 to 6 are implemented.

8. A heat dissipation monitoring system for a gear reducer, implementing the steps of the heat dissipation monitoring method for a gear reducer as claimed in claim 1, characterized in that: The system comprises: A data acquisition module is used to obtain the temperature time series data of the gear reducer; An interval partitioning module is used to determine the cutoff point corresponding to each data point in the temperature time series data according to the fluctuation and change trend of the temperature value in the local range of each data point, so as to obtain the data similarity interval corresponding to each data point; The abnormality degree analysis module is used to analyze the difference in temperature values ​​within the data similarity interval corresponding to each data point and the overlap of the data similarity intervals of all data points to obtain the initial abnormality degree value of each data point; based on the difference in temperature value changes between the data similarity interval of each data point and the data similarity interval of the corresponding cutoff point, and combined with the cutoff of the data similarity interval of the data point, the initial abnormality degree value of each data point is corrected to obtain the updated abnormality degree value of each data point; The heat dissipation monitoring module is used to filter out abnormal data points from all data points based on the updated abnormality degree values ​​of all data points and the discreteness of the temperature values ​​in the data similarity intervals of all data points; the model is trained according to the temperature changes of the abnormal data points, so as to monitor the temperature conditions of the gear reducer at the current moment based on the trained abnormality recognition model.

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