Bearing protrusion detection method, system and equipment based on image visual identification

Abstract

The invention relates to the field of image processing, in particular to a bearing protrusion detection method, system and device based on image visual identification, and the method comprises the steps of obtaining a target element model of a target device model for installing a ball screw supporting bearing; taking a target equipment model and a target element model as scene constraints, collecting a same-scene sample group meeting the scene constraints, and counting a pre-tightening force concentrated value and a first bearing protrusion interval; processing the pre-tightening force concentrated value through a predefined bearing protrusion configuration function to obtain a bearing protrusion confidence interval; performing intersection interval extraction according to the bearing protrusion confidence interval to obtain a second bearing protrusion interval; extracting a bearing protruding amount detection value of the bearing installation shooting image; and when the bearing protrusion amount detection value does not belong to the second bearing protrusion amount interval, carrying out bearing protrusion amount abnormity identification on the bearing according to the shot image, thereby solving the technical problems of inaccurate bearing protrusion amount detection data and insufficient scene adaptability.

Description

Technical Field

[0001] The present invention relates to the field of image processing, and particularly to a method, system and device for detecting the protrusion amount of a bearing by image vision recognition. Background Art

[0002] The ball screw support bearing is a key transmission component widely used in machine tools and precision machinery. Its main function is to efficiently convert rotational motion into linear motion, or convert torque into axial repetitive force. The ball screw support bearing consists of a screw, a nut, a reverse device and balls. Through the rolling of the balls between the screw and the nut, a low-friction and high-precision transmission effect is achieved. Due to its high precision, reversibility and high efficiency, the ball screw support bearing is widely used in various industrial equipment and precision instruments, such as numerical control machine tools, precision measuring equipment, robots, etc. In these applications, the ball screw support bearing not only undertakes the transmission task, but also directly affects the positioning accuracy, transmission speed and stability of the equipment.

[0003] During the installation and use of the ball screw support bearing, the protrusion amount of the bearing, that is, the relative height difference between the end face of the inner ring and the end face of the outer ring of the bearing, is an important parameter. The accurate control of the protrusion amount of the bearing can ensure the adaptability and normal operation of the bearing, thus guaranteeing the accuracy and stability of the entire transmission system. However, currently, the ball screw support bearing is installed according to the recommended protrusion amount of the bearing. This standardized installation method lacks a quantitative standard closely combined with the application scenario, resulting in frequent mismatch of the protrusion amount of the bearing in actual applications, affecting the performance and service life of the transmission system. In order to accurately detect the protrusion amount of the bearing, traditional methods mainly use contact detection equipment. However, there are some problems with this detection method. On the one hand, the contact detection equipment is prone to wear during use, especially when a large number of detections are carried out, and the wear situation is particularly serious. This will lead to inaccurate detection data, thus affecting the accurate evaluation of the protrusion amount of the bearing. On the other hand, most of the existing contact detection equipment relies on imports, with high costs and complex maintenance, and it is difficult to meet the needs of enterprises for a large number of detections. Therefore, developing a high-precision and non-contact method for detecting the protrusion amount of the bearing is of great significance for improving the adaptability and normal operation ability of the ball screw support bearing, and guaranteeing the accuracy and stability of the transmission system. Summary of the Invention

[0004] The present invention aims at the technical problems of inaccurate detection data of the protrusion amount of the bearing and insufficient adaptability to the scenario in the prior art, and provides a method, system and device for detecting the protrusion amount of the bearing by image vision recognition to solve the problems.

[0005] The technical solutions of the present invention for solving the above technical problems are as follows: In a first aspect, the present invention provides a method for detecting the protrusion amount of a bearing by image vision recognition, including: obtaining the target component model of the target equipment model on which the ball screw support bearing is installed; taking the target equipment model and the target component model as scene constraints, collecting a group of same-scene samples that meet the scene constraints, and statistically analyzing the concentrated preload value and the first bearing protrusion amount interval; processing the concentrated preload value through a predefined bearing protrusion amount configuration function to obtain a bearing protrusion amount confidence interval; extracting an intersection interval according to the bearing protrusion amount confidence interval to obtain a second bearing protrusion amount interval; extracting the bearing protrusion amount detection value of the bearing installation captured image; when the bearing protrusion amount detection value does not belong to the second bearing protrusion amount interval, performing an abnormal identification of the bearing protrusion amount on the bearing according to the captured image.

[0006] In a second aspect, the present invention provides a system for detecting the protrusion amount of a bearing by image vision recognition, the system including: a model recognition module, configured to obtain the target component model of the target equipment model on which the ball screw support bearing is installed; a collection and statistics module, configured to take the target equipment model and the target component model as scene constraints, collect a group of same-scene samples that meet the scene constraints, and statistically analyze the concentrated preload value and the first bearing protrusion amount interval; a numerical processing module, configured to process the concentrated preload value through a predefined bearing protrusion amount configuration function to obtain a bearing protrusion amount confidence interval; an interval extraction module, configured to extract an intersection interval according to the bearing protrusion amount confidence interval to obtain a second bearing protrusion amount interval; a numerical extraction module, configured to extract the bearing protrusion amount detection value of the bearing installation captured image; an abnormal identification module, configured to perform an abnormal identification of the bearing protrusion amount on the bearing according to the captured image when the bearing protrusion amount detection value does not belong to the second bearing protrusion amount interval.

[0007] In a third aspect, the present invention provides an electronic device, including: a memory, configured to store a computer software program; a processor, configured to read and execute the computer software program, thereby implementing a method for detecting the protrusion amount of a bearing by image vision recognition as described in the first aspect.

[0008] The beneficial effects of the present invention are as follows: By combining the target equipment model and the target component model to analyze the sample group under scene constraints, and using a predefined configuration function to process the preload data to obtain a confidence interval, and finally by comparing the bearing protrusion amount detection value obtained by image recognition with the confidence interval, the accurate recognition and identification of abnormal bearing protrusion amount are realized, achieving the technical effects of improving the detection accuracy and efficiency. Description of the Drawings

[0009] Figure 1 It is a schematic flow chart of a method for detecting the protrusion amount of a bearing by image vision recognition provided by the present invention.

[0010] Figure 2 Schematic structural diagram of a bearing protrusion amount detection system for image vision recognition provided by the present invention.

[0011] Figure 3 Schematic structural diagram of an electronic device provided by the present invention; Figure 4 Diagram showing the training effects of replacing different loss functions provided by the present invention.

[0012] Explanation of reference numerals: model recognition module 11, acquisition and statistics module 12, numerical processing module 13, interval extraction module 14, numerical extraction module 15, anomaly identification module 16, electronic device 500, memory 510, processor 520, first computer program 511. Specific embodiments

[0013] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0014] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined.

[0015] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for purposes of explanation. It should be understood that those skilled in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present invention.

[0016] Embodiment 1: As Figure 1 shown, the embodiment of the present invention provides a method for detecting the bearing protrusion amount by image vision recognition, including: S10: Obtain the target component model of the target equipment model for installing the ball screw support bearing.

[0017] S20: Taking the target equipment model and the target component model as scenario constraints, collect a group of same-scenario samples that meet the scenario constraints, and statistically analyze the concentrated preload value and the first bearing protrusion amount interval.

[0018] S30: Process the concentrated preload value through a predefined bearing protrusion amount configuration function to obtain the bearing protrusion amount confidence interval.

[0019] S40: Extract the intersection interval according to the bearing protrusion amount confidence interval to obtain the second bearing protrusion amount interval.

[0020] S50: Extract the bearing protrusion amount detection value of the bearing installation captured image.

[0021] S60: When the bearing protrusion amount detection value does not belong to the second bearing protrusion amount interval, mark the bearing with abnormal bearing protrusion amount according to the captured image.

[0022] Exemplarily, the bearing protrusion amount refers to the relative height difference between the inner ring end face and the outer ring end face of the bearing. The distance that the inner ring end face protrudes from the outer ring end face at the same end face of the bearing after applying a preload to a single bearing can ensure the adaptability and normal operation of the bearing. When detecting the bearing protrusion amount based on the image vision recognition technology, first, clarify the target to be detected, that is, obtain the target equipment model for installing the ball screw support bearing and its corresponding target component model. The target equipment model refers to the specific model of the machine or device on which the ball screw support bearing is installed, which determines the working environment and force conditions of the bearing; while the target component model refers to the specific model of the ball screw support bearing that matches this equipment, which affects key factors such as the size, performance, and installation method of the bearing. By clarifying these two models, accurate constraint conditions can be provided for the subsequent analysis of the bearing protrusion amount based on the specific application scenario, ensuring the accuracy and practicality of the detection results.

[0023] Furthermore, when dealing with the detection task of the protrusion amount of the ball screw support bearing for the target device model and the target component model, this solution adopts a more refined and scientific method rather than directly detecting the protrusion amount of the bearing. The reason is that the protrusion amount of the bearing is not determined by a single factor, and one of its underlying factors is the preload. In the actual application scenario, the preload and the axial stiffness of the bearing act together on the protrusion amount of the bearing, but the relationship between them is not strictly linear and is affected by various complex factors. Therefore, if only the protrusion amount of the bearing is directly detected without considering the quantification of the preload, the detection result may be interfered by unclear factors, thus affecting the accuracy. To more accurately reflect the actual situation of the protrusion amount of the bearing, a large number of same-scenario sample groups are first collected according to the scenario constraints of the target device model and the target component model to ensure the pertinence and accuracy of subsequent analysis. Specifically, the same-scenario sample groups that meet these scenario constraints are collected, and these sample groups contain the historical data of the ball screw support bearings installed under similar or the same application conditions. By analyzing these samples, the concentrated value of the preload is statistically obtained, and this value reflects the magnitude of the preload that the ball screw support bearing usually needs to be applied to achieve the ideal working state under a specific scenario. At the same time, the first bearing protrusion amount interval is statistically obtained, that is, the distribution of the protrusion amount of the bearing in these samples. The determination of this interval helps to understand the reasonable variation range of the protrusion amount of the bearing under a given preload. Next, the concentrated value of the preload is processed using a predefined bearing protrusion amount configuration function to obtain the theoretical confidence interval of the protrusion amount of the bearing. The predefined bearing protrusion amount configuration function is a mathematical tool constructed based on a large amount of experimental data and theoretical models, and it can reflect the complex relationship between the preload and the protrusion amount of the bearing. Subsequently, the concentrated value of the preload obtained previously is used as an input parameter and fed into this configuration function. The function uses the built-in algorithms and logic to perform a series of calculations and processes on the concentrated value of the preload, and finally outputs a confidence interval of the protrusion amount of the bearing. This confidence interval represents the reasonable range within which the protrusion amount of the bearing may fall under the given preload condition, and it provides a scientific and reliable reference standard for judging whether the protrusion amount of the bearing is in a healthy state. This process is equivalent to starting from the underlying factor (i.e., the preload) and predicting the possible range of the protrusion amount of the bearing through a mathematical model. Then, the intersection operation is performed between the theoretical confidence interval and the statistically obtained first bearing protrusion amount interval. The result obtained takes into account both the distribution characteristics of the actual data and the accuracy of the theoretical prediction, so it is more representative. In summary, by starting the analysis from the underlying factor of the preload and combining statistical data and theoretical prediction, the healthy range of the protrusion amount of the bearing can be more accurately determined, thus ensuring the accuracy and reliability of the detection result.

[0024] Specifically, the confidence intervals are obtained by processing the preload concentration values through a predefined configuration function, and they each represent the reasonable boundaries within which the bearing protrusion amount may vary under different scenarios or conditions. Subsequently, an extraction operation of the intersection interval is performed on these confidence intervals, which involves finding the part commonly covered by all the given intervals, that is, the overlapping area between them. Through this process, a narrower and more accurate second bearing protrusion amount interval is obtained, which comprehensively represents the most reasonable range of the bearing protrusion amount under all considered factors. The determination of the second bearing protrusion amount interval provides a more stringent basis for judging the health status of the bearing protrusion amount, contributing to improving the stability and reliability of the equipment.

[0025] Finally, an image vision recognition technology is combined to determine the actual state of the bearing protrusion amount. Clear images of the bearing installation position are captured by a high-precision image acquisition device. Subsequently, an advanced image processing algorithm is used to extract the detection value of the bearing protrusion amount from the captured images, and this process realizes non-contact and high-precision measurement of the bearing protrusion amount. Immediately afterwards, this detection value is compared with the previously obtained second bearing protrusion amount interval. If the detection value falls outside this interval, it means that the bearing protrusion amount does not meet the preset health standard. At this time, the corresponding bearing is marked with an abnormal protrusion amount based on the captured images. This step not only records the abnormal state of the bearing but also provides valuable visual evidence for subsequent problem tracing and analysis through the retention of image information. Through this series of processes, abnormal situations of the bearing protrusion amount can be identified in a timely and accurate manner, providing strong support for the maintenance and management of the equipment.

[0026] In a preferred embodiment, with the target equipment model and the target component model as scene constraints, a same-scene sample group that meets the scene constraints is collected, and the preload concentration value and the first bearing protrusion amount interval are statistically analyzed, including: retrieving the first bearing historical installation data of healthy installation samples whose ball screw support bearing model meets the scene constraints, where the first bearing historical installation data includes a set of installation preload record values and a set of bearing protrusion amount detection record values; performing a central tendency analysis on the set of installation preload record values to obtain the preload concentration value; and performing an outlier deletion on the set of bearing protrusion amount detection record values to obtain the first bearing protrusion amount interval.

[0027] Optionally, using a specific target device model and target component model as scenario constraints ensures the pertinence and relevance of subsequent analyses. On this basis, a same-scenario sample group that meets these scenario constraints is collected. These sample groups contain historical data of ball screw support bearings installed under similar or identical application conditions. In particular, the first bearing historical installation data of healthy installation samples whose ball screw support bearing models meet the scenario constraints are retrieved. These data detail the detection of preload and bearing protrusion amount during the installation process. After obtaining this historical data, a central tendency analysis is performed on the set of recorded preload values. This step calculates statistical measures such as the mean, median, or mode to obtain the central value of the preload, which reflects the magnitude of the preload that a ball screw support bearing typically needs to be applied to achieve an ideal working state under a given scenario. At the same time, an outlier deletion operation is performed on the set of recorded bearing protrusion amount detection values to eliminate extreme values caused by measurement errors, abnormal operations, or other atypical factors, thereby obtaining a more realistic and reliable first bearing protrusion amount interval. This interval provides a reasonable range of variation for the bearing protrusion amount under normal installation conditions.

[0028] In a preferred embodiment, performing an outlier deletion on the set of recorded bearing protrusion amount detection values to obtain the first bearing protrusion amount interval includes: sorting the recorded bearing protrusion amount detection values in ascending order to obtain a sorted result of the recorded bearing protrusion amount detection values; extracting the first recorded bearing protrusion amount detection value at the ceiling of one-fourth of the sorted result of the recorded bearing protrusion amount detection values; extracting the second recorded bearing protrusion amount detection value of the bearing protrusion amount at the floor of three-fourths of the sorted result of the recorded bearing protrusion amount detection values; calculating the modulus of the bearing protrusion amount deviation between the first recorded bearing protrusion amount detection value and the second recorded bearing protrusion amount detection value; using the first recorded bearing protrusion amount detection value minus 1.5 times the modulus of the bearing protrusion amount deviation to obtain a lower limit value of the bearing protrusion amount, and using the second recorded bearing protrusion amount detection value plus 1.5 times the modulus of the bearing protrusion amount deviation to obtain an upper limit value of the bearing protrusion amount; deleting from the set of recorded bearing protrusion amount detection values the outlier recorded bearing protrusion amount detection values that are less than the lower limit value of the bearing protrusion amount or greater than the upper limit value of the bearing protrusion amount to obtain the first bearing protrusion amount interval of the remaining recorded bearing protrusion amount detection value distribution.

[0029] Specifically, when processing the protrusion amount data of the ball screw support bearing, in order to ensure the accuracy and practicality of the analysis results, a strict outlier deletion process is adopted to determine the first bearing protrusion amount interval. This process begins with sorting the detected record values of the bearing protrusion amount, that is, arranging all the record values in ascending order to obtain an ordered sorting result of the detected record values of the bearing protrusion amount. Subsequently, key data points are extracted from the sorting result. Specifically, the first detected record value of the bearing protrusion amount corresponding to the ceiling serial number at the one-quarter position after sorting is found, and the second detected record value of the bearing protrusion amount corresponding to the floor serial number at the three-quarter position after sorting is found. These two data points respectively represent the preliminary estimates of the lower and upper bounds of the bearing protrusion amount distribution. Next, the deviation modulus value between these two data points is calculated, that is, their absolute difference, which reflects the range of the bearing protrusion amount distribution. Based on this deviation modulus value, the upper and lower limit values of the bearing protrusion amount are further determined. Specifically, the lower limit value is obtained by subtracting 1.5 times the deviation modulus value from the first detected record value of the bearing protrusion amount, and the upper limit value is obtained by adding 1.5 times the deviation modulus value to the second detected record value of the bearing protrusion amount. These two limit values constitute the benchmark for judging whether the bearing protrusion amount is abnormal. Finally, all the detected record values of the bearing protrusion amount are traversed, and the record values that are less than the lower limit value or greater than the upper limit value are regarded as outliers and deleted. After this step, a distribution interval composed of the remaining detected record values of the bearing protrusion amount is obtained, that is, the first bearing protrusion amount interval. This interval provides an accurate description of the reasonable variation range of the bearing protrusion amount under normal circumstances. Taking the protrusion amount data of a certain type of ball screw support bearing as an example, assume there is a set of 100 detected record values. According to the above process, these values are first sorted, and then the 25th (ceiling at the one-quarter position) and 75th (floor at the three-quarter position) data points are extracted as the preliminary bounds. After calculating the deviation modulus value between these two points, the lower limit value and the upper limit value are obtained, and 5 outliers are deleted accordingly. Finally, a first bearing protrusion amount interval composed of 95 remaining record values is obtained, which provides strong data support for subsequent analysis and decision-making.

[0030] In a preferred embodiment, the pre-tightening force concentration value is processed through a predefined bearing protrusion amount configuration function to obtain a bearing protrusion amount confidence interval, including: obtaining second bearing historical installation data of the ball screw support bearing model, where the second bearing historical installation data includes pre-tightening force record data and bearing protrusion amount identification interval record data; constructing a bearing protrusion amount configuration loss function according to the physical law loss term and the data-driven loss term; based on the bearing protrusion amount configuration loss function, using the bearing protrusion amount identification interval record data as supervision and the pre-tightening force record data as input, training the bearing protrusion amount configuration function through a BP neural network.

[0031] Preferably, in order to accurately predict the bearing protrusion amount and determine its confidence interval, it is carried out by combining physical laws and data-driven methods. This process first relies on obtaining the second bearing historical installation data of a specific ball screw support bearing model, which contains rich preload records and corresponding bearing protrusion amount identification interval records, providing a basis for subsequent model training. Next, a bearing protrusion amount configuration loss function is constructed, which combines a physical law loss term and a data-driven loss term. The physical law loss term is based on a deep understanding of the mechanical characteristics of the bearing, which ensures that the model prediction results conform to basic physical laws. The data-driven loss term uses the actual protrusion amount identification interval in the historical installation data to supervise the training of the model, enabling the model to learn the potential laws and patterns in the data. After having the loss function, a BP (backpropagation) neural network is used to train the bearing protrusion amount configuration function. In this process, the preload record data is used as the input of the neural network, and the bearing protrusion amount identification interval record data is used as the supervision information to guide the weight adjustment of the neural network. Through multiple iterative trainings, the neural network gradually learns how to predict a reasonable bearing protrusion amount and its confidence interval according to the input preload value. Taking a certain type of ball screw support bearing as an example, hundreds of historical installation records may be collected, and each record contains the preload and the corresponding bearing protrusion amount identification interval. Using these data to construct a loss function and performing multiple trainings through a BP neural network. Finally, a configuration function that can accurately predict the bearing protrusion amount and its confidence interval is obtained. This function not only considers physical laws but also makes full use of the information in historical data, providing a powerful tool for the installation and performance evaluation of ball screw support bearings.

[0032] In a preferred embodiment, according to the physical law loss term and the data-driven loss term, a bearing protrusion amount configuration loss function is constructed, including: constructing the physical law loss term: ; where represents the physical law loss value of the i-th training, represents the predicted bearing protrusion amount confidence interval of the i-th training, represents the maximum value of the predicted bearing protrusion amount confidence interval of the i-th training, represents the minimum value of the predicted bearing protrusion amount confidence interval of the i-th training, represents the preload record data of the i-th training, w and b represent hyperparameters, w represents the weight parameter, b represents the bias parameter, K represents the bearing axial stiffness, is the bearing protrusion amount calculation function obtained by empirical fitting; constructing the data-driven loss term: ; where represents the data-driven loss value of the i-th training, Record data of the bearing protrusion amount identification interval characterizing the i-th training Characterize the intersection-over-union ratio threshold; construct the bearing protrusion amount configuration loss function: ; where Characterize the loss value after every N trainings Characterize the weight of the physical law loss term, and N characterizes the number of training times of the preset statistical loss value

[0033] Furthermore, construct a physical law loss term, which evaluates the confidence interval of the predicted bearing protrusion amount based on physical laws and mechanical characteristics. Specifically, for each training, calculate the maximum and minimum values of the confidence interval of the predicted bearing protrusion amount, and evaluate the degree of compliance with physical laws according to the preload force record data and hyperparameters (such as weight parameter w and bias parameter b) in the model. This loss value reflects the consistency between the prediction result and physical laws. The specific formula for the physical law loss term is: Characterize the median value of the bearing protrusion amount prediction interval Characterize the predicted value of the bearing protrusion amount fitted according to experience. Through Although the predicted value of the fitted bearing protrusion amount has a deviation, it is mainly a fluctuating value around a certain accurate value, so it also has reference value. Therefore, the physical law loss term characterizes the deviation between the current model prediction value and the predicted value obtained through If it is too large, it is abnormal. Introducing the physical law loss term aims to limit the output to conform to physical laws, thereby improving the convergence speed. As Figure 4 shown, for replacing different loss functions in the simulation code, the experimental result data of the convergence speed and accuracy are obtained. Among them, the double loss function refers to the function mentioned in the embodiment of the present invention :

[0034] Next, construct a data-driven loss term, which uses the record data of the bearing protrusion amount identification interval in the historical installation data to supervise the training of the model. Specifically, calculate the intersection-over-union ratio between the prediction result and the true identification interval, and compare it with a preset intersection-over-union ratio threshold to obtain the data-driven loss value. This loss value reflects the degree of agreement between the prediction result and the actual data. The specific formula for the data-driven loss term is: ; where Characterize the data-driven loss value of the i-th training Characterize the record data of the bearing protrusion amount identification interval of the i-th training Characterize the intersection ratio threshold. Finally, combine the physical law loss term and the data-driven loss term to construct a bearing protrusion amount configuration loss function. This function comprehensively considers the constraints of physical laws and data-driven. By adjusting the weight of the physical law loss term and the number of training times of the preset statistical loss value, the weight between the two can be balanced, so as to obtain a loss function that conforms to physical laws and is close to actual data. After every N trainings, calculate the loss value and update the model parameters according to it to optimize the prediction performance. The specific formula for the bearing protrusion amount configuration loss function is: ; where characterizes the loss value after every N trainings, characterizes the weight of the physical law loss term, and N characterizes the number of training times of the preset statistical loss value. To sum up, the loss function construction method combines the advantages of physical laws and data-driven, providing an accurate and reliable framework for predicting the protrusion amount of ball screw support bearings. Through continuous iterative training and optimization, a model that can accurately predict the protrusion amount of the bearing and its confidence interval can be obtained, providing strong support for the installation and performance evaluation of the bearing.

[0035] In a preferred embodiment, based on the bearing protrusion amount configuration loss function, with the data recorded in the bearing protrusion amount identification interval as the supervision and the pre-tightening force recorded data as the input, train the bearing protrusion amount configuration function through a BP neural network, including: initialize the number of neurons in the hidden layer of the BP neural network to 1. Based on the bearing protrusion amount configuration loss function, with the data recorded in the bearing protrusion amount identification interval as the supervision and the pre-tightening force recorded data as the input, train the first bearing protrusion amount configuration function through a BP neural network; when the verification loss value of the first bearing protrusion amount configuration function for consecutive preset times is greater than or equal to the convergence loss threshold, increase the number of neurons by 1 and execute a loop until the verification loss value of the Mth bearing protrusion amount configuration function for consecutive preset times is less than the convergence loss threshold, and set the Mth bearing protrusion amount configuration function as the bearing protrusion amount configuration function.

[0036] Exemplarily, an iterative training method based on a BP (backpropagation) neural network is adopted. This method combines a bearing protrusion amount configuration loss function to optimize the bearing protrusion amount configuration function. The training process starts with initializing the number of hidden layer neurons of the BP neural network to 1, which is a relatively simple starting point and helps to gradually explore the complexity of the model. Next, using the data recorded in the bearing protrusion amount identification interval as supervision information and the preload force recorded data as input, the first bearing protrusion amount configuration function is trained through the BP neural network. During the training process, the bearing protrusion amount configuration loss function is continuously calculated. This loss function comprehensively considers physical laws and data-driven constraints, providing a clear direction for model optimization. To evaluate the performance of the model, the validation loss value is calculated after each training and compared with a preset convergence loss threshold. If the validation loss value of the first bearing protrusion amount configuration function for a continuous preset number of times is greater than or equal to the convergence loss threshold, it means that the performance of the current model has not reached the expected standard, and the complexity of the neural network needs to be increased to further improve the prediction ability. Therefore, enter the loop process. In each loop, the number of hidden layer neurons is increased by 1 and the bearing protrusion amount configuration function is retrained. This iterative process continues until the validation loss value of a certain bearing protrusion amount configuration function (denoted as the Mth bearing protrusion amount configuration function) for a continuous preset number of times is less than the convergence loss threshold. At this time, it is considered that the model has reached sufficient complexity and the prediction performance has reached the expected standard. Finally, this Mth bearing protrusion amount configuration function is set as the final bearing protrusion amount configuration function for subsequent bearing protrusion amount prediction tasks. Through gradually increasing the complexity of the neural network and combining the optimization of the bearing protrusion amount configuration loss function throughout the training process, an accurate and reliable bearing protrusion amount prediction model is achieved.

[0037] In a preferred embodiment, extracting the bearing protrusion amount detection value of the bearing installation captured image includes: collecting the bearing installation captured record image, wherein the bearing installation captured record image has a bearing inner ring end face identification position and a bearing outer ring end face identification position; using the bearing inner ring end face identification position as supervision and the bearing installation captured record image as input, constructing a bearing inner ring end face position extraction channel based on a convolutional neural network; using the bearing outer ring end face identification position as supervision and the bearing installation captured record image as input, constructing a bearing outer ring end face position extraction channel based on a convolutional neural network; configuring an end face relative Euclidean distance calculation layer; connecting the output layers of the bearing inner ring end face position extraction channel and the bearing outer ring end face position extraction channel to the input layer of the end face relative Euclidean distance calculation layer to obtain a bearing protrusion amount detection model, and processing the bearing installation captured image to obtain the bearing protrusion amount detection value.

[0038] Specifically, bearing installation captured record images containing the identification positions of the inner ring end face and the outer ring end face of the bearing are collected. These images provide rich visual information and serve as the basis for subsequent position extraction and protrusion amount calculation. Next, a bearing inner ring end face position extraction channel and a bearing outer ring end face position extraction channel are respectively constructed based on a convolutional neural network. These two channels use the identification positions of the inner ring end face and the outer ring end face of the bearing as supervision information and the bearing installation captured record images as input. Through training, these two channels can accurately extract the position information of the inner ring and the outer ring end faces of the bearing from the images. To calculate the bearing protrusion amount, a relative Euclidean distance calculation layer for the end faces is configured. This calculation layer is responsible for receiving the output information from the bearing inner ring end face position extraction channel and the bearing outer ring end face position extraction channel and calculating the Euclidean distance between the two. This distance value is the required bearing protrusion amount detection value. Finally, the output layers of the bearing inner ring end face position extraction channel and the bearing outer ring end face position extraction channel are connected to the input layer of the relative Euclidean distance calculation layer for the end faces, thereby constructing a complete bearing protrusion amount detection model. This model can process the input bearing installation captured images and automatically output the bearing protrusion amount detection value. In summary, an automated and high-precision bearing protrusion amount detection process is achieved by combining a convolutional neural network and Euclidean distance calculation. This process not only improves the detection efficiency but also ensures the accuracy and reliability of the detection results.

[0039] The bearing protrusion amount detection method based on image vision recognition provided by the embodiment of the present invention has at least the following technical effects: 1. By introducing the target device model and the target component model as scene constraints, the pre-tightening force concentration value and the first bearing protrusion amount interval of the same-scene sample group of the ball screw support bearing model are analyzed. This prediction method based on scene constraints can more accurately reflect the bearing protrusion amount characteristics in the actual installation environment, improving the accuracy and practicality of the prediction.

[0040] 2. By constructing a physical law loss term and a data-driven loss term and constructing a bearing protrusion amount configuration loss function based on these loss terms. Then, an adaptive training is carried out using a BP neural network, and the optimal bearing protrusion amount configuration function is found by gradually increasing the number of neurons in the hidden layer. This adaptive training method can fully consider the dual constraints of physical laws and actual data, improving the generalization ability and prediction accuracy of the model.

[0041] 3. A position extraction channel for the end face of the bearing inner ring and the end face of the outer ring is constructed using a convolutional neural network, and the protrusion amount of the bearing is detected by calculating the Euclidean distance between the two. This detection method based on the convolutional neural network can automatically extract key position information from the captured image and perform accurate distance calculation, realizing the rapid and accurate detection of the protrusion amount of the bearing. At the same time, this method also has high robustness and adaptability, and can handle image detection tasks under different shooting angles and lighting conditions. As Figure 2 shown, based on the same inventive concept as the bearing protrusion amount detection method for image vision recognition provided in Embodiment 1, the embodiment of the present invention also provides an image vision recognition bearing protrusion amount detection system, and the system includes: A model recognition module 11, configured to obtain a target component model of the target device model on which the ball screw support bearing is installed.

[0043] An acquisition and statistics module 12, configured to collect a same-scene sample group that meets the scene constraint with the target device model and the target component model as scene constraints, and statistically analyze the preload concentration value and the first bearing protrusion amount interval.

[0044] A numerical processing module 13, configured to process the preload concentration value through a predefined bearing protrusion amount configuration function to obtain a bearing protrusion amount confidence interval.

[0045] An interval extraction module 14, configured to perform intersection interval extraction according to the bearing protrusion amount confidence interval to obtain a second bearing protrusion amount interval.

[0046] A numerical extraction module 15, configured to extract the bearing protrusion amount detection value of the bearing installation captured image.

[0047] An abnormal identification module 16, configured to perform abnormal identification of the bearing protrusion amount on the bearing according to the captured image when the bearing protrusion amount detection value does not belong to the second bearing protrusion amount interval.

[0048] Furthermore, the acquisition and statistics module 12 is further configured to perform the following steps: Retrieve the first bearing historical installation data of the healthy installation samples whose ball screw support bearing models meet the scene constraints, where the first bearing historical installation data includes a set of installation preload record values and a set of bearing protrusion amount detection record values; perform a central tendency analysis on the set of installation preload record values to obtain the preload concentration value; perform outlier deletion on the set of bearing protrusion amount detection record values to obtain the first bearing protrusion amount interval.

[0049] Furthermore, the acquisition and statistics module 12 is further configured to perform the following steps: Sort the detected bearing protrusion amount recorded values in ascending order to obtain the sorting result of the detected bearing protrusion amount recorded values; extract the first detected bearing protrusion amount recorded value at the rounded-up serial number of one-fourth of the sorting result of the detected bearing protrusion amount recorded values; extract the second detected bearing protrusion amount recorded value of the bearing protrusion amount at the rounded-down serial number of three-fourths of the sorting result of the detected bearing protrusion amount recorded values; calculate the modulus of the bearing protrusion amount deviation between the first detected bearing protrusion amount recorded value and the second detected bearing protrusion amount recorded value; use the first detected bearing protrusion amount recorded value minus 1.5 times the modulus of the bearing protrusion amount deviation to obtain the lower limit value of the bearing protrusion amount, and use the second detected bearing protrusion amount recorded value plus 1.5 times the modulus of the bearing protrusion amount deviation to obtain the upper limit value of the bearing protrusion amount; delete the outlier detected bearing protrusion amount recorded values that are less than the lower limit value of the bearing protrusion amount or greater than the upper limit value of the bearing protrusion amount from the set of detected bearing protrusion amount recorded values to obtain the first bearing protrusion amount interval of the remaining detected bearing protrusion amount record distribution.

[0050] Furthermore, the numerical processing module 13 is further configured to perform the following steps: Obtain the second bearing historical installation data of the ball screw support bearing model, where the second bearing historical installation data includes preload record data and bearing protrusion amount identification interval record data; construct a bearing protrusion amount configuration loss function according to the physical law loss term and the data-driven loss term; based on the bearing protrusion amount configuration loss function, use the bearing protrusion amount identification interval record data as supervision and the preload record data as input, and train the bearing protrusion amount configuration function through a BP neural network.

[0051] Furthermore, the numerical processing module 13 is further configured to perform the following steps: Construct a physical law loss term: ; where represents the physical law loss value of the i-th training, represents the predicted bearing protrusion amount confidence interval of the i-th training, represents the maximum value of the predicted bearing protrusion amount confidence interval of the i-th training, represents the minimum value of the predicted bearing protrusion amount confidence interval of the i-th training, represents the preload record data of the i-th training, w and b represent hyperparameters, w represents the weight parameter, and b represents the bias parameter; construct a data-driven loss term: ; where represents the data-driven loss value of the i-th training, represents the bearing protrusion amount identification interval record data of the i-th training, Characterize the union and intersection ratio threshold; construct the bearing protrusion amount configuration loss function: ; where characterizes the loss value after every N training times, characterizes the weight of the physical law loss term, and N characterizes the preset number of training times of the statistical loss value.

[0052] Furthermore, the numerical processing module 13 is further configured to perform the following steps: Initialize the number of neurons in the hidden layer of the BP neural network to 1. Based on the bearing protrusion amount configuration loss function, using the data recorded in the bearing protrusion amount identification interval as supervision and the preload force recorded data as input, train the first bearing protrusion amount configuration function through the BP neural network; when the verification loss value of the first bearing protrusion amount configuration function for a continuous preset number of times is greater than or equal to the convergence loss threshold, increase the number of neurons by 1 and execute the loop until the verification loss value of the Mth bearing protrusion amount configuration function for a continuous preset number of times is less than the convergence loss threshold, and set the Mth bearing protrusion amount configuration function as the bearing protrusion amount configuration function.

[0053] Furthermore, the numerical extraction module 15 is further configured to perform the following steps: Collect the bearing installation shooting record image, where the bearing installation shooting record image has the bearing inner ring end face identification position and the bearing outer ring end face identification position; using the bearing inner ring end face identification position as supervision and the bearing installation shooting record image as input, construct the bearing inner ring end face position extraction channel based on the convolutional neural network; using the bearing outer ring end face identification position as supervision and the bearing installation shooting record image as input, construct the bearing outer ring end face position extraction channel based on the convolutional neural network; configure the end face relative Euclidean distance calculation layer; connect the output layers of the bearing inner ring end face position extraction channel and the bearing outer ring end face position extraction channel with the input layer of the end face relative Euclidean distance calculation layer to obtain the bearing protrusion amount detection model, process the bearing installation shooting image, and obtain the bearing protrusion amount detection value.

[0054] Through the foregoing detailed description of a method for detecting the bearing protrusion amount by image vision recognition in this specification, those skilled in the art can clearly know a system for detecting the bearing protrusion amount by image vision recognition in this embodiment. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for the relevant parts, reference can be made to the description in the method part.

[0055] Embodiment Three:

[0056] Please refer to Figure 3 , Figure 3 which is a schematic diagram of an embodiment of the electronic device provided by the embodiment of the present invention. As Figure 3As shown in the figure, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, it implements a method for detecting the protrusion amount of a bearing in an image vision recognition as described in Embodiment 1.

[0057] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0058] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0059] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows or multiple flows and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0060] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one or more of the flows or multiple flows and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0061] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts.

[0062] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for detecting the protrusion amount of a bearing in image vision recognition, characterized in that Including: Obtain the target component model of the target device model for installing the ball screw support bearing; Taking the target device model and the target component model as scene constraints, collect a same-scene sample group that meets the scene constraints, and statistically analyze the preload concentration value and the first bearing protrusion amount interval; Process the preload concentration value through a predefined bearing protrusion amount configuration function to obtain a bearing protrusion amount confidence interval; Extract an intersection interval according to the bearing protrusion amount confidence interval to obtain a second bearing protrusion amount interval; Extract the bearing protrusion amount detection value of the bearing installation captured image; When the bearing protrusion amount detection value does not belong to the second bearing protrusion amount interval, mark the bearing with an abnormal bearing protrusion amount according to the captured image.

2. The method according to claim 1, wherein Taking the target device model and the target component model as scene constraints, collect a same-scene sample group that meets the scene constraints, and statistically analyze the preload concentration value and the first bearing protrusion amount interval, including: Retrieve the first bearing historical installation data of healthy installation samples whose ball screw support bearing models meet the scene constraints, where the first bearing historical installation data includes a set of installation preload record values and a set of bearing protrusion amount detection record values; Perform a central tendency analysis on the set of installation preload record values to obtain the preload concentration value; Perform outlier deletion on the set of bearing protrusion amount detection record values to obtain the first bearing protrusion amount interval.

3. The method according to claim 2, wherein Perform outlier deletion on the set of bearing protrusion amount detection record values to obtain the first bearing protrusion amount interval, including: Sort the bearing protrusion amount detection record values in ascending order to obtain a sorted result of the bearing protrusion amount detection record values; Extract the first bearing protrusion amount detection record value at the ceiling of one-fourth of the sorted result of the bearing protrusion amount detection record values; Extract the second bearing protrusion amount detection record value of the bearing protrusion amount at the floor of three-fourths of the sorted result of the bearing protrusion amount detection record values; Calculate the bearing protrusion amount deviation modulus value between the first bearing protrusion amount detection record value and the second bearing protrusion amount detection record value; Subtract 1.5 times the bearing protrusion amount deviation modulus value from the first bearing protrusion amount detection record value to obtain a bearing protrusion amount lower limit value, and add 1.5 times the bearing protrusion amount deviation modulus value to the second bearing protrusion amount detection record value to obtain a bearing protrusion amount upper limit value; Delete the outlier bearing protrusion amount detection record values in the set of bearing protrusion amount detection record values that are less than the bearing protrusion amount lower limit value or greater than the bearing protrusion amount upper limit value, and obtain the first bearing protrusion amount interval of the remaining bearing protrusion amount detection record value distribution.

4. The method according to claim 1, characterized in that, Process the preload concentration value through a predefined bearing protrusion amount configuration function to obtain a bearing protrusion amount confidence interval, including: Obtain the second bearing historical installation data of the ball screw support bearing model, where the second bearing historical installation data includes preload record data and bearing protrusion amount identification interval record data; Construct a bearing protrusion amount configuration loss function according to the physical law loss term and the data-driven loss term; Configure a loss function based on the bearing protrusion amount. Using the data recorded in the bearing protrusion amount identification interval as supervision and the data recorded of the pre-tightening force as input, train the bearing protrusion amount configuration function through a BP neural network.

5. The method according to claim 4, wherein Construct a bearing protrusion amount configuration loss function according to the physical law loss term and the data-driven loss term, including: Construct the physical law loss term: ， Among them, represents the physical law loss value of the i-th training, represents the confidence interval of the predicted bearing protrusion amount of the i-th training, represents the maximum value of the confidence interval of the predicted bearing protrusion amount of the i-th training, represents the minimum value of the confidence interval of the predicted bearing protrusion amount of the i-th training, represents the preload force recorded data of the i-th training, w and b represent hyperparameters, w represents the weight parameter, b represents the bias parameter, and K represents the bearing axial stiffness, is the bearing protrusion amount calculation function obtained by empirical fitting; Construct a data-driven loss term: , Among them, represents the data-driven loss value of the i-th training, represents the recorded data of the bearing protrusion amount identification interval of the i-th training, represents the intersection-over-union ratio threshold; ， Among them, represents the loss value after every N training times, represents the weight of the physical law loss term, and N represents the preset number of training times for the statistical loss value.

6. The method according to claim 5, characterized in that Based on the bearing protrusion amount configuration loss function, using the data recorded in the bearing protrusion amount identification interval as supervision and the data recorded of the pre-tightening force as input, train the bearing protrusion amount configuration function through a BP neural network, including: Initialize the number of neurons in the hidden layer of the BP neural network to 1. Based on the bearing protrusion amount configuration loss function, using the data recorded in the bearing protrusion amount identification interval as supervision and the data recorded of the pre-tightening force as input, train the first bearing protrusion amount configuration function through a BP neural network; When the verification loss value of the first bearing protrusion amount configuration function for a continuous preset number of times is greater than or equal to the convergence loss threshold, increase the number of neurons by 1 and execute the loop until the verification loss value of the Mth bearing protrusion amount configuration function for a continuous preset number of times is less than the convergence loss threshold, and set the Mth bearing protrusion amount configuration function as the bearing protrusion amount configuration function.

7. The method according to claim 1, characterized in that, Extract the bearing protrusion amount detection value of the bearing installation captured image, including: Collect the bearing installation captured record image, where the bearing installation captured record image has the bearing inner ring end face identification position and the bearing outer ring end face identification position; Using the bearing inner ring end face identification position as supervision and the bearing installation captured record image as input, construct a bearing inner ring end face position extraction channel based on a convolutional neural network; Using the bearing outer ring end face identification position as supervision and the bearing installation captured record image as input, construct a bearing outer ring end face position extraction channel based on a convolutional neural network; Configure an end face relative Euclidean distance calculation layer; Connect the output layers of the bearing inner ring end face position extraction channel and the bearing outer ring end face position extraction channel to the input layer of the end face relative Euclidean distance calculation layer to obtain a bearing protrusion amount detection model, process the bearing installation captured image, and obtain the bearing protrusion amount detection value.

8. A bearing protrusion amount detection system for image visual recognition, characterized in that, A system for implementing the bearing protrusion amount detection method for image vision recognition according to any one of claims 1-7, the system includes: A model identification module for obtaining the target component model of the target device model on which the ball screw support bearing is installed; A collection and statistics module for taking the target device model and the target component model as scene constraints, collecting a same-scene sample group that meets the scene constraints, and statistically calculating the pre-tightening force concentration value and the first bearing protrusion amount interval; A numerical processing module for processing the pre-tightening force concentration value through a predefined bearing protrusion amount configuration function to obtain a bearing protrusion amount confidence interval; An interval extraction module for performing intersection interval extraction according to the bearing protrusion amount confidence interval to obtain a second bearing protrusion amount interval; A numerical extraction module for extracting the bearing protrusion amount detection value of the bearing installation captured image; An abnormal identification module, configured to perform abnormal identification of the bearing protrusion amount on the bearing according to the captured image when the detected value of the bearing protrusion amount does not belong to the second bearing protrusion amount interval.

9. An electronic device, characterized in that, It includes: A memory for storing computer software programs; A processor for reading and executing the computer software program, thereby implementing the method for detecting the bearing protrusion amount by image vision recognition according to any one of claims 1-7.

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