Clutch intelligent detection method based on multi-dimensional data acquisition

Abstract

The invention relates to the field of data processing, in particular to a clutch intelligent detection method based on multi-dimensional data acquisition, which comprises the following steps: acquiring clutch related data uploaded by a user; determining target spectral data according to the vehicle characterization coefficient; determining effective unit data according to the element characterization value, and determining a data screening mode of the target spectral data according to the number of the effective unit data; determining a unit data state according to the data paragraph reference value and the feature threshold; according to the unit data state of the effective unit data, determining that a data processing mode of the effective unit data is segmentation according to a concentration value or segmentation according to a torque threshold value; and determining whether the segmented paragraph is qualified or not according to the comprehensive deviation value under a preset condition. According to the invention, targeted training can be carried out on oil spectral data in different wear stages, and the accuracy of clutch aging life estimation detection is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an intelligent clutch detection method based on multi-dimensional data collection. Background Art

[0002] Since the oil spectral data contains the concentration of wear particles of multiple elements, but not all oil spectral data can provide useful degradation characterization information, and the clutch operation process includes different wear states, the oil spectral data of different wear states are not segmented, resulting in poor accuracy of clutch life prediction. Therefore, how to conduct targeted training on oil spectral data of different wear stages and improve the accuracy of clutch aging life estimation detection is a technical problem that needs to be urgently solved by technical personnel in this field.

[0003] Chinese patent publication number CN102254063B discloses a method for automatically updating a clutch database, including: first determining the parts and parameter information that affect the performance of the clutch to form an influencing parameter set; the elements in the influencing parameter set have different degrees of influence on the clutch design quality. In order to reflect the importance of each element, a corresponding weight should be determined for each element to form a weight set corresponding to the influencing parameter set; then the membership of the elements in the influencing parameter set is calculated using the membership function of the Cauchy distribution to construct a fuzzy relationship matrix; finally, the influencing parameter set, the fuzzy relationship matrix and the weight set are synthesized to obtain a comprehensive evaluation value. If it meets the requirements, the design data can be entered into the database, otherwise the parameters must be redesigned. It can be seen that the above technical solution has the following problems: it is only based on the complete oil spectral data for entry, the oil spectral data is not segmented, and the oil spectral data at different wear state stages cannot be trained in a targeted manner, and the accuracy of the clutch aging life estimation detection is poor. Summary of the invention

[0004] To this end, the present invention provides a clutch intelligent detection method based on multi-dimensional data acquisition, which is used to overcome the problems in the prior art that the data is only input based on the complete oil spectral data, the oil spectral data is not segmented, and the oil spectral data at different wear stages cannot be trained in a targeted manner, resulting in poor accuracy in clutch aging life estimation detection.

[0005] To achieve the above object, the present invention provides a clutch intelligent detection method based on multi-dimensional data acquisition, comprising: Get clutch related data uploaded by the user; Determining target spectral data according to vehicle characterization coefficients; Determine the valid unit data according to the element characterization value, and determine the target spectrum data according to the number of valid unit data. The data screening method is to screen according to the correlation coefficient, or to screen Ф valid unit data in descending order according to the element characterization value; Determine the unit data state according to the data segment reference value and the characteristic threshold value; Determining, according to the unit data state of the effective unit data, a data processing method of the effective unit data, that is, segmenting according to a concentration value, or segmenting according to a torque threshold value; Under the preset conditions, whether the segmented paragraph is qualified is determined according to the comprehensive deviation value; The preset condition is that the segmentation is completed according to the torque threshold.

[0006] Further, target spectral data is determined according to the vehicle characterization coefficient; Dividing the data sequence of the spectral data and obtaining a number of pre-screened spectral data, and recording each pre-screened spectral data as target spectral data; The data sequence of the spectral data is the order of vehicle characterization coefficients from large to small; The vehicle characterization coefficient is determined according to the block frequency reference value and the environment characterization value; The relationship between the vehicle characterization coefficient and the frequency blocking reference value is a positive correlation, and the relationship between the vehicle characterization coefficient and the environment characterization value is a positive correlation.

[0007] Further, valid unit data is determined according to the element characterization value, and a data screening method of the target spectral data is determined according to the number of valid unit data; If the number of valid unit data is greater than or equal to the preset number, the data screening method is to screen according to the correlation coefficient; If the number of valid unit data is less than the preset number, the data screening method is to screen Ф valid unit data in descending order according to the element representation value; The valid unit data is unit data whose element characterization value is greater than a preset element characterization value.

[0008] Further, screening based on correlation coefficients includes: For a target spectral data, the valid unit data with the largest element characterization value in the target spectral data is recorded as the target unit, and other valid unit data in the target spectral data excluding the target unit is recorded as the reference unit. The correlation coefficient between the target unit and each reference unit is detected, and Ф-1 reference units are screened in descending order of the correlation coefficient. The target unit and the screened Ф-1 reference units are recorded as screened data.

[0009] Further, determining a data processing mode of the valid unit data according to the unit data state of the valid unit data; If the unit data state is the first preset unit data state, segmentation is performed according to the concentration value; If the unit data state is the second preset unit data state, segmentation is performed according to the torque threshold.

[0010] Furthermore, the unit data state is determined according to the data segment reference value and the characteristic threshold, and the unit data state includes: A first preset unit data state in which the data segment reference value is less than a preset data segment reference value and the feature threshold is less than a preset feature threshold; A second preset unit data state in which the data segment reference value is greater than or equal to the preset data segment reference value or the feature threshold is greater than or equal to the preset feature threshold; The data segment reference value is determined according to the number of characteristic segments and the maximum slope deviation, and the characteristic threshold is determined according to the data increase and the unit data duration.

[0011] Further, segmentation is performed based on concentration values; For a valid unit of data, the starting concentration point and the ending concentration point of the data segment whose concentration value is greater than the first preset concentration and less than the second preset concentration are used as segmentation points for segmentation; The first preset concentration is less than the second preset concentration.

[0012] Further, segmentation is performed based on a torque threshold; For the torque data corresponding to a valid unit data, a torque segment whose torque reference coefficient is greater than or equal to a preset torque reference coefficient is recorded as a characteristic torque segment, and distribution detection is performed on each characteristic torque segment. When distribution detection is performed on a single characteristic torque segment, the characteristic torque segment is recorded as a target characteristic torque segment, and a distance reference value between each characteristic torque segment after the time sequence of the target characteristic torque segment is detected. If the distance reference value between each characteristic torque segment after the time sequence of the target characteristic torque segment is less than the preset distance reference value, it is recorded as an associated characteristic torque segment of the target characteristic torque segment. If the distance reference value between each characteristic torque segment after the time sequence of the target characteristic torque segment is greater than or equal to the preset distance reference value, the distribution detection of the target characteristic torque segment is stopped, and the segment between the starting torque point of the target characteristic torque segment and the ending torque point of the associated characteristic torque segment with the largest distance reference value corresponding to the target characteristic torque segment is recorded as a segment combination; The effective unit data is segmented using the starting torque point and the ending torque point corresponding to the paragraph combination with the largest horizontal coordinate length as segmentation points; The torque threshold is determined according to a torque reference coefficient and a torque fluctuation coefficient. The torque reference coefficient is positively correlated with the torque mean value, and the torque fluctuation coefficient is positively correlated with the torque fluctuation value.

[0013] Further, determining whether the segmented paragraph is qualified according to the comprehensive deviation value; Detecting the starting temperature point and the ending temperature point of the temperature segment whose temperature reference value is greater than or equal to the preset temperature reference value, and determining the comprehensive deviation value according to the sum of the absolute value of the difference between the starting temperature point and the starting torque point and the absolute value of the difference between the ending temperature point and the ending torque point; If the comprehensive deviation value is greater than or equal to the preset comprehensive deviation value, the segmentation fails, and the starting midpoint and the ending midpoint are selected as segmentation points for segmentation; If the comprehensive deviation value is less than the preset comprehensive deviation value, the segmentation is qualified.

[0014] Furthermore, the element characterization value is determined according to the sum of the unit characterization values ​​corresponding to each data point.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical scheme of the present invention, the target spectral data is determined according to the vehicle characterization coefficient, and the vehicle characterization coefficient is used to reflect the influence of operating habits on the pre-screening spectral data, and then the data sequence of the spectral data is divided and a number of pre-screening spectral data are obtained, thereby avoiding the problem of poor representativeness of the target spectral data, ensuring the richness of the target spectral data, and thus improving the accuracy of the clutch aging life estimation detection.

[0016] Furthermore, the present invention effectively reflects the information content contained in the target spectral data through the element standard value, and then determines the effective unit data according to the element characterization value, so that the selection of effective unit data is more representative, so that the screened effective unit data can better characterize the wear state of the clutch, thereby improving the accuracy and reliability of the clutch aging life estimation detection.

[0017] Furthermore, in the present invention, a data screening method for target spectral data is determined according to the number of valid unit data, and the richness of the data is effectively reflected by the number of valid unit data, and different data screening methods are adaptively selected, so that the selection of data screening method is more in line with the actual application scenario, ensuring that high-quality data can be selected when the number of valid unit data is different, avoiding the problem of inaccurate clutch life prediction due to data quality problems, and thus improving the reliability of clutch aging life estimation detection.

[0018] Furthermore, in the present invention, the correlation coefficient can effectively reflect the relevant situation of the effective unit data, and then screening can be performed according to the correlation coefficient to screen out the effective unit data that has a significant impact on clutch wear, thereby improving the reliability of clutch aging life estimation detection.

[0019] Furthermore, in the present invention, the unit data state is determined according to the data segment reference value and the characteristic threshold value, and the distribution points of the effective unit data are effectively reflected by the data segment reference value and the characteristic threshold value, and then different data processing methods are selected according to the unit data state, so that the selection of data processing method is more in line with the actual application scenario, and different wear stages can be effectively segmented, and oil spectral data of different wear stages can be targetedly trained, thereby improving the accuracy of clutch aging life estimation detection.

[0020] Furthermore, the present invention effectively reflects the wear state of the clutch through the concentration value, and can then segment different wear stages according to the concentration value, and can perform targeted training on the oil spectral data of different wear states, thereby improving the accuracy of clutch aging life estimation detection.

[0021] Furthermore, the present invention performs segmentation according to a torque threshold value, and effectively reflects the wear state of the clutch through the torque threshold value, and then segments the effective unit data according to the wear state, so as to conduct targeted training on the oil spectral data of different wear states, thereby improving the accuracy of the clutch aging life prediction detection, and effectively reflects the wear state of the clutch through the temperature fluctuation value, and then effectively reflects the deviation of the temperature point and the torque point through the comprehensive deviation value, and then determines whether the segmentation section is qualified according to the actual situation, so that the setting position of the segmentation point is more accurate, thereby improving the accuracy of the clutch aging life prediction detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the clutch intelligent detection method based on multi-dimensional data acquisition of the present invention; Figure 2 A flow chart of a data screening method for determining the number of effective unit data of the present invention for target spectral data; Figure 3 A flow chart of the present invention for determining a data processing method for valid unit data according to the unit data status of the valid unit data; Figure 4 The present invention is a flow chart of determining the unit data state according to the data segment reference value and the characteristic threshold value. DETAILED DESCRIPTION

[0023] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0024] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0025] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0026] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] See also Figures 1 to 4 As shown, the present invention provides a clutch intelligent detection method based on multi-dimensional data collection, comprising: Get clutch related data uploaded by the user; Determining target spectral data according to vehicle characterization coefficients; Determine the valid unit data according to the element characterization value, and determine the target spectrum data according to the number of valid unit data. The data screening method is to screen according to the correlation coefficient, or to screen Ф valid unit data in descending order according to the element characterization value; Determine the unit data state according to the data segment reference value and the characteristic threshold value; Determining, according to the unit data state of the effective unit data, a data processing method of the effective unit data, that is, segmenting according to a concentration value, or segmenting according to a torque threshold value; Under the preset conditions, whether the segmented paragraph is qualified is determined according to the comprehensive deviation value; The preset condition is that the segmentation is completed according to the torque threshold.

[0028] The application scenario of the present invention is for clutch aging life estimation detection, specifically spectral data sample selection, clutch-related data includes but is not limited to spectral data, torque data and temperature data, wherein the monitoring time of the spectral data, torque data and temperature data corresponding to a single clutch-related data is the same, a single spectral data includes a number of unit data, the unit data is the concentration value of a single element in the lubricating oil of the clutch monitored at different time points during the historical use of the clutch, the lubricating oil is the oil between the clutch friction plate and the steel plate, and the concentration value is detected by measuring it through an oil analysis spectrometer after the oil is excited at ultra-high voltage between the disk and rod electrodes. This is content that is easy for technical personnel in this field to understand and will not be elaborated in detail.

[0029] Among them, a unit data monitoring method is provided, in which the lubricating oil is sampled once every 1 hour, and the time of a single extraction of the lubricating oil is recorded as a time point to obtain the concentration value of the element corresponding to the unit data at each time point during the monitoring time. The torque data and temperature data are monitored by a torque sensor and a temperature sensor to obtain the torque value and temperature value corresponding to each time point during the monitoring time. This is content that is easy to understand for technicians in this field and will not be elaborated on in detail.

[0030] Specifically, target spectral data is determined according to the vehicle characterization coefficient; Dividing the data sequence of the spectral data and obtaining a number of pre-screened spectral data, and recording each pre-screened spectral data as target spectral data; The data sequence of the spectral data is the order of vehicle characterization coefficients from large to small; The vehicle characterization coefficient is determined according to the block frequency reference value and the environment characterization value; The relationship between the vehicle characterization coefficient and the frequency blocking reference value is a positive correlation, and the relationship between the vehicle characterization coefficient and the environment characterization value is a positive correlation.

[0031] The pre-screening spectral data is obtained by dividing the data sequence of the spectral data into M equal parts, and recording the spectral data at each equal division point as the pre-screening spectral data. The value of M is positively correlated with the length of the data sequence, and the length of the data sequence is the number of spectral data in the data sequence. For a pre-screened spectral data, the frequency reference value = the number of gear shifts / data duration, the number of gear shifts is the number of gear shifts within the monitoring time corresponding to the pre-screened spectral data, the data duration is the length of the monitoring time corresponding to the pre-screened spectral data, the environmental characterization value = (vehicle average speed / preset vehicle average speed) × speed coefficient + (vehicle setback number / preset vehicle setback number) × setback coefficient, vehicle average speed = vehicle travel distance within the monitoring time corresponding to the pre-screened spectral data / data duration, the number of vehicle setbacks is the number of times the vehicle speed change within the monitoring time corresponding to the pre-screened spectral data is greater than the preset speed change, the present invention is provided with a continuous cycle monitoring The status is determined once at the end of each monitoring cycle. The duration of the monitoring cycle can be set according to the needs of the user. The greater the user's demand for speed monitoring accuracy, the shorter the monitoring cycle. A value of the duration of a monitoring cycle is provided. The duration of the monitoring cycle is 1min. Speed ​​change = |the distance traveled by the vehicle in the current monitoring cycle / the length of the monitoring cycle-the distance traveled by the vehicle in the previous monitoring cycle adjacent to the current monitoring cycle / the length of the monitoring cycle|. The value of the preset speed change can be determined by the user according to the actual application scenario. A value of the preset speed change is provided. The preset speed change is 30km / h. It can be understood that the values ​​of the speed coefficient and the jerking coefficient can be obtained by the user through deep learning convolutional neural network learning based on historical records. It can be understood that the present invention reflects the impact of vehicle driving on effective unit data through environmental characterization values. The user can use deep learning through historical user data to obtain the impact of the average vehicle speed and the number of vehicle jerking times on the effective unit data, and then select the values ​​of the speed coefficient and the jerking coefficient accordingly, wherein the speed coefficient + the jerking coefficient = 1, and one value of the speed coefficient and the jerking coefficient is provided, with the speed coefficient being 0.6 and the jerking coefficient being 0.4.

[0032] The values ​​of the preset vehicle average speed and the preset vehicle jerking number can be determined by the user according to the actual application scenario, and a preset vehicle average speed and a preset vehicle jerking number are provided. The preset vehicle average speed is the average value of the vehicle average speeds corresponding to each valid unit data, and the preset vehicle jerking number is the average value of the vehicle jerking number corresponding to each valid unit data.

[0033] Specifically, valid unit data is determined according to the element characterization value, and a data screening method of the target spectral data is determined according to the number of valid unit data; If the number of valid unit data is greater than or equal to the preset number, the data screening method is to screen according to the correlation coefficient; If the number of valid unit data is less than the preset number, the data screening method is to screen Ф valid unit data in descending order according to the element representation value; The valid unit data is unit data whose element characterization value is greater than a preset element characterization value.

[0034] Among them, the number of valid unit data is the number of valid unit data in a target spectral data. The values ​​of the preset number and the preset element characterization value can be determined by the user according to the actual application scenario. The larger the values ​​of the preset number and the preset element characterization value, the more representative the target spectral data is in reflecting the clutch wear condition, and thus the accuracy of the clutch performance degradation model prediction is better. A preset number and a preset element characterization value are provided, and the preset number is 10. The method for confirming the preset element characterization value is to record the average value of the element characterization value corresponding to the valid unit data that can meet the user's needs in the historical data processing process as the preset element characterization value.

[0035] The user can determine the value of Ф according to the actual application scenario. The higher the user's demand for the accuracy of the clutch performance degradation model prediction, the larger the value of Ф. One value of Ф is provided, and the preset number is 4.

[0036] The calculation formula for the correlation coefficient r of two valid unit data is: Where n is the number of time points within the monitoring time corresponding to a single valid unit data; and are the concentration values ​​corresponding to the i-th time point during the monitoring time corresponding to the two valid unit data, for The average value of the concentration values ​​corresponding to each time point during the monitoring time corresponding to the corresponding effective unit data, for The average value of the concentration values ​​corresponding to each time point within the monitoring time corresponding to the corresponding valid unit data, i=1, 2, 3, ..., n.

[0037] Specifically, screening based on correlation coefficients includes: For a target spectral data, the valid unit data with the largest element characterization value in the target spectral data is recorded as the target unit, and other valid unit data in the target spectral data excluding the target unit is recorded as the reference unit. The correlation coefficient between the target unit and each reference unit is detected, and Ф-1 reference units are screened in descending order of the correlation coefficient. The target unit and the screened Ф-1 reference units are recorded as screened data.

[0038] Specifically, the data processing mode of the valid unit data is determined according to the unit data status of the valid unit data; If the unit data state is the first preset unit data state, segmentation is performed according to the concentration value; If the unit data state is the second preset unit data state, segmentation is performed according to the torque threshold.

[0039] Specifically, the unit data state is determined according to the data segment reference value and the characteristic threshold value, and the unit data state includes: A first preset unit data state in which the data segment reference value is less than a preset data segment reference value and the feature threshold is less than a preset feature threshold; A second preset unit data state in which the data segment reference value is greater than or equal to the preset data segment reference value or the feature threshold is greater than or equal to the preset feature threshold; The data segment reference value is determined according to the number of characteristic segments and the maximum slope deviation, and the characteristic threshold is determined according to the data increase and the unit data duration.

[0040] Among them, the data segment reference value = (number of characteristic segments / preset number of characteristic segments) × segment coefficient + (maximum slope deviation / preset maximum slope deviation) × slope coefficient. The number of characteristic segments is confirmed by drawing a spectral image corresponding to a valid unit data with the monitoring time as the horizontal axis and the concentration value as the vertical axis, dividing the spectral image into a number of spectral segments with the same horizontal axis length, performing an associated segment analysis on each spectral segment according to the time sequence of the spectral image, and when performing an associated segment analysis on a single spectral segment, recording the spectral segment as a target spectral segment, detecting the slope difference between each spectral segment and the target spectral segment after the time sequence of the target spectral segment, if the slope difference between the spectral segment and the target spectral segment is less than the preset slope difference, recording it as an associated spectral segment of the target spectral segment, and if the slope difference between the spectral segment and the target spectral segment is greater than or equal to the preset slope difference, stopping the associated segment analysis for the target spectral segment, and uniformly recording the target spectral segment and the associated spectral segment corresponding to it as one associated segment, and recording the number of each associated segment and the spectral segment not recorded as an associated segment as the number of characteristic segments. For two spectral sections, the slope difference is equal to the absolute value of the difference between the section slope of one spectral section and the section slope of another spectral section. For a single spectral section, the concentration value corresponding to the leftmost time point of the spectral section is recorded as the first content, and the concentration value corresponding to the rightmost time point of the spectral section is recorded as the second content. The section slope of the spectral section = (second content - first content) / spectral section length, and the spectral section length is the length of the horizontal axis corresponding to the spectral section. The value of the preset slope difference can be determined by the user according to the actual application scenario. The smaller the value of the preset slope difference, the greater the accuracy of the data in the associated section being the same wear stage. A preset slope difference value is provided. The preset slope difference is the average value of the slope differences corresponding to the associated sections that can meet the user's needs during the historical data processing process.

[0041] The maximum slope deviation is confirmed in the following manner: each associated paragraph and a spectral paragraph not recorded as an associated paragraph is recorded as a characteristic paragraph, the characteristic slope of each characteristic paragraph is detected, and the difference between the maximum characteristic slope and the minimum characteristic slope is recorded as the maximum slope deviation, wherein the characteristic slopes of associated paragraphs and spectral paragraphs not recorded as associated paragraphs are confirmed in different manners, the characteristic slope of an associated paragraph is the average value of the paragraph slopes of each spectral paragraph corresponding to a single associated paragraph, and for a single spectral paragraph not recorded as an associated paragraph, the characteristic slope is the paragraph slope of the single spectral paragraph not recorded as an associated paragraph.

[0042] It can be understood that the values ​​of the paragraph coefficient and the slope coefficient can be obtained by the user through deep learning convolutional neural network learning on historical records. It can be understood that the present invention reflects the distribution of feature paragraphs in the effective unit data through data paragraph reference values. The user can use deep learning through historical user data to obtain the number of feature paragraphs and the maximum slope deviation. The impact of the distribution of feature paragraphs in the effective unit data, and then select the corresponding values ​​of the paragraph coefficient and the slope coefficient, wherein the paragraph coefficient + slope coefficient = 1, and a paragraph coefficient and a slope coefficient value are provided, the paragraph coefficient is 0.5, and the slope coefficient is 0.5.

[0043] The values ​​of the preset number of feature paragraphs and the preset maximum slope deviation can be determined by the user according to the actual application scenario. The preset number of feature paragraphs and the preset maximum slope deviation are provided. The preset number of feature paragraphs is the average value of the number of feature paragraphs corresponding to each valid unit data, and the preset maximum slope deviation is the average value of the maximum slope deviation corresponding to each valid unit data.

[0044] Feature threshold = data increase / preset data increase × increase coefficient + unit data duration / preset unit data duration × duration coefficient, data increase is the difference between the maximum concentration value and the minimum concentration value in a single valid unit data, unit data duration is the monitoring time length corresponding to a single valid unit data, and the values ​​of the increase coefficient and the duration coefficient can be obtained by users through deep learning convolutional neural network learning on historical records. It can be understood that the present invention reflects the influence of the effective unit data characteristics through feature thresholds, and users can use deep learning through historical user data to obtain the influence of data increase and unit data duration on the effective unit data characteristics, and then select the values ​​of the increase coefficient and the duration coefficient accordingly, where the increase coefficient + duration coefficient = 1, and one value of the increase coefficient and the duration coefficient is provided, with the increase coefficient being 0.7 and the duration coefficient being 0.3.

[0045] The values ​​of the preset data increase and the preset unit data duration can be determined by the user according to the actual application scenario. A value of the preset data increase and the preset unit data duration is provided. The preset data increase is the average value of the data increase corresponding to each valid unit data, and the preset unit data duration is the average value of the unit data duration corresponding to each valid unit data.

[0046] The values ​​of the preset data paragraph reference value and the preset feature threshold can be determined by the user according to the actual application scenario. The higher the user's demand for the accuracy of the clutch performance degradation model prediction, the smaller the values ​​of the preset data paragraph reference value and the preset feature threshold. A value of a preset data paragraph reference value and a preset feature threshold is provided. The preset data paragraph reference value is the average value of the data paragraph reference values ​​that can meet the user's needs during the historical data processing. The preset data paragraph reference value is the average value of the feature thresholds that can meet the user's needs during the historical data processing.

[0047] Specifically, segmentation is performed based on concentration values; For a valid unit of data, the starting concentration point and the ending concentration point of the data segment whose concentration value is greater than the first preset concentration and less than the second preset concentration are used as segmentation points for segmentation; The first preset concentration is less than the second preset concentration.

[0048] Among them, a data segment is a segment containing concentration values ​​corresponding to several time points. For a data segment, the starting concentration point is the concentration value corresponding to the first time point of the data segment in the time sequence, and the ending concentration point is the concentration value corresponding to the last time point of the data segment in the time sequence.

[0049] The first preset concentration is greater than 0. The user can determine the values ​​of the first preset concentration and the second preset concentration according to the actual application scenario. A first preset concentration and a second preset concentration are provided. For a valid unit data, the valid unit data records with the same elements as the elements corresponding to the valid unit data are detected during the historical data processing process. The average value of the concentration values ​​corresponding to the starting concentration point that can meet the user's segmentation requirements is recorded as the first preset concentration, and the average value of the concentration values ​​corresponding to the ending concentration point that can meet the user's segmentation requirements is recorded as the second preset concentration.

[0050] Specifically, segmentation is performed based on torque thresholds; For the torque data corresponding to a valid unit data, a torque segment whose torque reference coefficient is greater than or equal to a preset torque reference coefficient is recorded as a characteristic torque segment, and distribution detection is performed on each characteristic torque segment. When distribution detection is performed on a single characteristic torque segment, the characteristic torque segment is recorded as a target characteristic torque segment, and a distance reference value between each characteristic torque segment after the time sequence of the target characteristic torque segment is detected. If the distance reference value between each characteristic torque segment after the time sequence of the target characteristic torque segment is less than the preset distance reference value, it is recorded as an associated characteristic torque segment of the target characteristic torque segment. If the distance reference value between each characteristic torque segment after the time sequence of the target characteristic torque segment is greater than or equal to the preset distance reference value, the distribution detection of the target characteristic torque segment is stopped, and the segment between the starting torque point of the target characteristic torque segment and the ending torque point of the associated characteristic torque segment with the largest distance reference value corresponding to the target characteristic torque segment is recorded as a segment combination; The effective unit data is segmented using the starting torque point and the ending torque point corresponding to the paragraph combination with the largest horizontal coordinate length as segmentation points; The torque threshold is determined according to a torque reference coefficient and a torque fluctuation coefficient. The torque reference coefficient is positively correlated with the torque mean value, and the torque fluctuation coefficient is positively correlated with the torque fluctuation value.

[0051] The method for confirming the torque segment is to draw a torque image corresponding to the effective unit data with the monitoring time as the horizontal axis and the torque value as the vertical axis, and use each turning point as an isolation point to obtain several torque segments; Torque threshold = torque reference coefficient + torque fluctuation coefficient. The torque mean is confirmed in the following way: for a single torque segment, the torque mean is the average value of the torque reference values ​​at each time point corresponding to the torque segment. For a single time point, the torque reference value = the torque value at the single time point / the piston pressure value at the single time point. The piston pressure value is the net pressure applied to the clutch piston. The torque fluctuation value = the maximum torque value - the minimum torque value. The maximum torque value is the maximum torque of the clutch in the torque data corresponding to the effective unit data, and the minimum torque value is the minimum torque of the clutch in the torque data corresponding to the effective unit data.

[0052] For a torque segment, the first time point corresponding to the torque segment in the time direction is recorded as the starting torque point, and the last time point corresponding to the horizontal coordinate of the torque segment in the time direction is recorded as the ending torque point. The distance reference value is confirmed by, for two torque segments, the distance between the ending point of the first torque segment in the time direction and the starting point of the second torque segment.

[0053] The values ​​of the preset distance reference value and the preset torque fluctuation value can be determined by the user according to the actual application scenario. It can be understood that the smaller the values ​​of the preset distance reference value and the preset torque fluctuation value are, the greater the accuracy of the data in the segmented section for the same wear stage, and thus the higher the accuracy of the clutch performance degradation model prediction. A preset distance reference value and a preset torque fluctuation value are provided, and the preset distance reference value is 3h. The method for confirming the preset torque fluctuation value is to detect the torque fluctuation value corresponding to the record with the same element as the effective unit data corresponding to the current target spectral data in the historical data processing process, and record the average value of the torque fluctuation values ​​corresponding to the historical data processing process in which the segmentation effect meets the user's needs as the preset torque fluctuation value.

[0054] Specifically, whether the segmented paragraph is qualified is determined according to the comprehensive deviation value; Detecting the starting temperature point and the ending temperature point of the temperature segment whose temperature reference value is greater than or equal to the preset temperature reference value, and determining the comprehensive deviation value according to the sum of the absolute value of the difference between the starting temperature point and the starting torque point and the absolute value of the difference between the ending temperature point and the ending torque point; If the comprehensive deviation value is greater than or equal to the preset comprehensive deviation value, the segmentation fails, and the starting midpoint and the ending midpoint are selected as segmentation points for segmentation; If the comprehensive deviation value is less than the preset comprehensive deviation value, the segmentation is qualified.

[0055] Among them, the temperature reference value is the temperature of the clutch at each time point, the temperature section is a section containing the temperatures corresponding to several time points, the starting temperature point of the temperature section is the first time point corresponding to the temperature section in the time direction, and the ending temperature point is the last time point corresponding to the temperature section in the time direction; For a valid unit data, the value of the preset temperature reference value can be determined by the user according to the actual application scenario, and a preset temperature reference value is provided. The preset temperature value is the temperature at which the clutch lasts the longest during the detection process corresponding to the valid unit data. For a valid unit data, the value of the preset comprehensive deviation value can be determined by the user according to the actual application scenario. The higher the user's demand for the accuracy of the clutch performance degradation model prediction, the smaller the value of the preset comprehensive deviation value is. A preset comprehensive deviation value is provided, and the preset comprehensive deviation value is 4h.

[0056] The starting middle point = (starting temperature point + starting torque point) / 2, and the ending middle point = (ending temperature point + ending torque point) / 2.

[0057] Specifically, the element characterization value is determined according to the sum of the unit characterization values ​​corresponding to each data point.

[0058] Among them, for a data point, the unit representation value corresponding to the data point = -(data point probability value × logarithm of the data point probability value with base 2), and the data representation value is equal to the sum of the unit representation values ​​corresponding to each data point.

[0059] The data points are the concentration values ​​corresponding to each time point, and the probability value of the data point = the number of time points corresponding to the same concentration value / the total number of time points.

[0060] In the present invention, in the first preset unit data state, the starting concentration point of the data segment whose concentration value is greater than the first preset concentration and less than the second preset concentration is recorded as the first segmentation point, and the ending concentration point of the data segment whose concentration value is greater than the first preset concentration and less than the second preset concentration is recorded as the second segmentation point; In the second preset unit data state, if the segmentation is qualified, the starting torque point corresponding to the segment combination with the largest horizontal coordinate length is recorded as the first segmentation point, and the ending torque point corresponding to the segment combination with the largest horizontal coordinate length is recorded as the second segmentation point; If the segmentation fails, the starting midpoint is recorded as the first segmentation point, and the ending midpoint is recorded as the second segmentation point; The concentration value corresponding to the first segmentation point and the concentration values ​​corresponding to each time point before the first segmentation point in the time sequence are recorded as the first segmentation section, the concentration values ​​corresponding to the time points between the first segmentation point and the second segmentation point are recorded as the second segmentation section, and the concentration value corresponding to the second segmentation point and the concentration values ​​corresponding to each time point after the second segmentation point in the time sequence are recorded as the third segmentation section; Among them, the wear stage corresponding to the first segmented paragraph is the initial wear stage, the wear stage corresponding to the second segmented paragraph is the stable wear stage, and the wear stage corresponding to the third segmented paragraph is the severe wear stage. The first segmented paragraph, the second segmented paragraph and the third segmented paragraph corresponding to each valid unit data are modeled and predicted using the Wiener process respectively. This is content that is easy for technical personnel in this field to understand and will not be elaborated on in detail.

[0061] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A clutch intelligent detection method based on multi-dimensional data acquisition, characterized in that: include: Get clutch related data uploaded by the user; Determining target spectral data according to vehicle characterization coefficients; Determine the valid unit data according to the element characterization value, and determine the target spectrum data according to the number of valid unit data. The data screening method is to screen according to the correlation coefficient, or to screen Ф valid unit data in descending order according to the element characterization value; Determine the unit data state according to the data segment reference value and the characteristic threshold value; Determining, according to the unit data state of the effective unit data, a data processing method of the effective unit data, that is, segmenting according to a concentration value, or segmenting according to a torque threshold value; Under the preset conditions, whether the segmented paragraph is qualified is determined according to the comprehensive deviation value; The preset condition is that the segmentation is completed according to the torque threshold.

2. The intelligent clutch detection method based on multi-dimensional data acquisition according to claim 1 is characterized in that: Determining target spectral data according to vehicle characterization coefficients; Dividing the data sequence of the spectral data and obtaining a number of pre-screened spectral data, and recording each pre-screened spectral data as target spectral data; The data sequence of the spectral data is the order of vehicle characterization coefficients from large to small; The vehicle characterization coefficient is determined according to the block frequency reference value and the environment characterization value; The relationship between the vehicle characterization coefficient and the frequency blocking reference value is a positive correlation, and the relationship between the vehicle characterization coefficient and the environment characterization value is a positive correlation.

3. The intelligent clutch detection method based on multi-dimensional data acquisition according to claim 2 is characterized in that: Determine effective unit data according to the element characterization value, and determine the data screening method of the target spectrum data according to the number of effective unit data; If the number of valid unit data is greater than or equal to the preset number, the data screening method is to screen according to the correlation coefficient; If the number of valid unit data is less than the preset number, the data screening method is to screen Ф valid unit data in descending order according to the element representation value; The valid unit data is unit data whose element characterization value is greater than a preset element characterization value.

4. The intelligent clutch detection method based on multi-dimensional data acquisition according to claim 3 is characterized in that: Screening based on correlation coefficients includes: For a target spectral data, the valid unit data with the largest element characterization value in the target spectral data is recorded as the target unit, and other valid unit data in the target spectral data excluding the target unit is recorded as the reference unit. The correlation coefficient between the target unit and each reference unit is detected, and Ф-1 reference units are screened in descending order of the correlation coefficient. The target unit and the screened Ф-1 reference units are recorded as screened data.

5. The intelligent clutch detection method based on multi-dimensional data collection according to claim 4 is characterized in that: Determine the data processing mode of the valid unit data according to the unit data status of the valid unit data; If the unit data state is the first preset unit data state, segmentation is performed according to the concentration value; If the unit data state is the second preset unit data state, segmentation is performed according to the torque threshold.

6. The intelligent clutch detection method based on multi-dimensional data collection according to claim 5 is characterized in that: The unit data state is determined according to the data segment reference value and the characteristic threshold value, and the unit data state includes: A first preset unit data state in which the data segment reference value is less than a preset data segment reference value and the feature threshold is less than a preset feature threshold; A second preset unit data state in which the data segment reference value is greater than or equal to the preset data segment reference value or the feature threshold is greater than or equal to the preset feature threshold; The data segment reference value is determined according to the number of characteristic segments and the maximum slope deviation, and the characteristic threshold is determined according to the data increase and the unit data duration.

7. The intelligent clutch detection method based on multi-dimensional data collection according to claim 5 is characterized in that: Segmentation based on concentration values; For a valid unit of data, the starting concentration point and the ending concentration point of the data segment whose concentration value is greater than the first preset concentration and less than the second preset concentration are used as segmentation points for segmentation; The first preset concentration is less than the second preset concentration.

8. The intelligent clutch detection method based on multi-dimensional data collection according to claim 5 is characterized in that: Segmentation based on torque threshold; For torque data corresponding to a valid unit data, a torque segment whose torque reference coefficient is greater than or equal to a preset torque reference coefficient is recorded as a characteristic torque segment, and distribution detection is performed on each characteristic torque segment. When distribution detection is performed on a single characteristic torque segment, the characteristic torque segment is recorded as a target characteristic torque segment, and a distance reference value between each characteristic torque segment after the time sequence of the target characteristic torque segment is detected. If the distance reference value between each characteristic torque segment after the time sequence of the target characteristic torque segment is less than the preset distance reference value, it is recorded as an associated characteristic torque segment of the target characteristic torque segment. If the distance reference value between each characteristic torque segment after the time sequence of the target characteristic torque segment is greater than or equal to the preset distance reference value, distribution detection of the target characteristic torque segment is stopped, and the segment between the starting torque point of the target characteristic torque segment and the ending torque point of the associated characteristic torque segment with the largest distance reference value corresponding to the target characteristic torque segment is recorded as a segment combination; The effective unit data is segmented using the starting torque point and the ending torque point corresponding to the paragraph combination with the largest horizontal coordinate length as segmentation points; The torque threshold is determined according to a torque reference coefficient and a torque fluctuation coefficient. The torque reference coefficient is positively correlated with the torque mean value, and the torque fluctuation coefficient is positively correlated with the torque fluctuation value.

9. The intelligent clutch detection method based on multi-dimensional data collection according to claim 5 is characterized in that: Determine whether the segmented paragraph is qualified according to the comprehensive deviation value; Detecting the starting temperature point and the ending temperature point of the temperature segment whose temperature reference value is greater than or equal to the preset temperature reference value, and determining the comprehensive deviation value according to the sum of the absolute value of the difference between the starting temperature point and the starting torque point and the absolute value of the difference between the ending temperature point and the ending torque point; If the comprehensive deviation value is greater than or equal to the preset comprehensive deviation value, the segmentation fails, and the starting midpoint and the ending midpoint are selected as segmentation points for segmentation; If the comprehensive deviation value is less than the preset comprehensive deviation value, the segmentation is qualified.

10. The intelligent clutch detection method based on multi-dimensional data collection according to claim 3, characterized in that: The element characterization value is determined according to the sum of the unit characterization values ​​corresponding to each data point.

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