Bearing detection system and method of automobile generator

Abstract

The invention discloses a bearing detection system and method for an automobile generator, and relates to the technical field of motors, and the method comprises the steps: carrying out the data collection through executing a sensor array, and obtaining a historical data set of the sensor array for monitoring a target bearing in a preset historical window; and traversing the historical data set to carry out data stability analysis, and determining a vibration stability factor, a temperature stability factor and an acoustic emission signal stability factor. And performing centralized search in the bearing detection configuration space by taking each stability factor as an index, and determining a target detection step length. And carrying out bearing state detection on the target bearing according to a preset detection item set based on the target detection step length to obtain a periodic detection data set, and identifying the periodic detection data set by using a bearing detection identifier to obtain a target bearing detection result. The technical problems of low detection efficiency and poor detection accuracy of the bearing detection method of the automobile generator in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of motor technology, and in particular to a bearing detection system and method for an automobile generator. Background Art

[0002] Bearings are common key components in mechanical equipment, and their operating status has an important impact on the stability and service life of the equipment. However, since bearings are easily affected by factors such as vibration, temperature and wear during long-term operation, they are prone to failure. Traditional bearing monitoring methods mostly rely on a single data source such as vibration data or temperature data, which makes it difficult to fully and accurately evaluate the operating status of bearings. In recent years, it has become a trend to collect bearing operation data through a variety of sensor arrays. This method combines multi-dimensional data such as vibration, temperature and acoustic emission signals, and can more comprehensively reflect the operating status of bearings. However, the bearing status detection method based on a variety of sensor arrays in the prior art still has the problems of low detection efficiency and poor detection accuracy.

[0003] Therefore, the bearing detection method of the automobile generator in the prior art has the technical problems of low detection efficiency and poor detection accuracy. Summary of the invention

[0004] The present application solves the technical problems of low detection efficiency and poor detection accuracy of bearing detection methods for automobile generators in the prior art by providing a bearing detection system and method for automobile generators. By dynamically optimizing the detection step length of sensor data acquisition, the system can acquire data according to the optimal acquisition step length when executing data acquisition, thereby realizing accurate detection of the bearing operating status and improving the efficiency and reliability of bearing status detection.

[0005] The first aspect of the present application provides a bearing detection system for an automobile generator, the system comprising: a data acquisition module, which is used to perform sensor array data acquisition, and obtain a historical vibration monitoring data set, a historical temperature monitoring data set, and a historical acoustic emission signal monitoring data set that the sensor array monitors the target bearing within a preset historical window. A stability analysis module, which is used to traverse the historical vibration monitoring data set, the historical temperature monitoring data set, and the historical acoustic emission signal monitoring data set to perform data stability analysis, and determine the vibration stability factor, the temperature stability factor, and the acoustic emission signal stability factor. A target detection step acquisition module, which is used to use the vibration stability factor, the temperature stability factor, and the acoustic emission signal stability factor as indexes to perform a centralized search in the bearing detection configuration space to determine the target detection step. A detection result acquisition module, which is used to perform bearing status detection on the target bearing according to a preset detection item set based on the target detection step, obtain a periodic detection data set, and use a bearing detection identifier to identify the periodic detection data set to obtain a target bearing detection result.

[0006] In the implementation, the stability analysis module is also used to: perform data analysis on the historical vibration monitoring data set using the box plot method to obtain a vibration box plot. Count the historical vibration monitoring data between the upper and lower bounds of the aggregation in the vibration box plot to obtain a cleaning historical vibration data set, wherein the upper bound of the aggregation is the boundary obtained by extending the third quartile upward by 1.5 times the interquartile range, and the lower bound of the aggregation is the boundary obtained by extending the first quartile downward by 1.5 times the interquartile range. Perform variance calculation on the cleaning historical vibration data set to obtain the vibration stability factor. Perform data stability analysis on the historical temperature monitoring data set and the historical acoustic emission signal monitoring data set to obtain the temperature stability factor and the acoustic emission signal stability factor.

[0007] In the implementation, the stability analysis module is also used to: sort the historical vibration monitoring data set in ascending order, and use the historical vibration monitoring data in the middle position as the box center line. Calculate the first quartile and the third quartile of the historical vibration monitoring data set. Use the first quartile as the upper boundary, and the third quartile as the lower boundary, combined with the box center line, to construct the vibration box plot.

[0008] In the implementation, the target detection step acquisition module is also used to: obtain multiple sample vibration stability factors, multiple sample temperature stability factors and multiple sample acoustic emission signal stability factors, and corresponding multiple sample detection step lengths as construction data. Pre-construct a three-dimensional space, wherein the coordinate origin of the three-dimensional space is o, the x-axis is the vibration stability factor, the y-axis is the temperature stability factor, and the z-axis is the acoustic emission signal stability factor. The construction data is input into the three-dimensional space to obtain multiple sample space points, and the multiple sample space points are marked using the multiple sample detection step lengths to obtain the bearing detection configuration space.

[0009] In the implementation, the target detection step acquisition module is also used to: take the plane in the bearing detection configuration space that passes through the vibration stability factor and is parallel to the yoz plane as the first plane. Take the plane in the bearing detection configuration space that passes through the temperature stability factor and is parallel to the xoz plane as the second plane. Take the plane in the bearing detection configuration space that passes through the acoustic emission signal stability factor and is parallel to the xoy plane as the third plane. Take the space enclosed by the yoz plane, xoz plane, xoy plane and the first plane, the second plane, and the third plane as the configuration subspace, wherein the configuration subspace includes multiple configuration sample space points. Perform a centralized search on the multiple configuration sample space points to determine the target configuration sample space point, and take the sample detection step corresponding to the target configuration sample space point as the target detection step.

[0010] In the implementation, the target detection step acquisition module is also used to: extract the central configuration sample space point of the configuration subspace, take the central configuration sample space point as the starting point, and construct the central neighborhood according to the preset centralized search bandwidth, wherein the central neighborhood is a spherical subspace constructed with the central configuration sample space point as the sphere center and the preset centralized search bandwidth as the radius. Count the number of configuration sample space points in the central neighborhood, and divide the statistical result by the volume of the central neighborhood to obtain the central neighborhood concentration density. Randomly extract a configuration sample space point from the edge of the central neighborhood as the first search configuration sample space point, and construct the first search neighborhood concentration density of the first search configuration sample space point. Determine whether the first search neighborhood concentration density is greater than or equal to the central neighborhood concentration density. If so, update the first search configuration sample space point as the starting point, and continue to conduct centralized search until the preset centralized search times are met, and use the search configuration sample space point obtained in the last search as the target configuration sample space point.

[0011] In the implementation method, the target detection step acquisition module is also used to: if not, update the number of search update failures with an initial value of 0 to 1, and randomly select a configuration sample space point from the edge of the central neighborhood again as the first search configuration sample space point for centralized search analysis, and when the number of search update failures is greater than the preset maximum number of search update failures, use the central configuration sample space point as the target configuration sample space point.

[0012] The second aspect of the present application provides a bearing detection method for an automobile generator, the method comprising: executing sensor array data acquisition to obtain a historical vibration monitoring data set, a historical temperature monitoring data set and a historical acoustic emission signal monitoring data set of the sensor array monitoring the target bearing within a preset historical window.

[0013] The historical vibration monitoring data set, the historical temperature monitoring data set and the historical acoustic emission signal monitoring data set are traversed to perform data stability analysis to determine a vibration stability factor, a temperature stability factor and an acoustic emission signal stability factor.

[0014] Using the vibration stability factor, temperature stability factor and acoustic emission signal stability factor as indexes, a concentrated search is performed in the bearing detection configuration space to determine the target detection step length.

[0015] Based on the target detection step, the target bearing is subjected to bearing status detection according to a preset detection item set to obtain a periodic detection data set, and the periodic detection data set is identified using a bearing detection identifier to obtain a target bearing detection result.

[0016] The bearing detection system and method for automobile generator proposed in this application is proposed to collect data by executing the sensor array to obtain the historical vibration monitoring data set, historical temperature monitoring data set and historical acoustic emission signal monitoring data set of the target bearing monitored by the sensor array within the preset historical window. The historical vibration monitoring data set, historical temperature monitoring data set and historical acoustic emission signal monitoring data set are traversed to perform data stability analysis to determine the vibration stability factor, temperature stability factor and acoustic emission signal stability factor. With the vibration stability factor, temperature stability factor and acoustic emission signal stability factor as indexes, a centralized search is performed in the bearing detection configuration space to determine the target detection step length. Based on the target detection step length, the target bearing is subjected to bearing state detection according to the preset detection item set to obtain a periodic detection data set, and the periodic detection data set is identified by using a bearing detection identifier to obtain the target bearing detection result. The technical problems of low detection efficiency and poor detection accuracy in the bearing detection method for automobile generators in the prior art are solved. By dynamically optimizing the detection step size of sensor data acquisition, the system can acquire data according to the optimal acquisition step size when performing data acquisition, thereby achieving accurate detection of the bearing operating status and improving the efficiency and reliability of bearing status detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0018] Figure 1 A schematic structural diagram of a bearing detection system for an automobile generator provided in an embodiment of the present application.

[0019] Figure 2 A schematic flow chart of a bearing detection method for an automobile generator provided in an embodiment of the present application.

[0020] Explanation of the reference numerals: data acquisition module 11 , stability analysis module 12 , target detection step acquisition module 13 , detection result acquisition module 14 . DETAILED DESCRIPTION

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0022] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0023] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0024] The present application embodiment provides a bearing detection system and method for an automobile generator, such as Figure 2 As shown, the method includes: Execute sensor array data acquisition to obtain a historical vibration monitoring data set, a historical temperature monitoring data set, and a historical acoustic emission signal monitoring data set of the sensor array monitoring the target bearing within a preset historical window. Traverse the historical vibration monitoring data set, the historical temperature monitoring data set, and the historical acoustic emission signal monitoring data set to perform data stability analysis and determine the vibration stability factor, the temperature stability factor, and the acoustic emission signal stability factor.

[0025] Execute sensor array data acquisition to obtain monitoring data of the sensor array monitoring the target bearing within a preset historical window. The preset historical window is a pre-set historical time interval window, such as the data collected by the sensor array in the past month. The monitoring data includes: a historical vibration monitoring data set, a historical temperature monitoring data set, and a historical acoustic emission signal monitoring data set. Among them, the vibration monitoring data is to measure the vibration signal of the bearing through an acceleration sensor, and record information such as vibration amplitude, frequency, and vibration waveform. The temperature monitoring data is to monitor the change of the bearing surface temperature through a temperature sensor. The temperature increase may indicate that the bearing is poorly lubricated or is about to fail. The acoustic emission signal monitoring data is to capture the high-frequency sound signal generated by the bearing during operation through an acoustic emission sensor. Abnormal acoustic emission signals may indicate cracks, wear or other faults. Subsequently, the stability analysis module performs data stability analysis by traversing the historical vibration monitoring data set, the historical temperature monitoring data set, and the historical acoustic emission signal monitoring data set to determine the vibration stability factor, the temperature stability factor, and the acoustic emission signal stability factor.

[0026] The method provided in the embodiment of the present application also includes: using the box plot method to perform data analysis on the historical vibration monitoring data set to obtain a vibration box plot. Counting the historical vibration monitoring data located between the upper bound and the lower bound of the aggregation in the vibration box plot to obtain a cleaning historical vibration data set, wherein the upper bound of the aggregation is the boundary obtained by extending the third quartile upward by 1.5 times the interquartile range, and the lower bound of the aggregation is the boundary obtained by extending the first quartile downward by 1.5 times the interquartile range. Performing variance calculation on the cleaning historical vibration data set to obtain the vibration stability factor. Performing data stability analysis on the historical temperature monitoring data set and the historical acoustic emission signal monitoring data set to obtain the temperature stability factor and the acoustic emission signal stability factor.

[0027] The box plot method is used to analyze the historical vibration monitoring data set, and the box plot corresponding to the historical vibration monitoring data set is drawn to obtain a vibration box plot. Among them, the box plot is a graphical tool that displays data distribution through five statistics (minimum value, first quartile, median, third quartile and maximum value). The historical vibration monitoring data between the upper and lower bounds of the aggregation in the vibration box plot are counted, and the outliers outside the upper and lower bounds of the aggregation are eliminated. These outliers are usually data fluctuations caused by sensor failure or external interference, thereby completing the cleaning of the historical vibration data set and obtaining a cleaned historical vibration data set. Among them, the upper bound of the aggregation is the boundary obtained by extending the third quartile upward by 1.5 times the interquartile range, that is, the third quartile plus 1.5 times the interquartile range, and the lower bound of the aggregation is the boundary obtained by extending the first quartile downward by 1.5 times the interquartile range, that is, the first quartile plus 1.5 times the interquartile range. Furthermore, the variance of the cleaning history vibration data set is calculated to obtain the vibration stability factor. When the vibration stability factor is smaller, the corresponding vibration is relatively stable, indicating that the vibration state of the bearing is normal. A larger vibration stability factor may indicate that the vibration fluctuation is large, which may be related to the lubrication or failure of the bearing. Finally, the historical temperature monitoring data set and the historical acoustic emission signal monitoring data set are subjected to data stability analysis using the same processing steps as the vibration stability factor acquisition steps, including drawing box plots and cleaning the data, so as to obtain the temperature stability factor and the acoustic emission signal stability factor.

[0028] The method provided in the embodiment of the present application also includes: sorting the historical vibration monitoring data set in ascending order, and using the historical vibration monitoring data in the middle position as the box center line. Calculating the first quartile and the third quartile of the historical vibration monitoring data set. Using the first quartile as the upper boundary and the third quartile as the lower boundary, combined with the box center line, construct the vibration box plot.

[0029] When drawing a vibration box plot, the historical vibration monitoring data set is arranged in order from small to large, and the historical vibration monitoring data in the middle position is used as the box center line, and the box center line is the median of the data set. Calculate the first quartile and the third quartile of the historical vibration monitoring data set. The first quartile is the value located in the lower 25% position after the data set is sorted from small to large. The third quartile is the value located in the upper 25% position after the data set is sorted from small to large. The first quartile is used as the upper boundary, and the third quartile is used as the lower boundary, combined with the box center line, to construct the vibration box plot.

[0030] The vibration stability factor, temperature stability factor and acoustic emission signal stability factor are used as indexes to perform a centralized search in the bearing detection configuration space to determine the target detection step length. Based on the target detection step length, the target bearing is subjected to a bearing state detection according to a preset detection item set to obtain a periodic detection data set, and the periodic detection data set is identified using a bearing detection identifier to obtain a target bearing detection result.

[0031] The target detection step acquisition module uses the vibration stability factor, temperature stability factor and acoustic emission signal stability factor as index parameters, performs centralized search in the bearing detection configuration space, and determines the target detection step. The target detection step is the acquisition period of the sensor array data acquisition. Finally, through the detection result acquisition module, the target bearing is subjected to bearing state detection according to the preset detection item set based on the target detection step, and a periodic detection data set is obtained. The periodic detection data set is identified by a bearing detection identifier to obtain the target bearing detection result. The bearing detection identifier is constructed based on a neural network model, wherein the construction data is the bearing detection result identification data corresponding to a large amount of historical vibration monitoring data, temperature monitoring data and acoustic emission signal monitoring data collected. The bearing detection result identification data includes: normal identification, the normal identification includes a normal state rating, abnormal identification, the abnormal identification includes an abnormal state rating and a specific abnormal category, such as raceway failure, lubrication problem, missing ball, etc. The technical problems of low detection efficiency and poor detection accuracy in the bearing detection method of the automobile generator in the prior art are solved. By dynamically optimizing the detection step size of sensor data acquisition, the system can acquire data according to the optimal acquisition step size when performing data acquisition, thereby achieving accurate detection of the bearing operating status and improving the efficiency and reliability of bearing status detection.

[0032] The method provided in the embodiment of the present application also includes: obtaining multiple sample vibration stability factors, multiple sample temperature stability factors, and multiple sample acoustic emission signal stability factors, as well as corresponding multiple sample detection step lengths as construction data. Pre-constructing a three-dimensional space, wherein the coordinate origin of the three-dimensional space is o, the x-axis is the vibration stability factor, the y-axis is the temperature stability factor, and the z-axis is the acoustic emission signal stability factor. Inputting the construction data into the three-dimensional space to obtain multiple sample space points, and using the multiple sample detection step lengths to mark the multiple sample space points, obtain the bearing detection configuration space.

[0033] The target detection step acquisition module is also used to obtain multiple sample vibration stability factors, multiple sample temperature stability factors and multiple sample acoustic emission signal stability factors, as well as corresponding multiple sample detection steps as construction data. The sample detection step is the acquisition period of the sensor array data acquisition. A three-dimensional space is pre-constructed, wherein the coordinate origin of the three-dimensional space is o, the x-axis is the vibration stability factor, the y-axis is the temperature stability factor, and the z-axis is the acoustic emission signal stability factor. The construction data is input into the three-dimensional space, and the distribution of the construction data in the three-dimensional space is obtained to obtain multiple sample space points. The multiple sample space points are identified using the multiple sample detection steps, so that the multiple sample space points reflect their corresponding sample detection steps, and the bearing detection configuration space is obtained.

[0034] The method provided in an embodiment of the present application also includes: taking the plane in the bearing detection configuration space that passes through the vibration stability factor and is parallel to the yoz plane as the first plane. Taking the plane in the bearing detection configuration space that passes through the temperature stability factor and is parallel to the xoz plane as the second plane. Taking the plane in the bearing detection configuration space that passes through the acoustic emission signal stability factor and is parallel to the xoy plane as the third plane. Taking the space enclosed by the yoz plane, xoz plane, xoy plane and the first plane, the second plane, and the third plane as a configuration subspace, wherein the configuration subspace includes a plurality of configuration sample space points. Performing a concentrated search on the plurality of configuration sample space points to determine the target configuration sample space point, and taking the sample detection step corresponding to the target configuration sample space point as the target detection step.

[0035] The plane in the bearing detection configuration space that passes through the vibration stability factor and is parallel to the yoz plane is taken as the first plane. The plane in the bearing detection configuration space that passes through the temperature stability factor and is parallel to the xoz plane is taken as the second plane. The plane in the bearing detection configuration space that passes through the acoustic emission signal stability factor and is parallel to the xoy plane is taken as the third plane. The space surrounded by the first plane, the second plane, the third plane, the yoz plane, the xoz plane, and the xoy plane is taken as a configuration subspace, and the configuration subspace contains multiple sample points, that is, multiple configuration sample space points.

[0036] Further, the multiple configuration sample space points are searched intensively to determine the target configuration sample space point, the target configuration sample space point being the spatial range with the largest sample point density among the multiple configuration sample space points. The sample detection step length corresponding to the target configuration sample space point is used as the target detection step length. The sample detection step length corresponding to the target configuration sample space point is the step length average of the sample detection step lengths of each sample point in the target configuration sample space.

[0037] The method provided by the embodiment of the present application also includes: extracting the central configuration sample space point of the configuration subspace, taking the central configuration sample space point as the starting point, and constructing a central neighborhood according to the preset centralized search bandwidth, wherein the central neighborhood is a spherical subspace constructed with the central configuration sample space point as the sphere center and the preset centralized search bandwidth as the radius. Count the number of configuration sample space points in the central neighborhood, and divide the statistical result by the volume of the central neighborhood to obtain the central neighborhood concentration density. Randomly extract a configuration sample space point from the edge of the central neighborhood as the first search configuration sample space point, and construct the first search neighborhood concentration density of the first search configuration sample space point. Determine whether the first search neighborhood concentration density is greater than or equal to the central neighborhood concentration density. If so, update the first search configuration sample space point as the starting point, and continue the centralized search until the preset centralized search number is met, and use the search configuration sample space point obtained in the last search as the target configuration sample space point.

[0038] Extract the central configuration sample space point of the configuration subspace, where the central configuration sample space point is the intersection point of the lines connecting the center of the first plane, the center of the second plane, and the center of the third plane. Take the central configuration sample space point as the starting point, and construct a central neighborhood according to the preset centralized search bandwidth, that is, a spherical subspace is constructed with the central configuration sample space point as the center of the sphere and the preset centralized search bandwidth as the radius to obtain the central neighborhood. The search bandwidth is a pre-set search radius, which can be set based on actual conditions, and the central neighborhood is included in the configuration subspace. Statistically obtain the number of configuration sample space points in the central neighborhood, and compare the statistical result with the volume of the central neighborhood to obtain the central neighborhood concentration density.

[0039] Furthermore, a configuration sample space point is randomly selected from the edge of the central neighborhood as the first search configuration sample space point, and the first search configuration sample space point and the corresponding first search neighborhood concentration density are constructed by adopting the same acquisition method as that of the central neighborhood concentration density. It is determined whether the first search neighborhood concentration density is greater than or equal to the central neighborhood concentration density. If so, the sample points in the first search neighborhood are relatively abundant, and the first search configuration sample space point is updated as the starting point, and the concentrated search is continued until the preset number of concentrated searches is met, and the search configuration sample space point obtained in the last search is used as the target configuration sample space point.

[0040] Determine whether the first search neighborhood concentration density is greater than or equal to the central neighborhood concentration density. If not, update the number of search update failures with an initial value of 0 to 1, and randomly select a configuration sample space point from the edge of the central neighborhood as the first search configuration sample space point for concentrated search analysis. When the next search neighborhood concentration density is less than the central neighborhood concentration density, continue to add 1 to the updated number of search failures. When the number of search update failures is greater than the preset maximum number of search update failures, use the central configuration sample space point as the target configuration sample space point.

[0041] In the above, refer to Figure 2 The bearing detection method of the automobile generator according to the embodiment of the present invention is described in detail. Figure 1 A bearing detection system for an automobile generator according to an embodiment of the present invention is described.

[0042] The bearing detection system for an automobile generator according to an embodiment of the present invention solves the technical problems of low detection efficiency and poor detection accuracy in the bearing detection method for an automobile generator in the prior art. By dynamically optimizing the detection step length of sensor data acquisition, the system can acquire data according to the optimal acquisition step length when performing data acquisition, thereby realizing accurate detection of the bearing operating state and improving the efficiency and reliability of bearing state detection. The bearing detection system for an automobile generator includes: a data acquisition module 11, a stability analysis module 12, a target detection step length acquisition module 13, and a detection result acquisition module 14.

[0043] The data acquisition module 11 is used to perform sensor array data acquisition to obtain a historical vibration monitoring data set, a historical temperature monitoring data set and a historical acoustic emission signal monitoring data set of the sensor array monitoring the target bearing within a preset historical window.

[0044] The stability analysis module 12 is used to traverse the historical vibration monitoring data set, the historical temperature monitoring data set and the historical acoustic emission signal monitoring data set to perform data stability analysis and determine a vibration stability factor, a temperature stability factor and an acoustic emission signal stability factor.

[0045] The target detection step length acquisition module 13 is used to perform a centralized search in the bearing detection configuration space using the vibration stability factor, the temperature stability factor and the acoustic emission signal stability factor as indexes to determine the target detection step length.

[0046] The detection result acquisition module 14 is used to perform bearing state detection on the target bearing according to a preset detection item set based on the target detection step, obtain a periodic detection data set, and use a bearing detection identifier to identify the periodic detection data set to obtain a target bearing detection result.

[0047] The specific configuration of the stability analysis module 12 will be described in detail below. The stability analysis module 12 further includes: using the box plot method to perform data analysis on the historical vibration monitoring data set to obtain a vibration box plot. The historical vibration monitoring data between the upper bound and the lower bound of the aggregation in the vibration box plot are counted to obtain a cleaning historical vibration data set, wherein the upper bound of the aggregation is the boundary obtained by extending the third quartile upward by 1.5 times the interquartile range, and the lower bound of the aggregation is the boundary obtained by extending the first quartile downward by 1.5 times the interquartile range. Variance calculation is performed on the cleaning historical vibration data set to obtain the vibration stability factor. Data stability analysis is performed on the historical temperature monitoring data set and the historical acoustic emission signal monitoring data set to obtain the temperature stability factor and the acoustic emission signal stability factor.

[0048] The specific configuration of the stability analysis module 12 will be described in detail below. The stability analysis module 12 may further include: sorting the historical vibration monitoring data set in ascending order, and using the historical vibration monitoring data in the middle position as the box center line. Calculate the first quartile and the third quartile of the historical vibration monitoring data set. Use the first quartile as the upper boundary, and the third quartile as the lower boundary, combined with the box center line, to construct the vibration box line diagram.

[0049] Below, the specific configuration of the target detection step acquisition module 13 will be described in detail. The target detection step acquisition module 13 further includes: obtaining multiple sample vibration stability factors, multiple sample temperature stability factors and multiple sample acoustic emission signal stability factors, and corresponding multiple sample detection steps as construction data. Pre-construct a three-dimensional space, wherein the coordinate origin of the three-dimensional space is o, the x-axis is the vibration stability factor, the y-axis is the temperature stability factor, and the z-axis is the acoustic emission signal stability factor. The construction data is input into the three-dimensional space to obtain multiple sample space points, and the multiple sample space points are marked using the multiple sample detection steps to obtain the bearing detection configuration space.

[0050] Below, the specific configuration of the target detection step acquisition module 13 will continue to be described in detail. The target detection step acquisition module 13 further includes: taking the plane in the bearing detection configuration space that passes through the vibration stability factor and is parallel to the yoz plane as the first plane. Taking the plane in the bearing detection configuration space that passes through the temperature stability factor and is parallel to the xoz plane as the second plane. Taking the plane in the bearing detection configuration space that passes through the acoustic emission signal stability factor and is parallel to the xoy plane as the third plane. The space enclosed by the yoz plane, xoz plane, xoy plane and the first plane, the second plane, and the third plane is taken as a configuration subspace, wherein the configuration subspace includes multiple configuration sample space points. The multiple configuration sample space points are searched intensively to determine the target configuration sample space point, and the sample detection step corresponding to the target configuration sample space point is taken as the target detection step.

[0051] The specific configuration of the target detection step acquisition module 13 will be described in detail below. The target detection step acquisition module 13 further includes: extracting the central configuration sample space point of the configuration subspace, taking the central configuration sample space point as the starting point, and constructing a central neighborhood according to the preset centralized search bandwidth, wherein the central neighborhood is a spherical subspace constructed with the central configuration sample space point as the sphere center and the preset centralized search bandwidth as the radius. Counting the number of configuration sample space points in the central neighborhood, and dividing the statistical result by the volume of the central neighborhood to obtain the central neighborhood concentration density. Randomly extract a configuration sample space point from the edge of the central neighborhood as the first search configuration sample space point, and construct the first search neighborhood concentration density of the first search configuration sample space point; determine whether the first search neighborhood concentration density is greater than or equal to the central neighborhood concentration density, if so, update the first search configuration sample space point as the starting point, and continue to conduct centralized search until the preset centralized search times are met, and use the search configuration sample space point obtained in the last search as the target configuration sample space point.

[0052] The specific configuration of the correlation index acquisition module 13 will be described in detail below. The correlation index acquisition module 13 may further include: if not, the number of search update failures whose initial value is 0 is updated to 1, and a configuration sample space point is randomly selected from the edge of the central neighborhood again as the first search configuration sample space point for centralized search analysis, and when the number of search update failures is greater than the preset maximum number of search update failures, the central configuration sample space point is used as the target configuration sample space point.

[0053] The automobile generator bearing detection system provided in the embodiment of the present invention can execute the automobile generator bearing detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0054] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or the server, and the various units and modules included are only divided according to functional logic, but are not limited to the above divisions, as long as the corresponding functions can be achieved. In addition, the specific names of the functional units are only for the convenience of distinguishing from each other, and are not used to limit the scope of protection of the present invention.

[0055] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. Bearing detection system for automobile generator, characterized in that: The system comprises: A data acquisition module, used to perform sensor array data acquisition, and obtain a historical vibration monitoring data set, a historical temperature monitoring data set, and a historical acoustic emission signal monitoring data set of the sensor array monitoring the target bearing within a preset historical window; A stability analysis module, used to traverse the historical vibration monitoring data set, the historical temperature monitoring data set and the historical acoustic emission signal monitoring data set to perform data stability analysis, and determine a vibration stability factor, a temperature stability factor and an acoustic emission signal stability factor; A target detection step length acquisition module is used to perform a centralized search in the bearing detection configuration space using the vibration stability factor, temperature stability factor and acoustic emission signal stability factor as indexes to determine the target detection step length; The detection result acquisition module is used to perform bearing status detection on the target bearing according to a preset detection item set based on the target detection step, obtain a periodic detection data set, and use a bearing detection identifier to identify the periodic detection data set to obtain a target bearing detection result.

2. The bearing detection system for an automobile generator according to claim 1, characterized in that: The stability analysis module is also used to: Performing data analysis on the historical vibration monitoring data set using a box plot method to obtain a vibration box plot; The historical vibration monitoring data between the upper and lower bounds of the aggregation in the vibration box plot are counted to obtain a cleaning historical vibration data set, wherein the upper bound of the aggregation is a boundary obtained by extending the third quartile upward by 1.5 times the interquartile range, and the lower bound of the aggregation is a boundary obtained by extending the first quartile downward by 1.5 times the interquartile range; Performing variance calculation on the cleaning history vibration data set to obtain the vibration stability factor; Data stability analysis is performed on the historical temperature monitoring data set and the historical acoustic emission signal monitoring data set to obtain the temperature stability factor and the acoustic emission signal stability factor.

3. The bearing detection system for an automobile generator according to claim 2, characterized in that: The stability analysis module is also used to: Sorting the historical vibration monitoring data set in ascending order, and taking the historical vibration monitoring data in the middle as the box center line; Calculating the first quartile and the third quartile of the historical vibration monitoring data set; The first quartile is used as the upper boundary, and the third quartile is used as the lower boundary, combined with the box center line, to construct the vibration box plot.

4. The bearing detection system for an automobile generator according to claim 1, characterized in that: The target detection step length acquisition module is also used for: Acquire multiple sample vibration stability factors, multiple sample temperature stability factors, multiple sample acoustic emission signal stability factors, and corresponding multiple sample detection step lengths as construction data; Pre-constructing a three-dimensional space, wherein the coordinate origin of the three-dimensional space is o, the x-axis is the vibration stability factor, the y-axis is the temperature stability factor, and the z-axis is the acoustic emission signal stability factor; The construction data is input into the three-dimensional space to obtain a plurality of sample space points, and the plurality of sample space points are marked using the plurality of sample detection step lengths to obtain the bearing detection configuration space.

5. The bearing detection system for an automobile generator according to claim 4, characterized in that: The target detection step length acquisition module is also used for: Taking a plane in the bearing detection configuration space that passes through the vibration stability factor and is parallel to the yoz plane as a first plane; Taking a plane in the bearing detection configuration space that passes through the temperature stability factor and is parallel to the xoz plane as a second plane; A plane in the bearing detection configuration space that passes through the acoustic emission signal stability factor and is parallel to the xoy plane is used as a third plane; The space enclosed by the yoz plane, the xoz plane, the xoy plane and the first plane, the second plane and the third plane is used as a configuration subspace, wherein the configuration subspace includes a plurality of configuration sample space points; The multiple configuration sample space points are searched intensively to determine a target configuration sample space point, and the sample detection step corresponding to the target configuration sample space point is used as the target detection step.

6. The bearing detection system for an automobile generator according to claim 5, characterized in that: The target detection step length acquisition module is also used for: Extracting a central configuration sample space point of the configuration subspace, taking the central configuration sample space point as a starting point, and constructing a central neighborhood according to a preset centralized search bandwidth, wherein the central neighborhood is a spherical subspace constructed with the central configuration sample space point as a sphere center and the preset centralized search bandwidth as a radius; Counting the number of sample space points configured in the central neighborhood, and dividing the statistical result by the volume of the central neighborhood to obtain the central neighborhood concentration density; Randomly extract a configuration sample space point from the edge of the central neighborhood as a first search configuration sample space point, and construct a first search neighborhood concentration density of the first search configuration sample space point; Determine whether the first search neighborhood concentration density is greater than or equal to the central neighborhood concentration density. If so, update the first search configuration sample space point as the starting point, continue the concentrated search until the preset number of concentrated searches is met, and use the search configuration sample space point obtained in the last search as the target configuration sample space point.

7. The bearing detection system for an automobile generator according to claim 6, characterized in that: The target detection step length acquisition module is also used for: If not, the number of search update failures with an initial value of 0 is updated to 1, and a configuration sample space point is randomly selected from the edge of the central neighborhood as the first search configuration sample space point for centralized search analysis. When the number of search update failures is greater than the preset maximum number of search update failures, the central configuration sample space point is used as the target configuration sample space point.

8. A bearing detection method for an automobile generator, characterized in that: The method comprises: Execute sensor array data acquisition to obtain a historical vibration monitoring data set, a historical temperature monitoring data set, and a historical acoustic emission signal monitoring data set of the sensor array monitoring the target bearing within a preset historical window; Traversing the historical vibration monitoring data set, the historical temperature monitoring data set and the historical acoustic emission signal monitoring data set to perform data stability analysis, and determine a vibration stability factor, a temperature stability factor and an acoustic emission signal stability factor; Using the vibration stability factor, temperature stability factor and acoustic emission signal stability factor as indexes, a centralized search is performed in the bearing detection configuration space to determine the target detection step length; Based on the target detection step, the target bearing is subjected to bearing status detection according to a preset detection item set to obtain a periodic detection data set, and the periodic detection data set is identified using a bearing detection identifier to obtain a target bearing detection result.

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