A coupled vibration data analysis method for vehicle-bridge

Abstract

The present application relates to the technical field of coupled vibration data analysis of a car and a sedan, and specifically to a coupled vibration data analysis method for a vehicle bridge, the method comprising: using a vibration acceleration sensor, a load sensor, a speed sensor, and a pressure sensor to obtain vibration data, vehicle load, vehicle speed, and tire pressure at each moment of the vehicle bridge, and obtain each vibration peak of each cluster; determining the volatility coefficient of each vibration peak of each cluster; determining the vehicle speed variation coefficient of each vibration peak of each cluster; determining the tire pressure variation coefficient of each vibration peak of each cluster; based on the vehicle speed variation coefficient and the tire pressure variation coefficient, determining the vibration abnormality of each vibration peak of each cluster, obtaining the abnormal vibration index of each vibration peak, and monitoring and managing the health status of the vehicle bridge. The present application improves the accuracy of the assessment of the health status of the vehicle bridge, so as to monitor and manage the health status of the vehicle bridge.

Description

Technical Field

[0001] The present application relates to the technical field of coupled vibration data analysis of a vehicle and a sedan, and in particular to a coupled vibration data analysis method for a vehicle axle. Background Art

[0002] The axle is a part of the car, also known as the axle, which is responsible for transmitting the engine power to the wheels to propel the car forward. The axle plays an important role in the car. It not only transmits power, but also needs to bear the weight of the vehicle and the impact of the road surface. At the same time, it must be able to adapt to different road conditions. The stability and reliability of the axle directly affect the driving safety and riding experience of the car.

[0003] Traditional methods usually analyze the static strength of axles when evaluating the health of axles. However, axles are affected by many factors during the actual driving of a car. For example, the vibration data of axles will show different characteristics when driving on flat or rough roads. When the vehicle load is inconsistent, the vibration data of axles will also be inconsistent. Traditional technology only monitors the operating status of axles based on sensors to determine whether any of the monitoring data has data anomalies. It does not combine multiple data to conduct coupled evaluation of the health status of axles. There is insufficient use of data, which can easily cause misdetection of axle status and inaccurate evaluation of the health status of axles. Summary of the invention

[0004] In order to solve the above technical problems, a coupled vibration data analysis method for a vehicle bridge is provided to solve the existing problems.

[0005] The solution to the technical problem of the present application is to provide a coupled vibration data analysis method for a vehicle bridge, comprising the following steps:

[0006] Use various sensors to obtain the vibration data of the axle, vehicle load, vehicle speed and tire pressure at all times;

[0007] Cluster the vehicle loads, and obtain the vibration peaks and vibration troughs of each cluster according to the peaks of the vibration data at each moment;

[0008] According to the difference degree of the peak values ​​of the vibration peaks in each cluster and the discrete degree of the local vibration data, the volatility coefficient of each vibration peak of each cluster is determined;

[0009] According to the time difference between the trough and the vibration peak of each cluster at all times, and the discreteness of the vehicle speed, the vehicle speed variation coefficient of each vibration peak of each cluster is determined;

[0010] Determine the tire pressure variation coefficient of each vibration peak of each cluster according to the time difference between the peak of the tire pressure and the vibration peak at all times of each cluster, and the correlation between the tire pressure and vibration data;

[0011] Based on the vehicle speed variation coefficient and the tire pressure variation coefficient, the abnormal vibration index of each vibration peak is obtained, and the health status of the vehicle axle is monitored and managed.

[0012] Preferably, the method for obtaining each vibration peak and vibration trough of each cluster is:

[0013] Cluster the vehicle loads at all times to obtain multiple clusters;

[0014] According to the vehicle load at each moment in each cluster, the vibration data at the corresponding moment is extracted to obtain the vibration data at each moment in each cluster;

[0015] The peaks and troughs of the vibration data at all times in each cluster are obtained to obtain each vibration peak and each vibration trough of each cluster.

[0016] Preferably, the determining of the volatility coefficient of each vibration peak of each cluster comprises:

[0017] The vibration data at all moments between two vibration troughs adjacent to each vibration peak in each cluster are used to form a peak vibration sequence of each vibration peak in each cluster;

[0018] Determining the vibration discreteness of each vibration peak of each cluster according to the discreteness of each element in the peak vibration sequence;

[0019] Taking the mean of all elements in the peak vibration sequence as the first mean of each vibration peak of each cluster;

[0020] The vibration dispersion and the first mean are combined to determine the vibration fluctuation coefficient of each vibration peak of each cluster;

[0021] Determining the peak difference coefficient of each vibration peak of each cluster according to the difference of the peak values ​​of each vibration peak in each cluster;

[0022] The peak difference coefficient and the vibration fluctuation coefficient are fused to determine the fluctuation coefficient of each vibration peak of each cluster.

[0023] Preferably, the step of determining the peak difference coefficient of each vibration peak of each cluster comprises:

[0024] Determine the peak difference of each vibration peak according to the difference between the peak value of each vibration peak in each cluster and the minimum vibration data in the corresponding cluster;

[0025] Determine the peak difference of each vibration peak according to the peak difference between each vibration peak in each cluster and the other vibration peaks;

[0026] The extreme peak difference and the inter-peak difference are fused to determine the peak difference coefficient of each vibration peak of each cluster.

[0027] Preferably, the determining of the vehicle speed variation coefficient of each vibration peak of each cluster includes:

[0028] According to the vibration data at each moment in the peak vibration sequence, the vehicle speed and tire pressure at the corresponding moment are respectively extracted to form a peak vehicle speed sequence and a peak tire pressure sequence of each vibration peak in each cluster;

[0029] Determine the corresponding vehicle speed factor of each vibration wave peak of each cluster according to the time interval difference between each vibration wave peak and the trough of its peak vehicle speed sequence;

[0030] Determining the vehicle speed fluctuation coefficient of each vibration wave peak of each cluster according to the discrete distribution of each element in the peak vehicle speed sequence;

[0031] Determine the instantaneous vibration difference of each vibration peak of each cluster according to the difference between the vibration data corresponding to each vibration peak of each cluster and the vibration data of the previous moment;

[0032] The corresponding vehicle speed factor, the instantaneous vibration difference and the vehicle speed fluctuation coefficient are integrated to determine the road roughness of each vibration peak of each cluster;

[0033] The ratio of the volatility coefficient to the road roughness is used as the vehicle speed variation coefficient of each vibration peak of each cluster.

[0034] Preferably, the determining of the corresponding vehicle speed factor of each vibration peak of each cluster includes:

[0035] The corresponding time of each vibration peak in each cluster is recorded as the vibration peak time of each vibration peak;

[0036] Obtaining corresponding moments of all troughs of the peak vehicle speed sequence of each vibration wave peak, and recording them as vehicle speed trough moments of each vehicle speed trough in the peak vehicle speed sequence of each vibration wave peak;

[0037] The difference between the vehicle speed trough moment of each vehicle speed trough and the vibration peak moment of the corresponding vibration peak is recorded as the interval time of each vehicle speed trough in the peak vehicle speed sequence of each vibration peak;

[0038] The minimum interval duration of the interval durations of all vehicle speed troughs is recorded as the minimum time difference of each vibration peak;

[0039] If the minimum time difference is less than or equal to the preset time length, the corresponding vehicle speed factor of each vibration peak is assigned the preset time length. If the minimum time difference is greater than the preset time length, the corresponding vehicle speed factor of each vibration peak is assigned 0.

[0040] Preferably, the determining of the vehicle speed fluctuation coefficient of each vibration peak of each cluster includes:

[0041] Calculate the coefficient of variation of the peak vehicle speed sequence of each vibration peak of each cluster;

[0042] Calculating the range of the peak vehicle speed sequence of each vibration peak of each cluster;

[0043] The vehicle speed fluctuation coefficient of each vibration wave peak of each cluster is positively correlated with the coefficient of variation and the range.

[0044] Preferably, determining the tire pressure variation coefficient of each vibration peak of each cluster includes:

[0045] Determine the tire pressure factor corresponding to each vibration peak in each cluster according to the time interval difference between each vibration peak and the peak tire pressure sequence;

[0046] According to the correlation between the vibration data at all times and the tire pressure, a first correlation coefficient is obtained;

[0047] According to the correlation between the peak vibration sequence of each vibration peak of each cluster and the peak tire pressure sequence, a second correlation coefficient of each vibration peak of each cluster is obtained;

[0048] Determining the correlation degree of each vibration peak of each cluster according to the difference between the first correlation coefficient and the second correlation coefficient;

[0049] The tire pressure variation coefficient of each vibration wave peak of each cluster is positively correlated with the volatility coefficient and the correlation degree, and negatively correlated with the corresponding tire pressure factor.

[0050] Preferably, the step of determining the tire pressure factor corresponding to each vibration peak in each cluster includes:

[0051] Obtaining corresponding moments of all peaks of the peak tire pressure sequence, recorded as tire pressure peak moments of each tire pressure peak in the peak tire pressure sequence of each vibration peak;

[0052] The difference between the tire pressure peak time of each tire pressure peak and the vibration peak time of the corresponding vibration peak is recorded as the peak time difference of each tire pressure peak in the peak tire pressure sequence of each vibration peak;

[0053] The minimum value of the peak time difference of all tire pressure peaks is recorded as the minimum peak time difference of each vibration peak;

[0054] If the minimum peak time difference is less than or equal to the preset time length, the corresponding tire pressure factor of each vibration peak is assigned the preset time length; if the minimum peak time difference is greater than the preset time length, the corresponding tire pressure factor of each vibration peak is assigned 0.

[0055] Preferably, the determining of the abnormal vibration index of each vibration peak to monitor and manage the health status of the axle includes:

[0056] The vibration abnormality of each vibration peak of each cluster is positively correlated with the vehicle speed variation coefficient and the tire pressure variation coefficient respectively;

[0057] For the vibration anomaly of all vibration peaks of all clusters, an abnormal detection algorithm is used to obtain the abnormal vibration index of each vibration peak;

[0058] The abnormal vibration indexes of all vibration peaks are normalized. If the normalized abnormal vibration index is greater than a preset abnormal threshold, it is determined that the health status of the axle is abnormal.

[0059] This application has at least the following beneficial effects:

[0060] The present application clusters the vehicle load and determines the volatility coefficient of each vibration peak according to the difference in the peaks of the vibration data in each cluster. The beneficial effect is that the stability of the vibration data of the axle is evaluated when the axle is in good health and the vehicle load is consistent, reflecting the degree of fluctuation of the axle vibration data; the roughness of the road surface is obtained by analyzing the difference in the trend change between the vehicle speed and the vibration data, and the vehicle speed change coefficient is determined. The beneficial effect is that the change of the vehicle speed and the vibration data is evaluated, reflecting the roughness of the road surface, and the vehicle speed change coefficient is used to reflect the health of the axle; secondly, the correlation degree of the trend change between the tire pressure and the vibration data is determined to determine the tire pressure change coefficient. The beneficial effect is that the change of the correlation characteristic between the tire pressure and the vibration is evaluated, and the tire pressure change coefficient is used to reflect the health of the axle; the vibration abnormality of each vibration peak is determined to obtain the abnormal vibration index of each vibration peak, and the health of the axle is detected and managed. The beneficial effect is that the possibility of abnormality during peak vibration is analyzed, the accuracy of the axle health assessment is improved, and the monitoring and management of the axle health is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] A coupled vibration data analysis method for a vehicle bridge of the present application is further described in detail below in conjunction with the accompanying drawings.

[0062] Figure 1A flowchart of the steps of a coupled vibration data analysis method for a vehicle bridge provided in the present application;

[0063] Figure 2 A flowchart of the steps of the method for obtaining the volatility coefficient of each vibration peak of each cluster provided in the present application;

[0064] Figure 3 A flowchart of the steps of the method for obtaining the road surface roughness of each vibration wave peak of each cluster provided by the present application;

[0065] Figure 4 A flowchart of the steps of the method for obtaining the tire pressure variation coefficient of each vibration peak of each cluster provided in the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the coupled vibration data analysis method for axles proposed in the present application is further described in detail below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0068] See also Figure 1 , which shows a flowchart of a coupled vibration data analysis method for a vehicle bridge provided by an embodiment of the present application, the method comprising the following steps:

[0069] Step 1: Use various sensors to obtain the vibration data of the axle, vehicle load, vehicle speed and tire pressure at each moment.

[0070] Vibration acceleration sensors and load sensors are used to collect vibration data of the axle and vehicle load when the vehicle is driving, and the vibration data and vehicle load at each moment are obtained;

[0071] Place speed sensors and pressure sensors at the four tires of the vehicle respectively, collect the speed and pressure of the four tires when the vehicle is running, calculate the average speed and pressure of the four tires at the same time as the vehicle speed and tire pressure at that time, thereby obtaining the vehicle speed and tire pressure at each time;

[0072] The collection time interval is t, the collection time is T, and the data collected at each moment is preprocessed to obtain the vibration data, vehicle load, vehicle speed and tire pressure at each moment.

[0073] Preferably, as an embodiment of the present application, the collection time interval is 1s, the collection time is 0.5 hours, and the maximum value normalization processing is used for the data collected at all times to eliminate the dimension effect, and the vibration data, vehicle load, vehicle speed and tire pressure at each time are obtained. Among them, the maximum value normalization technology is a well-known technology and will not be repeated here.

[0074] At this point, the vibration data, vehicle load, vehicle speed and tire pressure at each moment are obtained.

[0075] Step 2: cluster the vehicle loads, and obtain the vibration peaks and vibration troughs of each cluster according to the peak values ​​of the vibration data at each moment; determine the volatility coefficient of each vibration peak of each cluster according to the peak value difference of each vibration peak of each cluster and the discreteness of the local vibration data.

[0076] Normally, factors such as vehicle load and road conditions will have a greater impact on the vibration of the axle, while the vehicle speed has a smaller impact on the vibration of the axle. When the axle is in good health and the vehicle load is consistent (the number of passengers is consistent), if the car is driving on a good road, the vibration data of the axle is relatively stable, and the vibration amplitude changes very little. Therefore, the fluctuation degree of the axle vibration data under the condition of consistent vehicle load is analyzed. Specifically:

[0077] Perform cluster analysis on the vehicle load at all times to obtain multiple clusters;

[0078] Preferably, as an embodiment of the present application, a K-means clustering algorithm is used for clustering, wherein the number of clusters is determined by a silhouette coefficient, wherein the K-means algorithm and the silhouette coefficient are well-known technologies and will not be described in detail here.

[0079] It can be understood that, in this embodiment, the K-means clustering algorithm is used for clustering to obtain multiple cluster clusters. As other implementation methods, the implementer can use other methods of the prior art to perform clustering analysis, such as hierarchical clustering algorithms, DBSCAN clustering algorithms, etc. This embodiment does not impose any special restrictions on this.

[0080] Furthermore, the volatility of the axle vibration data is evaluated through the vibration peaks and vibration troughs, specifically:

[0081] According to the vehicle load at each moment in each cluster, the vibration data at the corresponding moment is extracted to obtain the vibration data at each moment in each cluster;

[0082] Acquire the peaks and troughs of the vibration data at all times in each cluster, and obtain the vibration peaks and vibration troughs of each cluster;

[0083] Preferably, as an embodiment of the present application, a second-order difference algorithm is used to obtain the peaks and troughs of the vibration data at all times in each cluster, wherein the second-order difference algorithm is a well-known technology and will not be described in detail in this embodiment.

[0084] It is understandable that obtaining the peaks and troughs of a set of data does not only use the second-order difference algorithm. As other implementation methods, the implementer can use other methods of the prior art, such as the derivative method, the local extreme value method, the AMPD algorithm, etc. This embodiment does not impose any special restrictions on this.

[0085] Furthermore, the differences between different vibration peaks within each cluster can reflect the vibration stability of the axle when the axle is in good health and the vehicle load is consistent, reflecting the degree of fluctuation of the axle vibration data, specifically:

[0086] Determine the peak difference of each vibration peak according to the difference between the peak value of each vibration peak of each cluster and the minimum vibration data in the corresponding cluster;

[0087] Determine the peak difference between each vibration peak according to the peak difference between each vibration peak of each cluster and the remaining vibration peaks;

[0088] Preferably, as an embodiment of the present application, the absolute value of the difference between the peak value of each vibration peak of each cluster and the minimum vibration data in the corresponding cluster is taken as the peak difference; the average of the absolute values ​​of the difference between each vibration peak of each cluster and the peak values ​​of the remaining vibration peaks is taken as the peak-to-peak difference of each vibration peak.

[0089] The extreme peak difference and the inter-peak difference are merged to determine the peak difference coefficient of each vibration peak of each cluster;

[0090] It is understandable that fusion can represent a multiplication relationship, an addition relationship, and the idempotence of an exponential function, and this application does not impose any special restrictions on this.

[0091] Preferably, as an embodiment of the present application, the peak difference coefficient of each vibration peak of each cluster is the product of the extreme peak difference and the peak-to-peak difference.

[0092] Preferably, as another embodiment of the present application, the peak difference coefficient of each vibration peak of each cluster is the sum of the extreme peak difference and the peak-to-peak difference.

[0093] It should be noted that the smaller the extreme peak difference is, the greater the possibility that the peak value of each vibration wave peak belongs to normal fluctuation. The smaller the peak difference is, the smaller the difference between the peak value of each vibration wave peak and the peak values ​​of other wave peaks is. The smaller the peak difference coefficient is, the smaller the degree of fluctuation is, and the better the stability of the vibration data at each moment is.

[0094] The vibration data at all moments between two vibration troughs adjacent to each vibration peak in each cluster are used to form a peak vibration sequence of each vibration peak in each cluster;

[0095] Determining the vibration discreteness of each vibration peak of each cluster according to the discreteness of each element in the peak vibration sequence;

[0096] Preferably, as an embodiment of the present application, the vibration dispersion of each vibration peak of each cluster may be the variance of each element in the peak vibration sequence.

[0097] Preferably, as another embodiment of the present application, the standard deviation of each element in the peak vibration sequence is calculated as the vibration dispersion of each vibration peak of each cluster.

[0098] It can be understood that the vibration dispersion of this embodiment is calculated by calculating the standard deviation and variance of each element in the peak vibration sequence. As other implementation methods, the implementer can use other methods of the prior art to calculate the degree of dispersion, such as coefficient of variation, information entropy, etc. This embodiment does not impose any special restrictions on this.

[0099] Taking the mean of all elements in the peak vibration sequence as the first mean of each vibration peak of each cluster;

[0100] The vibration dispersion and the first mean are combined to determine the vibration fluctuation coefficient of each vibration peak of each cluster;

[0101] Preferably, as an embodiment of the present application, the product of the vibration dispersion and the first mean value is used as the vibration fluctuation coefficient of each vibration peak of each cluster.

[0102] The peak difference coefficient and the vibration fluctuation coefficient are combined to determine the fluctuation coefficient of each vibration peak of each cluster;

[0103] Preferably, as an embodiment of the present application, the product of the peak difference coefficient and the vibration fluctuation coefficient is used as the fluctuation coefficient of each vibration peak of each cluster.

[0104] Preferably, as another embodiment of the present application, the sum of the peak difference coefficient and the vibration fluctuation coefficient is used as the fluctuation coefficient of each vibration peak of each cluster.

[0105] It should be noted that the smaller the volatility coefficient is, the smaller the fluctuation degree of the peak vibration sequence where each vibration peak is located is when the vehicle load is consistent, and the greater the possibility that the vibration data in the peak vibration sequence is a normal fluctuation; if the volatility coefficient is larger, it means that when the vehicle load is relatively consistent, the greater the possibility that the vibration data in the peak vibration sequence is an abnormal fluctuation.

[0106] Furthermore, the step flow chart of the method for obtaining the volatility coefficient of each vibration peak of each cluster provided in the present application is as follows: Figure 2 shown.

[0107] At this point, the volatility coefficient of each vibration peak of each cluster is obtained.

[0108] Step 3: Determine the vehicle speed variation coefficient of each vibration peak of each cluster according to the time difference between the trough and the vibration peak of each cluster at all times and the discreteness of the vehicle speed.

[0109] In the daily driving process of the car, when the road condition is bad, the vehicle speed is usually low and fluctuates greatly; at the same time, because the road condition is bad, even if the axle is in good health, the vibration data of the axle will increase and fluctuate more.

[0110] When gradually driving towards a pothole, the vehicle speed usually decreases gradually, and when reaching the pothole, the vehicle speed usually reaches a valley value; since the vehicle speed has little effect on vibration, the vibration data of the axle does not change much when the vehicle speed slows down, but due to the influence of the pothole, the vibration of the vehicle at the pothole will reach a peak value; the vibration level is significantly different from the vibration level at the previous moment, and shows an instantaneous increase trend. Based on the above analysis, the vibration data of the vehicle is analyzed by the vehicle speed.

[0111] Furthermore, due to the changing trend between the trough of vehicle speed and the peak of vibration, the influence of vehicle speed change on axle vibration can be reflected by analyzing the time interval difference between the trough of vehicle speed and the peak of vibration. Specifically:

[0112] According to the vibration data at each moment in the peak vibration sequence, the vehicle speed and tire pressure at the corresponding moment are respectively extracted to form a peak vehicle speed sequence and a peak tire pressure sequence of each vibration peak of each cluster;

[0113] The corresponding time of each vibration peak in each cluster is recorded as the vibration peak time of each vibration peak;

[0114] Obtaining corresponding moments of all troughs of the peak vehicle speed sequence of each vibration wave peak, and recording them as vehicle speed trough moments of each vehicle speed trough in the peak vehicle speed sequence of each vibration wave peak;

[0115] The difference between the vehicle speed trough moment of each vehicle speed trough and the vibration peak moment of the corresponding vibration peak is recorded as the interval time of each vehicle speed trough in the peak vehicle speed sequence of each vibration peak;

[0116] Preferably, as an embodiment of the present application, the absolute value of the difference between the vehicle speed trough moment of each vehicle speed trough and the vibration peak moment of the corresponding vibration peak is used as the interval duration of each vehicle speed trough in the peak vehicle speed sequence of each vibration peak.

[0117] The minimum interval duration of the interval durations of all vehicle speed troughs is recorded as the minimum time difference of each vibration peak;

[0118] If the minimum time difference is less than or equal to the preset time length, the corresponding vehicle speed factor of each vibration peak in each cluster is assigned the preset time length; if the minimum time difference is greater than the preset time length, the corresponding vehicle speed factor of each vibration peak in each cluster is assigned 0;

[0119] Preferably, as an embodiment of the present application, the preset time length is 3, and the AMPD algorithm is used to obtain the trough. As other implementation methods, the implementer can set the value of the time length according to the actual situation.

[0120] It should be noted that if the time difference between the peak value of the vibration and the valley value of the vehicle speed is less than or equal to the preset time length, that is, when the vehicle speed reaches the valley value, the vibration data increases suddenly and reaches the peak value, then the weight of the corresponding vehicle speed factor is increased.

[0121] Furthermore, when the road surface is rough, the axle vibration data will change significantly. Therefore, by analyzing the change in vehicle speed, the roughness of the road surface can be reflected, and the vibration state of the axle can be further reflected. Specifically:

[0122] Determine the instantaneous vibration difference of each vibration peak according to the difference between the vibration data corresponding to each vibration peak of each cluster and the vibration data at the previous moment;

[0123] Preferably, as an embodiment of the present application, the instantaneous vibration difference of each vibration peak can be calculated as the absolute value of the difference between the vibration data corresponding to each vibration peak of each cluster and the vibration data at the previous moment.

[0124] Calculate the coefficient of variation of the peak vehicle speed sequence of each vibration peak of each cluster, wherein the coefficient of variation is a well-known technology and will not be described in detail here;

[0125] Preferably, as another embodiment of the present application, the vehicle speed dispersion of each vibration wave peak can be calculated by calculating the standard deviation of the peak vehicle speed sequence of each vibration wave peak of each cluster.

[0126] Calculating the range of the peak vehicle speed sequence of each vibration peak of each cluster;

[0127] The vehicle speed fluctuation coefficient of each vibration wave peak of each cluster is positively correlated with the coefficient of variation and the range respectively;

[0128] Preferably, as an embodiment of the present application, the product of the coefficient of variation and the range is used as the vehicle speed fluctuation coefficient of each vibration peak of each cluster.

[0129] Preferably, as another embodiment of the present application, the sum of the coefficient of variation and the range is used as the vehicle speed fluctuation coefficient of each vibration peak of each cluster.

[0130] The corresponding vehicle speed factor, the instantaneous vibration difference and the vehicle speed fluctuation coefficient are integrated to determine the road roughness of each vibration peak of each cluster.

[0131] Preferably, as an embodiment of the present application, the sum of the corresponding vehicle speed factor and the vehicle speed fluctuation coefficient is recorded as the first sum; the product of the first sum and the instantaneous vibration difference is used as the road roughness of each vibration peak of each cluster.

[0132] Preferably, as another embodiment of the present application, the sum of the corresponding vehicle speed factor and the vehicle speed fluctuation coefficient is recorded as the first sum; the result of an exponential function with a natural constant as the base and the first sum as the exponent is recorded as the first index; the product of the first index and the instantaneous vibration difference is used as the degree of road roughness of each vibration peak of each cluster.

[0133] It should be noted that when the vibration data fluctuates greatly, if the speed at the corresponding moment is low and there is a large fluctuation, the speed fluctuation coefficient is larger; if the peak moment of the vibration corresponds to the valley moment of the speed, the corresponding speed factor is also larger; at the same time, if the vibration data has obvious instantaneous differences, the instantaneous vibration difference is also larger. Therefore, the greater the ruggedness of the road surface, the greater the possibility that the vehicle is driving on a rugged road surface; the smaller the ruggedness of the road surface, the greater the average driving speed, the smaller the speed change, the smaller the instantaneous difference of the vibration data, and the greater the possibility of driving on a good road surface.

[0134] Furthermore, the step flow chart of the method for obtaining the road surface roughness of each vibration wave peak of each cluster provided by the present application is as follows: Figure 3 shown.

[0135] The ratio of the volatility coefficient to the road roughness is used as the vehicle speed variation coefficient of each vibration peak of each cluster;

[0136] It should be noted that, when the axle is in a healthy state, if the vehicle is driving on a flat road, the volatility coefficient is small, the vehicle speed is stable, the vibration peak does not correspond to the speed valley, and the instantaneous difference is small, the road surface roughness is also small, and the vehicle speed variation coefficient is small; if the vehicle is driving on a rugged road, that is, the elements in the peak speed sequence fluctuate greatly, but there is only one peak in the peak speed sequence, and the peak trend is not increasing, but increasing instantaneously, the overall volatility of the peak speed sequence is still relatively gentle, and the increase in the volatility coefficient is not as fast as the road surface roughness, so the vehicle speed variation coefficient is still small; if the axle state is abnormal, when driving on a flat road, the fluctuation degree of the elements in the peak speed sequence is large, the volatility coefficient is large, the volatility coefficient is small, and the vehicle speed variation coefficient is large; when driving on a rugged road, the fluctuation degree of the vibration data is greater, and the volatility coefficient is greater. At the same time, because the vibration peak does not correspond to the speed valley when an abnormality occurs, and the instantaneous difference is small, the increase degree of the volatility coefficient is faster than the road surface roughness, and the road surface roughness is still large. Secondly, the larger the speed variation coefficient, the greater the possibility that the axle health status is abnormal.

[0137] At this point, the vehicle speed variation coefficient of each vibration peak of each cluster is obtained.

[0138] Step 4: Determine the tire pressure variation coefficient of each vibration peak of each cluster according to the time difference between the tire pressure peak and the vibration peak at all times of each cluster, and the correlation between the tire pressure and vibration data.

[0139] Furthermore, through the analysis of tire pressure and vibration data, it is found that the tire pressure has remained stable, and the vibration data of the axle in good condition has also remained stable. There is a certain positive correlation trend between the tire pressure and the vibration data. If the axle is in good health, even when driving on a rough road, when the axle passes through a pothole and the vibration increases instantaneously, the corresponding tire pressure will also increase synchronously and instantaneously, and the corresponding trend is still relatively consistent, and the peak corresponding time is also consistent. According to the characteristic relationship between the tire pressure and vibration data of the vehicle, the health status of the axle can be analyzed.

[0140] Based on the above analysis, due to the correspondence between the tire pressure of the axle and the vibration peak, the influence of the tire pressure change on the axle vibration can be reflected by analyzing the time interval difference between the tire pressure peak and the vibration peak. Specifically:

[0141] Obtaining corresponding moments of all peaks of the peak tire pressure sequence of each vibration peak, and recording them as tire pressure peak moments of each tire pressure peak in the peak tire pressure sequence of each vibration peak;

[0142] The difference between the tire pressure peak time of each tire pressure peak and the vibration peak time of the corresponding vibration peak is recorded as the peak time difference of each tire pressure peak in the peak tire pressure sequence of each vibration peak;

[0143] Preferably, as an embodiment of the present application, the absolute value of the difference between the tire pressure peak moment of each tire pressure peak and the vibration peak moment of the corresponding vibration peak is used as the peak time difference of each tire pressure peak in the peak tire pressure sequence of each vibration peak.

[0144] The minimum value of the peak time difference of all tire pressure peaks is recorded as the minimum peak time difference of each vibration peak;

[0145] If the minimum peak time difference is less than or equal to the preset time length, the tire pressure factor corresponding to each vibration peak is assigned the preset time length; if the minimum peak time difference is greater than the preset time length, the tire pressure factor corresponding to each vibration peak is assigned 0;

[0146] Preferably, as an embodiment of the present application, the preset time length is 3, and the AMPD algorithm is used to obtain the peak. As other implementation methods, the implementer can set the value of the time length according to the actual situation.

[0147] Furthermore, based on the corresponding trend between the tire pressure of the axle and the vibration of the axle, the tire pressure is analyzed through correlation to reflect the vibration state of the axle. Specifically:

[0148] According to the correlation between the vibration data at all times and the tire pressure, a first correlation coefficient is obtained;

[0149] According to the correlation between the peak vibration sequence of each vibration peak of each cluster and the peak tire pressure sequence, a second correlation coefficient of each vibration peak of each cluster is obtained;

[0150] Determining the correlation degree of each vibration peak of each cluster according to the difference between the first correlation coefficient and the second correlation coefficient;

[0151] Preferably, as an embodiment of the present application, the Pearson correlation coefficient method is used to calculate the first correlation coefficient and the second correlation coefficient; secondly, the correlation degree of each vibration peak of each cluster is the absolute value of the difference between the first correlation coefficient and the second correlation coefficient.

[0152] The tire pressure variation coefficient of each vibration peak of each cluster is positively correlated with the volatility coefficient and the correlation degree, and negatively correlated with the corresponding tire pressure factor;

[0153] It can be understood that a positive relationship indicates that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. The specific relationship in the process can be a multiplication relationship, an addition relationship, or the power of an exponential function, which is determined by actual application; a negative relationship indicates that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. The specific relationship can be a subtraction relationship, a division relationship, or a negative exponential power, which is determined by actual application, and this application does not impose any special restrictions on this.

[0154] Preferably, as an embodiment of the present application, the product of the volatility coefficient and the correlation degree is recorded as the first product, and the ratio of the first product to the corresponding tire pressure factor is used as the tire pressure variation coefficient of each vibration peak of each cluster. When calculating the ratio, since the tire pressure factor corresponding to the denominator may be 0, a parameter adjustment factor τ is set, and the value of τ is 0.01.

[0155] Preferably, as another embodiment of the present application, the product of the volatility coefficient and the correlation degree is recorded as the first product, and the result of the exponential function with the natural constant as the base and the corresponding tire pressure factor as the exponent is recorded as the tire pressure influence factor; the ratio of the first product to the tire pressure influence factor is used as the tire pressure variation coefficient of each vibration peak of each cluster.

[0156] It should be noted that if the axle is in good health, even when driving on a rough road, when the axle vibrates instantaneously when passing through a pothole, the corresponding tire pressure will also increase synchronously and instantaneously, and the corresponding trends are still relatively consistent, and the peak values ​​correspond to the same time, that is, the greater the volatility coefficient, the smaller the correlation, the larger the corresponding tire pressure factor, and the smaller the tire pressure variation coefficient; if the axle state is abnormal, the fluctuation degree is always large, the greater the volatility coefficient and the correlation degree, the corresponding tire pressure factor is 0, and the tire pressure variation coefficient is larger. Therefore, the smaller the tire pressure variation coefficient, the greater the possibility that the axle is in good health.

[0157] Further, the step flow chart of the method for obtaining the tire pressure variation coefficient of each vibration peak of each cluster provided by the present application is as follows: Figure 4 shown.

[0158] At this point, the tire pressure variation coefficient of each vibration peak of each cluster is obtained.

[0159] Step 5: Based on the vehicle speed variation coefficient and the tire pressure variation coefficient, an abnormal vibration index of each vibration peak is obtained to monitor and manage the health status of the vehicle axle.

[0160] Based on the above analysis, the vehicle vibration is comprehensively analyzed through the vehicle speed variation coefficient and the tire pressure variation coefficient to reflect the health of the axle state. The specific method is as follows:

[0161] Based on the vehicle speed variation coefficient and the tire pressure variation coefficient, the vibration abnormality of each vibration peak of each cluster is determined, wherein the vibration abnormality is positively correlated with the vehicle speed variation coefficient and the tire pressure variation coefficient, respectively.

[0162] It should be noted that the smaller the vibration abnormality, the greater the possibility that the axle health state is good; the larger the vibration abnormality, the greater the possibility that the axle health state is abnormal.

[0163] Preferably, as an embodiment of the present application, the product of the vehicle speed variation coefficient and the tire pressure variation coefficient is used as the vibration abnormality of each vibration peak of each cluster.

[0164] Preferably, as an embodiment of the present application, the sum of the vehicle speed variation coefficient and the tire pressure variation coefficient is used as the vibration abnormality of each vibration peak of each cluster.

[0165] For the vibration anomaly of all vibration peaks of all clusters, an abnormal detection algorithm is used to obtain the abnormal vibration index of each vibration peak;

[0166] The abnormal vibration index of all vibration peaks is normalized. If the normalized abnormal vibration index is greater than the preset abnormal threshold, it is determined that the health status of the axle is abnormal, and the user needs to be reminded to repair the axle in time to realize the monitoring and management of the health status of the axle.

[0167] Preferably, as an embodiment of the present application, the LOF abnormality detection algorithm is used to obtain the abnormal vibration index of each vibration peak; secondly, standardization is used for normalization processing; and the preset abnormal threshold value is 0.5.

[0168] It can be understood that the LOF anomaly detection algorithm is used for anomaly detection in this embodiment. As other implementation methods, implementers can use other methods in the prior art, such as isolation forest, etc. This embodiment does not impose any special restrictions on this.

[0169] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0170] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A coupled vibration data analysis method for a vehicle bridge, characterized in that: The method comprises the following steps: Use various sensors to obtain the vibration data of the axle, vehicle load, vehicle speed and tire pressure at all times; Cluster the vehicle loads, and obtain the vibration peaks and vibration troughs of each cluster according to the peaks of the vibration data at each moment; According to the difference degree of the peak values ​​of the vibration peaks in each cluster and the discrete degree of the local vibration data, the volatility coefficient of each vibration peak of each cluster is determined; According to the time difference between the trough and the vibration peak of each cluster at all times, and the discreteness of the vehicle speed, the vehicle speed variation coefficient of each vibration peak of each cluster is determined; Determine the tire pressure variation coefficient of each vibration peak of each cluster according to the time difference between the peak of the tire pressure and the vibration peak at all times of each cluster, and the correlation between the tire pressure and vibration data; Based on the vehicle speed variation coefficient and the tire pressure variation coefficient, an abnormal vibration index of each vibration peak is obtained to monitor and manage the health status of the vehicle axle; The determining of the vehicle speed variation coefficient of each vibration wave peak of each cluster includes: According to the vibration data at each moment in the peak vibration sequence, the vehicle speed and tire pressure at the corresponding moment are extracted respectively to form the peak vehicle speed sequence and peak tire pressure sequence of each vibration peak in each cluster; Determine the corresponding vehicle speed factor of each vibration wave peak of each cluster according to the time interval difference between each vibration wave peak and the trough of its peak vehicle speed sequence; Determining the vehicle speed fluctuation coefficient of each vibration wave peak of each cluster according to the discrete distribution of each element in the peak vehicle speed sequence; Determine the instantaneous vibration difference of each vibration peak of each cluster according to the difference between the vibration data corresponding to each vibration peak of each cluster and the vibration data of the previous moment; The corresponding vehicle speed factor, the instantaneous vibration difference and the vehicle speed fluctuation coefficient are integrated to determine the road roughness of each vibration peak of each cluster; The ratio of the volatility coefficient to the road roughness is used as the vehicle speed variation coefficient of each vibration peak of each cluster; The determining of the corresponding vehicle speed factor of each vibration peak of each cluster includes: The corresponding time of each vibration peak in each cluster is recorded as the vibration peak time of each vibration peak; Obtaining corresponding moments of all troughs of the peak vehicle speed sequence of each vibration wave peak, and recording them as vehicle speed trough moments of each vehicle speed trough in the peak vehicle speed sequence of each vibration wave peak; The difference between the vehicle speed trough moment of each vehicle speed trough and the vibration peak moment of the corresponding vibration peak is recorded as the interval time of each vehicle speed trough in the peak vehicle speed sequence of each vibration peak; The minimum interval duration of the interval durations of all vehicle speed troughs is recorded as the minimum time difference of each vibration peak; If the minimum time difference is less than or equal to the preset time length, the corresponding vehicle speed factor of each vibration wave peak is assigned the preset time length; if the minimum time difference is greater than the preset time length, the corresponding vehicle speed factor of each vibration wave peak is assigned 0; The step of determining the tire pressure variation coefficient of each vibration peak of each cluster includes: Determine the tire pressure factor corresponding to each vibration peak in each cluster according to the time interval difference between each vibration peak and the peak tire pressure sequence; According to the correlation between the vibration data at all times and the tire pressure, a first correlation coefficient is obtained; According to the correlation between the peak vibration sequence of each vibration peak of each cluster and the peak tire pressure sequence, a second correlation coefficient of each vibration peak of each cluster is obtained; Determining the correlation degree of each vibration peak of each cluster according to the difference between the first correlation coefficient and the second correlation coefficient; The tire pressure variation coefficient of each vibration peak of each cluster is positively correlated with the volatility coefficient and the correlation degree, and negatively correlated with the corresponding tire pressure factor; The step of determining the tire pressure factor corresponding to each vibration peak in each cluster includes: Obtaining corresponding moments of all peaks of the peak tire pressure sequence, recorded as tire pressure peak moments of each tire pressure peak in the peak tire pressure sequence of each vibration peak; The difference between the tire pressure peak time of each tire pressure peak and the vibration peak time of the corresponding vibration peak is recorded as the peak time difference of each tire pressure peak in the peak tire pressure sequence of each vibration peak; The minimum value of the peak time difference of all tire pressure peaks is recorded as the minimum peak time difference of each vibration peak; If the minimum peak time difference is less than or equal to the preset time length, the corresponding tire pressure factor of each vibration peak is assigned the preset time length; if the minimum peak time difference is greater than the preset time length, the corresponding tire pressure factor of each vibration peak is assigned 0.

2. A coupled vibration data analysis method for a vehicle bridge as claimed in claim 1, characterized in that: The method for obtaining each vibration peak and vibration trough of each cluster is as follows: Cluster the vehicle loads at all times to obtain multiple clusters; According to the vehicle load at each moment in each cluster, the vibration data at the corresponding moment is extracted to obtain the vibration data at each moment in each cluster; The peaks and troughs of the vibration data at all times in each cluster are obtained to obtain the vibration peaks and vibration troughs of each cluster.

3. A coupled vibration data analysis method for a vehicle bridge as claimed in claim 1, characterized in that: Determining the volatility coefficient of each vibration peak of each cluster includes: The vibration data at all moments between two vibration troughs adjacent to each vibration peak in each cluster are used to form a peak vibration sequence of each vibration peak in each cluster; Determining the vibration discreteness of each vibration peak of each cluster according to the discreteness of each element in the peak vibration sequence; Taking the mean of all elements in the peak vibration sequence as the first mean of each vibration peak of each cluster; The vibration dispersion and the first mean are combined to determine the vibration fluctuation coefficient of each vibration peak of each cluster; Determining the peak difference coefficient of each vibration peak of each cluster according to the difference of the peak values ​​of each vibration peak in each cluster; The peak difference coefficient and the vibration fluctuation coefficient are fused to determine the fluctuation coefficient of each vibration peak of each cluster.

4. A coupled vibration data analysis method for a vehicle bridge as claimed in claim 3, characterized in that: The step of determining the peak difference coefficient of each vibration peak of each cluster comprises: Determine the peak difference of each vibration peak according to the difference between the peak value of each vibration peak in each cluster and the minimum vibration data in the corresponding cluster; Determine the peak difference of each vibration peak according to the peak difference between each vibration peak in each cluster and the other vibration peaks; The extreme peak difference and the inter-peak difference are fused to determine the peak difference coefficient of each vibration peak of each cluster.

5. The coupled vibration data analysis method for a vehicle bridge according to claim 1, characterized in that: The determining of the vehicle speed fluctuation coefficient of each vibration peak of each cluster includes: Calculate the coefficient of variation of the peak vehicle speed sequence of each vibration peak of each cluster; Calculating the range of the peak vehicle speed sequence of each vibration peak of each cluster; The vehicle speed fluctuation coefficient of each vibration wave peak of each cluster is positively correlated with the coefficient of variation and the range.

6. A coupled vibration data analysis method for a vehicle bridge as claimed in claim 1, characterized in that: Determining the abnormal vibration index of each vibration peak and monitoring and managing the health status of the axle include: The vibration abnormality of each vibration peak of each cluster is positively correlated with the vehicle speed variation coefficient and the tire pressure variation coefficient respectively; For the vibration anomaly of all vibration peaks of all clusters, an abnormal detection algorithm is used to obtain the abnormal vibration index of each vibration peak; The abnormal vibration indexes of all vibration peaks are normalized. If the normalized abnormal vibration index is greater than a preset abnormal threshold, it is determined that the health status of the axle is abnormal.

Images