Method, device and system for detecting production quality of automobile control inhaul cable

Abstract

The invention relates to the technical field of inhaul cable detection, in particular to an automobile control inhaul cable production quality detection method, device and system, and the method comprises the steps: calculating a quality instability coefficient through the mutual relation between different detection data under the same curvature, and carrying out the quantification of the stability of the internal resistance of an inhaul cable; calculating a mass out-of-control coefficient according to the change condition of detection data under different curvatures, quantifying the resistance change in the dynamic movement process of the inhaul cable, and performing quantitative analysis on the dynamic change characteristics of the resistance by combining the influence of the internal mass of the inhaul cable on the resistance; the quality out-of-control coefficient is used for correcting the LOF value of detection data when static detection is conducted on the automobile inhaul cable, the production quality of the automobile inhaul cable is detected based on the corrected LOF value, the accuracy of inhaul cable quality and performance detection is further improved, and the reliability of inhaul cable quality detection results is guaranteed.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of cable detection, in particular to a method, device and system for detecting the production quality of a vehicle operating cable. BACKGROUND

[0002] As a key mechanical transmission element connecting the cockpit control mechanism and the execution component, the vehicle operating cable is widely used in clutch, brake, throttle, gear shifting, engine cover opening and door locking systems, and its performance is directly related to the control accuracy, driving safety and user operation experience of the vehicle. With the development of the automobile industry towards high quality and high reliability, higher requirements are put forward for the dynamic response characteristics, operation consistency and durability stability of the cable. Although the traditional cable quality control system has covered the basic mechanical performance indicators, it has obvious limitations in the face of increasingly stringent driving control quality requirements.

[0003] In the cable production quality inspection process of the prior art, static or quasi-static test methods are mainly used, such as maximum bearing capacity, fatigue life, length tolerance and other intuitive measurable parameters, while the dynamic mechanical behavior of the cable under real use conditions is seriously ignored, such as the dynamic change of cable resistance. In the prior art, a static loading method with a single curvature radius is often used for resistance detection. In fact, resistance is not a fixed value, but a dynamic variable affected by internal factors such as wire rope twisting quality, lubrication uniformity, sheath matching degree, etc. The fluctuation will significantly affect the control fluency, cause the hysteresis of operation and accelerate fatigue failure. Due to the lack of dynamic, continuous and multi-parameter coupling detection capability of the resistance-curvature relationship, the current quality inspection method cannot accurately evaluate the real performance of the cable, and some cables with dynamic performance defects may cause inconsistent feel, jamming, early failure and other quality problems. SUMMARY

[0004] In view of the above, it is necessary to provide a method, device and system for detecting the production quality of a vehicle operating cable to solve the above problems.

[0005] According to an aspect of the application, a method for detecting the production quality of a vehicle operating cable is provided, the method comprising:

[0006] Detecting the vehicle cable under each bending curvature, obtaining the tension data, displacement data and vibration data of each sample;

[0007] For each curvature, analyzing the difference characteristics between the tension data of the two ends of the cable at the same collection time, and analyzing the change trend of the difference characteristics, determining the resistance measurement trend under each curvature, and combining the dispersion degree of the difference characteristics to obtain the quality instability coefficient of each curvature;

[0008] The quality instability coefficient of each curvature is compared with overall distribution characteristics of all vibration data corresponding to each curvature, variation of displacement data in each curvature is analyzed, and the quality instability coefficient is determined;

[0009] The LOF value of detection data in static detection of the automobile cable is corrected by using the quality instability coefficient, and the production quality of the automobile cable is detected based on the corrected LOF value.

[0010] Preferably, the resistance trend in each curvature is determined, specifically as follows:

[0011] The cable includes a cable output end and a cable input end at two ends;

[0012] The difference between the input end tension and the output end tension of each sample at the same collection time is taken as the cable resistance at the time;

[0013] A sequence composed of all cable resistances obtained by each sample in each curvature is taken as a cable resistance sequence, a trend sequence of the cable resistance sequence is obtained, and fitting is performed to obtain a fitting curve function;

[0014] The change value between function values corresponding to adjacent independent variables in the fitting curve function is obtained, and the average value of all change values is taken as the resistance trend degree of each curvature.

[0015] Preferably, the quality instability coefficient of each curvature is obtained, including:

[0016] For each curvature, the coefficient of variation of the cable resistance sequence of each sample is obtained, the average of the coefficients of variation obtained by all samples is calculated, and the average cable resistance of all samples, the resistance trend degree are positively fused to obtain the quality instability coefficient of each curvature.

[0017] Preferably, the quality instability coefficient is determined, specifically as follows:

[0018] A sequence composed of the quality instability coefficients of all curvatures is taken as an instability coefficient sequence, and the average of the vibration data of each sample in each curvature is obtained, and the average vibration data of all samples is taken to form a total vibration sequence;

[0019] Based on the tension data of the cable output end, a first difference sequence of a sequence composed of all displacement data in each curvature is fitted, a curve before the position of the maximum tension data is taken as an output advancing curve function, and the remaining curves are taken as return curve functions; the distribution of integral areas corresponding to the output advancing curve function and the return curve function is analyzed to determine a response speed coefficient and a curve consistency coefficient;

[0020] Based on the distribution similarity between the total vibration sequence of each curvature and the instability coefficient sequence, a curvature influence coefficient is determined.

[0021]

[0022] In the formula, It is the quality runaway coefficient. It is the length of the first-order difference sequence of the instability coefficient sequence. It is the first difference sequence of the instability coefficient sequence. The normalized result of each element, It is the general category of curvature. , They are the first The response speed coefficient and curve consistency coefficient are given. It is the curvature influence coefficient; It is the normalized result of the element mean of the total vibration sequence.

[0023] Preferably, the determination of the response speed coefficient is specifically the normalized value of the average length of the tension data at the input end of all sample cables under each curvature; the curve consistency coefficient is specifically the normalized result of the absolute value of the difference between the integral areas of the output propulsion curve function and the return curve function obtained for each curvature.

[0024] Preferably, the determination of the curvature influence coefficient is specifically the Pearson correlation coefficient between the total vibration sequence and the runaway coefficient sequence.

[0025] Preferably, the formula for correcting the LOF value of the test data during static testing of the vehicle cable is as follows:

[0026]

[0027] In the formula, It is the first The LOF value after weighting by the bar chart. In the LOF anomaly detection algorithm k-distance neighborhood, , These are the preset quality runaway coefficient threshold and the normalized first... The mass runaway coefficient of the cable, , These are the LOF anomaly detection algorithms. , Locally achievable density; , This indicates the cable number.

[0028] Preferably, the production quality of the automobile cable is detected, specifically: the LOF values of all cables are threshold segmented to obtain a segmentation threshold; the cable with the LOF value greater than the segmentation threshold is an abnormal cable, and the production quality is unqualified; otherwise, the cable is a normal cable, and the production quality is qualified.

[0029] According to another aspect of the present application, there is provided a production quality detection device for an automobile control cable, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0030] According to still another aspect of the present application, there is provided a production quality detection system for an automobile control cable, wherein the system stores a computer program, and the computer program is executed by a processor to implement any one of the above methods.

[0031] The present application has at least the following beneficial effects:

[0032] The application detects the automobile cable under various bending curvatures, obtains tension data, displacement data and vibration data, and through the detection of the tension, displacement and vibration data of the cable under different curvatures, the performance of the cable in actual use can be comprehensively understood, which helps to identify potential fault points in advance. Multi-dimensional acquisition of data can provide more information for subsequent analysis; the difference characteristics between the tension data of the two ends of the cable at the same collection time are analyzed, and the change trend of the difference characteristics is analyzed to determine the resistance measurement trend. Through the analysis of the tension difference between the two ends of the cable, the load imbalance of the cable under different curvatures can be effectively identified, and the determination of the resistance measurement trend helps to understand the flexibility and anti-deformation ability of the cable, which are important indicators for measuring the performance of the cable; combined with the dispersion degree of the difference characteristics, the quality instability coefficient of each curvature is obtained. By quantifying the dispersion degree of the tension difference between the two ends of the cable, the stability of the cable under different curvatures can be reflected, and the calculation of the quality instability coefficient can help to predict the performance of the cable when it is disturbed by the outside world, so as to identify the unevenness or structural defects that may occur in the production or use of the cable. It is particularly important for the consistency and stability evaluation of the cable in the manufacturing process; by comparing the quality instability coefficient with the overall distribution of the vibration data, the dynamic response of the cable can be further analyzed, which helps to comprehensively evaluate the dynamic performance and structural stability of the cable and improve the accuracy of the detection; the change of the displacement data in each curvature is analyzed to determine the quality loss coefficient; the displacement data can reflect the deformation of the cable under different loads, and combined with the analysis of the quality loss coefficient, it can help to judge whether the cable has deformation or instability problems beyond the normal range, improve the monitoring accuracy of the cable loss behavior, and timely find the possible abnormal deformation of the cable; the LOF value of the detection data of the automobile cable in the static detection is corrected by using the quality loss coefficient, and by correcting the LOF (local outlier factor) value of the static detection data, the misjudgment caused by data deviation or abnormal value can be reduced, the accuracy of the detection result is improved, and thus the potential quality problems are helped to be identified; based on the corrected LOF value, the production quality of the automobile cable is detected, and the corrected LOF value can more accurately reflect the production quality of the cable. Through the detection of the production quality, defects and unevenness in the production process can be found in time to ensure that the production quality of the cable meets the standard. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 A step flow chart of a production quality detection method of an automobile control cable provided by the application is provided.

[0034] Figure 2 A flow chart for obtaining the quality loss coefficient provided by the application. DETAILED DESCRIPTION

[0035] In the description of the present embodiments, the words "exemplary," "or," "for example," and the like are used solely to indicate example, instance, or illustration, and not to imply or warrant that any of the embodiments or designs are preferred, advantageous or superior to other embodiments or designs. In fact, the words "exemplary," "or," "for example," and the like are used merely for the sake of convenience and to simplify the present disclosure, and do not in any way necessarily imply that any particular embodiment or design is superior.

[0036] Unless otherwise defined, 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. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0037] In addition, it should be pointed out that the terms "first", "second" in the present application and the drawings are used to distinguish similar objects, and are not used to describe a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flowchart includes one or more steps for implementing the method, and the execution order of the steps can be interchanged with each other without departing from the scope of the present application, and some steps can also be deleted.

[0038] Please refer to Figure 1 , which shows a step flowchart of a method for detecting the production quality of an automobile control cable provided by an embodiment of the present application. The method comprises the following steps:

[0039] Step one: detecting the automobile cable under each bending curvature to obtain the tension data, displacement data and vibration data of each sample.

[0040] The production quality of the automobile control cable is detected by a universal material testing machine. The UTM load sensor of the input end (the moving end of the universal testing machine) and the output end (the fixed base) of the universal material testing machine is used to collect the tension data of the input end and the output end. The displacement data of the moving end is collected by a laser displacement sensor. A point is randomly selected from the points where the cable contacts the guide set of wheels, and the vibration data of the cable during the movement is collected by a vibration sensor on the cable at the corresponding position of the point. The collection frequency of various data is set to 1KHz, and one sample corresponds to one complete reciprocating movement process. By installing replaceable guide set of wheels in the cable path, the bending curvature of the cable is controlled, and the automobile cable is detected under different bending curvatures. Four different bending curvatures are used in the present embodiment, and the radii of the guide wheels corresponding to each bending curvature are 35mm, 50mm, 75mm and 100mm respectively. Each bending curvature is detected times, and in the present embodiment Take 10. The collected data is normalized, in this embodiment, the maximum and minimum value normalization method is used for normalization of various data; the normalized data of various types is arranged in sequence according to the collection sequence to form the corresponding data sequence, and each sample can obtain the input end tension sequence, the output end tension sequence, the displacement sequence and the vibration sequence.

[0041] Step two: for each curvature, analyze the difference characteristics between the tension data of the two ends of the cable at the same collection time, and analyze the change trend of the difference characteristics, determine the resistance trend under each curvature, and combine the dispersion degree of the difference characteristics to obtain the quality instability coefficient of each curvature.

[0042] The resistance of the automobile cable refers to the torque generated on the rotating shaft due to internal friction and external resistance (such as bending, vibration, etc.) during the movement or force of the cable. This resistance not only affects the operation efficiency and sensitivity of the cable, but also may have a negative impact on the durability of the cable, thereby accelerating the degradation of the performance of the cable. In this process, the resistance of the cable is affected by many factors, including the mass of the internal steel wire rope of the cable and the material and structure of the inner liner tube. Generally, the inner core of the cable is twisted by multiple steel wires. If the steel wire diameter is small, the number of strands is more, and the size error of each strand is small, the overall structure of the cable will become more flexible. This is because the structure of fine steel wire and higher strand count increases the number of contact points and the contact distribution is more uniform, resulting in smaller friction force and lower resistance when bending.

[0043] In addition, during the twisting process of multiple steel wires, the uniformity of the stress of each strand of steel wire and the difference in twisting pitch also cause fluctuations in the resistance of the cable. When the stress of each strand of steel wire is uneven or the twisting pitch is inconsistent, the cable will loosen when bending, causing strand slip. Such slip makes the resistance fluctuation of the cable unpredictable. In addition, due to the effect of running-in, the resistance of the cable is usually high and fluctuates greatly in the initial stage, but as the number of uses and tests increases, the different strands of steel wire gradually adapt and run-in, and the resistance gradually decreases and tends to be stable.

[0044] Finally, the automobile cable usually adds lubricant in the inner liner tube to reduce friction. However, if the lubricant is not evenly coated or the amount is insufficient, it may cause resistance differences at different positions of the cable. Since the lubricant has a certain flowability, uneven coating may sometimes be self-repaired through mechanical movement of the cable, so in some cases, the effect of the lubricant is small. But if the total amount of lubricant is insufficient, the resistance of the cable may rebound after a slight decline, causing more obvious resistance fluctuations, and may cause the resistance to rise again, affecting the performance stability of the cable.

[0045] The difference between the input and output tensions obtained at the same acquisition moment is taken as the cable resistance at that moment. Therefore, based on the input and output tension sequences for each sample, the cable resistance sequence for each sample can be obtained. The cable resistance sequences of all samples under the same curvature, arranged in the sample acquisition order, constitute the resistance fluctuation sequence under that condition. Using a resistance fluctuation sequence as input, STL time series decomposition is applied to obtain the trend sequence. STL time series decomposition is a well-known technique and will not be elaborated further. Finally, using the index of the element in the trend sequence as the independent variable and the element value as the dependent variable, a polynomial fitting is performed, and the corresponding fitted curve function is output. Polynomial fitting is a well-known technique and will not be elaborated further.

[0046] Based on the above analysis, a mass instability coefficient is calculated to measure the stability characteristics of the vehicle control cable mass. Specifically, this involves obtaining the change values ​​between corresponding function values ​​of adjacent independent variables in the fitted curve function, and using the average of all change values ​​as the resistance trend measure for each curvature. In this embodiment, the specific formula for the resistance trend measure is: In the formula, It is the first The resistance trend measurement below; It is the first The length of the downtrend sequence The independent variable on the trend sequence fitting curve function is... The change in the function value corresponding to the previous independent variable is calculated using the absolute value of the difference.

[0047] Furthermore, for each curvature, the mean of the coefficient of variation of the cable resistance sequence of all test samples is obtained, and positively fused with the mean resistance of all test samples and the resistance trend measure to obtain the mass instability coefficient for each curvature. In this embodiment, the positive fusion of multiple variables is performed using a multiplication calculation method.

[0048] Understandably, for cables of poor quality, the main reason is the poor quality of the internal steel wire rope. The resistance of these cables fluctuates significantly due to the quality of the steel wire rope. Furthermore, due to the break-in effect of the cable and the lubricating effect of the lubricant, cables of poor quality initially exhibit higher resistance and stronger fluctuations. As testing progresses, the resistance gradually decreases and the fluctuations also lessen, but it is still greater than the resistance of a normally high-quality cable. When the lubricant quality is also poor, the resistance may rebound after a reduction, leading to a further increase in resistance and stronger fluctuations. Therefore, the worse the quality of the cable, the stronger the resistance fluctuations during use, resulting in poorer cable stability and a higher quality instability coefficient.

[0049] Step three: compare the quality instability coefficient obtained based on each curvature with the overall distribution characteristics of all vibration data corresponding to each curvature, analyze the change of displacement data in each curvature, and determine the quality instability coefficient.

[0050] The quality instability coefficient measures the stability of the steering cable quality under the same curvature, but since the state of the automobile cable in actual use is dynamically changing, the fluctuation of the cable resistance under a single working condition is not enough to ensure the accuracy of the detection. For example, there may be some emergency braking situations during the use of the automobile. If the uncontrollability of the cable resistance is stronger, it is more likely to cause the steering of the automobile to lose control in extreme situations and produce corresponding risks. Therefore, it is necessary to further detect the quality of the cable according to the fluctuation of the automobile cable resistance under different curvatures.

[0051] Under normal circumstances, high-quality cables can adapt to changes in bending curvature more smoothly, making the resistance change more stable. As the bending curvature of the cable increases, the resistance fluctuation is smaller, and the response speed is faster. It should be noted that the response of the cable refers to the time required for the cable to move to the target position and return to the initial state. When the cable quality is poor, as the bending curvature increases, the mutual interference of the internal steel wires will limit the free sliding between the steel wires, causing the resistance of the cable to gradually increase, and the response time will also become longer.

[0052] In addition, under normal circumstances, the resistance change inside the high-quality cable is relatively uniform and stable, and even when the bending curvature increases, the dynamic motion of the cable will maintain a small and consistent vibration. Due to the unevenness of the internal resistance distribution of the poor-quality cable, the vibration fluctuation during dynamic motion may increase.

[0053] For high-quality cables, the internal structure is relatively fixed and stable, which makes the force-displacement curve during cable motion smooth and repeatable, that is, the consistency of the forward curve and the return curve during reciprocating motion is relatively high.

[0054] The quality instability coefficients under each curvature are arranged in order of the size of the curvature to form an instability coefficient sequence, and the mean values of the vibration sequence elements of all samples under each curvature are arranged in order of the size of the curvature to form a total vibration sequence.

[0055] Then, the first-order difference sequence of the displacement sequence of each sample under a curvature condition is calculated, and the elements of the tension sequence of each sample except the last element are taken as the independent variable, and the elements of the first-order difference sequence of the displacement sequence of the sample are taken as the dependent variable. The polynomial fitting is performed for the advancing process and the returning process of the reciprocating motion respectively, and the advancing curve function and the returning curve function are output respectively. Specifically, in a tension sequence, the maximum value of the tension is selected, and the points formed by the elements before the maximum value and the elements in the displacement sequence with the same index together are the advancing points, and the points formed by the elements after the maximum value and the elements in the displacement sequence with the same index together are the returning points. All the advancing points and the returning points are fitted respectively, and the advancing curve and the returning curve are output. The polynomial fitting is a known technology, and details are not described again.

[0056] Based on the above analysis, the mass out-of-control coefficient is calculated to measure the uncontrollability of the mass of the automobile control cable. The specific formula is:

[0057]

[0058] In the formula, is the mass out-of-control coefficient, is the length of the first-order difference sequence of the instability coefficient sequence, is the normalized result of the first element of the first-order difference sequence of the instability coefficient sequence, is the total category of the curvature, are the response speed coefficient and the curve consistency coefficient under the i-th curvature respectively, is the curvature influence coefficient, which is obtained by the Pearson correlation coefficient of the total vibration sequence and the instability coefficient sequence; is the normalized result of the mean value of the elements of the total vibration sequence. In the embodiment, the normalized result of the mean value of the elements of the total vibration sequence is 1. is the normalized value of the length mean value of the tension sequence of all samples under the i-th curvature; is the normalized result of the absolute value of the difference of the integral areas of the advancing curve and the returning curve under the i-th curvature, and the upper and lower limits of integration are 0 and the maximum displacement respectively. It should be noted that the normalization adopts the maximum value normalization, and the maximum value of the denominator is the maximum value under all curvatures, not the maximum value under a single curvature. It should be noted that when the mass of the cable is poor, the stability of the resistance of the cable during the movement is poor, and the resistance fluctuation of the cable between different curvatures is larger, that is, The flowchart for obtaining the mass out-of-control coefficient is shown in FIG. 6. It can be understood that when the mass of the cable is poor, the stability of the resistance of the cable during the movement is poor, and the resistance fluctuation of the cable between different curvatures is larger, that is,

[0059] Figure 2

[0060] It can be understood that when the mass of the cable is poor, the stability of the resistance of the cable during the movement is poor, and the resistance fluctuation of the cable between different curvatures is larger, that is, ​​​​Firstly, the lower the quality of the cable, the more drastic the internal vibration changes when the resistance changes. The corresponding resistance changes between different curvatures fluctuate more, resulting in more intense vibrations. Furthermore, the correlation between vibration and changes in the cable's instability coefficient is stronger. At the same time, the lower the quality of the cable, the slower its response speed, meaning the stronger the hysteresis, and the longer it takes to complete the control of the cable. Due to the fluctuation of resistance, the consistency between the advance curve and the return curve is also worse, resulting in a larger quality runaway coefficient.

[0061] Step 4: Correct the LOF value of the detection data during static testing of the automotive cable using the aforementioned quality runaway coefficient, and then test the production quality of the automotive cable based on the corrected LOF value.

[0062] collection The data for the cable is obtained from standard test data used in static testing of automotive cables, such as maximum load value, axial fatigue, and stroke efficiency. In this embodiment... The value is set to 200, which can be set by the implementer according to the actual situation. The quality runaway coefficient of each cable is calculated. Then, the standard test data of all cables are used to form a test matrix. Each row of the matrix corresponds to the test data of one cable, and each column corresponds to a standard test data. LOF anomaly detection is performed, and the LOF value of each cable is output. LOF anomaly detection is a well-known technology and will not be described in detail.

[0063] In traditional LOF anomaly detection, all samples have equal weight. However, current technologies for automotive cable quality inspection mostly rely on static measurements, making it difficult to assess the dynamic quality of automotive cables. The calculated quality runaway coefficient, however, measures data fluctuations under dynamic conditions by adapting to changes in detection parameters. Therefore, the weights of different cables during anomaly detection can be adjusted using the quality runaway coefficient, resulting in a more sensitive LOF value for dynamic quality anomalies in cables. The specific formula is as follows:

[0064]

[0065] In the formula, It is the first The LOF value after weighting by the bar chart. In the LOF anomaly detection algorithm k-distance neighborhood, , The preset quality runaway coefficient threshold and the normalized first... The mass runaway coefficient of the cable, , These are the LOF anomaly detection algorithms. , Locally achievable density; 、 The number of the cable is represented.

[0066] It should be noted that the mass out-of-control coefficient threshold value can be set by itself according to the need, or the mass out-of-control coefficient of the cable is obtained to obtain the corresponding threshold value, including but not limited to the upper quartile, the Otsu threshold value.

[0067] In this embodiment, after the LOF values of all the cables are obtained, Otsu threshold segmentation is performed to obtain a segmentation threshold value. The Otsu threshold segmentation is a known technology, and will not be described in detail. The cable with the LOF value greater than the segmentation threshold value is regarded as an abnormal cable, and it is judged that the corresponding production quality is unqualified.

[0068] Based on the same concept as the method embodiment of the present application, a vehicle control cable production quality detection device is provided, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above embodiments are implemented.

[0069] Based on the same concept as the method embodiment of the present application, a vehicle control cable production quality detection system is provided, and the system stores a computer program. When the computer program is executed by the processor, the method described in any one of the above embodiments is implemented.

[0070] It should be noted that the flowchart and block diagram in the accompanying drawings show the possible implementation architecture, function and operation of the system, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the blocks can also occur in different order from that marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. In the description corresponding to the flowchart and block diagram in the drawings, the operations or steps corresponding to different blocks can also occur in different order from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0071] ​The above examples are only used to illustrate the technical solutions of the present application, but not limit the same; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent ones; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for detecting the production quality of a steering cable for an automobile, characterized by, The method comprises the following steps: Detecting the automobile cable under each bending curvature, and obtaining tension data, displacement data, and vibration data of each sample; For each curvature, analyzing the difference between the tension data at both ends of the cable at the same collection time, analyzing the change trend of the difference, determining the resistance trend of each curvature, and combining the dispersion degree of the difference to obtain the quality instability coefficient of each curvature; Comparing the quality instability coefficient obtained for each curvature with the overall distribution characteristics of all vibration data corresponding to each curvature, analyzing the change of the displacement data in each curvature, and determining the quality instability coefficient; Using the quality instability coefficient to correct the LOF value of the detection data during the static detection of the automobile cable, and detecting the production quality of the automobile cable based on the corrected LOF value.

2. The method for quality inspection of automotive control cables as described in claim 1, characterized in that, The determination of the resistance trend of each curvature is specifically: The two ends of the cable include a cable output end and a cable input end; The difference between the input end tension and the output end tension of each sample obtained at the same collection time is taken as the cable resistance at this time; A sequence composed of all cable resistances obtained by each sample for each curvature is taken as a cable resistance sequence, a trend sequence of the cable resistance sequence is obtained, and fitting is performed to obtain a fitting curve function; The average value of all change values between the function values corresponding to adjacent independent variables in the fitting curve function is taken as the resistance trend of each curvature.

3. The method of claim 2, wherein the method further comprises: determining the quality of the automobile control cable by measuring the length of the automobile control cable. The quality instability coefficient of each curvature is obtained by: For each curvature, the coefficient of variation of the cable resistance sequence of each sample is obtained, the average value of the coefficients of variation obtained for all samples is calculated, and the average value is fused with the average value of the cable resistance of all samples and the resistance trend to obtain the quality instability coefficient of each curvature.

4. The method of claim 1, wherein the method comprises: The determination of the quality instability coefficient is specifically: A sequence composed of the quality instability coefficients obtained for all curvatures is taken as an instability coefficient sequence; the average value of the vibration data of each sample for each curvature is obtained, and the average value of the vibration data obtained for all samples is taken as a total vibration sequence; Based on the tension data of the cable output end, a first-order difference sequence of a sequence composed of all displacement data under each curvature is fitted, the curve before the position of the maximum tension data is taken as an output advancing curve function, and the remaining curves are taken as return curve functions; the distribution of the integral areas corresponding to the output advancing curve function and the return curve functions is analyzed to determine a response speed coefficient and a curve consistency coefficient; Based on the distribution similarity between the total vibration sequence of each curvature and the instability coefficient sequence, a curvature influence coefficient is determined. In the formula, It is the quality runaway coefficient. It is the length of the first-order difference sequence of the instability coefficient sequence. It is the first difference sequence of the instability coefficient sequence. The normalized result of each element, It is the general category of curvature. , They are the first The response speed coefficient and curve consistency coefficient are given. It is the curvature influence coefficient; It is the normalized result of the element mean of the total vibration sequence.

5. The method of claim 4, wherein the quality of the automobile control cable is determined by measuring the length of the automobile control cable and the diameter of the automobile control cable. The response speed coefficient is specifically the normalized value of the length average value of the tension data of the cable input end of all samples under each curvature; and the curve consistency coefficient is specifically the normalized result of the absolute value of the difference between the integral areas of the output advancing curve function and the return curve function obtained for each curvature.

6. The method of claim 4, wherein the quality of the automobile control cable is detected by measuring the tension of the automobile control cable. The curvature influence coefficient is specifically the Pearson correlation coefficient between the total vibration sequence and the instability coefficient sequence.

7. The method of claim 1, wherein the method comprises: The formula for correcting the LOF value of the detection data during the static detection of the automobile cable is: ​ In the formula, is the k-distance neighborhood of the th cable, is the k-distance neighborhood of the th cable in the LOF anomaly detection algorithm, , are respectively a preset quality out-of-control coefficient threshold and a normalized quality out-of-control coefficient of the th cable, , are respectively a local reachable density of the , th cable in the LOF anomaly detection algorithm; , represents the number of the cable.

8. The method of claim 1, wherein the method comprises: The production quality of the automobile cable is detected, specifically: threshold segmentation is performed on LOF values of all cables to obtain a segmentation threshold; if the LOF value is greater than the segmentation threshold, the cable is an abnormal cable and the production quality is unqualified; otherwise, the cable is a normal cable and the production quality is qualified. ​ 9. An automobile steering cable production quality detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The computer program is executed by the processor to implement the steps of the method according to any one of claims 1-8.

10. A system for detecting the production quality of a steering cable for an automobile, wherein a computer program is stored in the system, characterized in that, The computer program is executed by the processor to implement the method according to any one of claims 1-8.

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