CN115741218B

CN115741218B - Machine tool fault early warning method and system based on machining image analysis

Info

Publication number: CN115741218B
Application number: CN202310011878.4A
Authority: CN
Inventors: 王钢强
Original assignee: Shan County Asia Pacific Paper Products Co ltd
Current assignee: Shan County Asia Pacific Paper Products Co ltd
Priority date: 2023-01-05
Filing date: 2023-01-05
Publication date: 2023-04-21
Anticipated expiration: 2043-01-05
Also published as: CN115741218A

Abstract

The invention relates to the field of image processing, in particular to a machine tool fault early warning method and system based on machining image analysis.

Description

Machine tool fault early warning method and system based on machining image analysis

Technical Field

Background

In the field of modern machining, numerical control machine tool machining has taken a dominant role, and especially for machining parts with large batch and high precision requirements, various numerical control machine tools play an irreplaceable role, so that the working stability and reliability of the numerical control machine tools have very important significance for improving the production efficiency of enterprises.

Chinese patent publication No. CN112650146B discloses a fault diagnosis optimization method, a fault diagnosis optimization system and fault diagnosis optimization equipment for a numerical control machine under multiple working conditions, wherein the effectiveness of data acquisition and utilization is improved by arranging and optimizing sensors of the numerical control machine, characteristic information of the numerical control machine in different states is extracted by utilizing an improved multi-scale entropy algorithm, deep characteristic information is mined, and the differentiation of characteristics among different states is improved; on the basis, the ITML-K mean value clustering is utilized to conduct numerical control machine tool working condition identification, so that the problem that the identification effect is poor under the condition of multi-working condition boundary blurring in the traditional clustering method is solved; finally, the problem of overfitting of the fault diagnosis model of the numerical control machine driven by data is solved by utilizing the regularization function based on entropy, so that generalization and accuracy of the fault diagnosis model of the numerical control machine are improved, optimization of the fault diagnosis model of the numerical control machine is realized, and the method has important help for improving operation safety and reliability of the numerical control machine and improving fault diagnosis rate of the numerical control machine.

Therefore, according to the technical scheme, the machine tool fault diagnosis model is established to identify the machine tool fault, and the type of the machine tool fault can not be accurately distinguished, so that the machine tool fault diagnosis is high in complexity and poor in accuracy.

Disclosure of Invention

Therefore, the invention provides a machine tool fault early warning method and system based on processing portrait analysis, which can solve the technical problems of high complexity and poor accuracy of machine tool fault diagnosis in the prior art.

In order to achieve the above object, the present invention provides a machine tool fault early warning method based on processing portrait analysis, comprising:

acquiring processing temperature information and processing image information of a machine tool according to a standard transmission speed, determining the safety level of the machine tool according to the processing temperature information, and determining an alarm mode according to the safety level;

when the machine tool accords with a preset safety level, acquiring the pixel number of a processed image, determining the image level of the processed image according to the pixel number, and processing the processed image according to the image level;

when the processing image accords with a preset image grade, restoring the processing image to obtain a standard processing image;

performing time-frequency conversion on the standard processing image and adjusting the standard processing image in a frequency domain to obtain an adjusted processing image;

Storing the gray value of the adjustment processing image in a target linked list, calculating the similarity between the target linked list and a preset standard linked list, judging the abnormal condition of a machine tool according to the similarity, and extracting the edge of the adjustment processing image when the similarity meets the preset similarity condition so as to obtain an effective processing image;

calculating the similarity between the effective processing image and a preset risk image, and judging the abnormal condition of the machine tool according to the rechecking similarity, wherein the similarity is used as rechecking similarity;

and acquiring an effective machining image for judging that the machine tool has faults, generating a three-dimensional track model according to the judging machining image and time as a judging machining image, and judging the abnormal condition of the machine tool according to the three-dimensional track model.

Further, a processing thermal image is generated according to the processing temperature information, a gray level image of the processing thermal image is obtained, a gray level frequency histogram is generated according to the gray level image, the frequency histogram is divided into three parts at the positions of 85 and 175 of abscissa along the direction parallel to the ordinate, the three parts comprise a first part, a second part and a third part, the safety level of the machine tool is determined according to the frequency area of the three parts, and three types of alarm modes for the machine tool are determined according to the safety level;

The first type of alarm mode is that when the frequency area of the first part accords with a first area condition, the machine tool is judged to be a first safety level, and no alarm is carried out;

the second type of alarming mode is that when the frequency area of the second part accords with a second area condition, the machine tool is judged to be a second safety level, and alarming is carried out by using the first alarming mode;

the third type of alarming mode is that when the frequency area of the third part accords with a third area condition, the machine tool is judged to be of a third safety level, alarming is carried out by using the second alarming mode, and emergency braking is carried out on the machine tool;

the first area condition comprises that the frequency area of the first part is the highest in the frequency areas of the three parts, the second area condition comprises that the frequency area of the second part is the highest in the frequency areas of the three parts, the third area condition comprises that the frequency area of the third part is the highest in the frequency areas of the three parts, the safety of the first safety level is larger than that of the second safety level, the safety of the second safety level is larger than that of the third safety level, and the emergency degree of the first alarm mode is smaller than that of the second alarm mode.

Further, when the machine tool accords with a preset first safety level, acquiring the pixel number of the processing image according to the processing image information, determining the image level of the processing image according to the pixel number, and determining two types of processing modes according to the image level to obtain a standard processing image;

the first processing mode is that when the pixel number accords with a first pixel condition, the processing image is judged to be a first image grade, and image restoration is carried out on the processing image so as to obtain the standard processing image;

the second processing mode is that when the pixel number accords with a second pixel condition, the processing image is judged to be a second image grade, the processing image is directly used as the standard processing image, and the standard transmission speed is corrected by using a speed correction coefficient;

the first pixel condition includes that the number of pixels is smaller than a preset number of pixels, the first pixel condition includes that the number of pixels is greater than or equal to the preset number of pixels, and the definition of the first image level is smaller than the second image level.

Further, when the processed image accords with a preset first image level, calculating an image variance of a gray value of the processed image, dividing the processed image into at least two partial images, calculating a partial variance of the gray value of the partial images, and determining three types of image restoration modes of the processed image according to the image variance and the partial images;

The first type of image restoration method is that when the image variance and the local variance meet a first restoration condition, the processing image is restored by using the first restoration method;

the second type of image restoration mode is that when the image variance and the local variance meet a second restoration condition, the processing image is restored by using a second restoration mode;

the third type of image restoration method is that when the image variance and the local variance meet a third restoration condition, the processed image is restored by using a third restoration method;

the first restoration condition includes that the local variance is 0, the second restoration condition includes that the local variance is not 0 and the local variance and the image variance are not equal, the third restoration condition includes that the local variance is not 0 and the local variance and the image variance are equal, the first restoration mode includes that the gray value of the local image is not changed, the second restoration mode includes that the gray value of the local image is corrected by using a gray value correction coefficient, and the third restoration mode includes that the gray value of the local image is changed to the local variance.

Further, performing Fourier transform on the standard processing image to obtain a processing spectrogram, determining three types of frequency adjustment modes for the processing spectrogram according to the processing spectrogram, adjusting the frequencies in the processing spectrogram according to the frequency adjustment modes to obtain an adjustment spectrogram, and performing inverse Fourier transform on the adjustment spectrogram to obtain an adjustment processing image;

the first type of frequency adjustment mode is that when the processing spectrogram accords with a first frequency condition, the processing spectrogram is adjusted by using the first adjustment mode;

the second type of frequency adjustment mode is that when the processing spectrogram accords with a second frequency condition, the processing spectrogram is adjusted by using the second adjustment mode;

the third type of frequency adjustment mode is that when the processing spectrogram accords with a third frequency condition, the processing spectrogram is adjusted by using the third adjustment mode;

the first frequency condition includes that the frequency is smaller than a first preset frequency, the second frequency condition includes that the frequency is larger than or equal to the first preset frequency and smaller than the second preset frequency, the third frequency condition includes that the frequency is larger than or equal to the second preset frequency, the first preset frequency is smaller than the second preset frequency, the first adjustment mode includes that a denoising process is performed on the frequency by using a denoising formula, and the denoising formula is that

X is the abscissa value of the processing spectrogram, y is the ordinate value of the processing spectrogram, D (x, y) is the distance from the point with the spectral coordinates (x, y) to the center of the spectrogram, and D1 is the cut-off frequency; the second adjustment mode comprises not adjusting the frequencyThe third adjustment mode comprises enhancement processing of the frequency by using an enhancement formula, wherein the enhancement formula is that

。

Further, acquiring a gray value of each pixel point in the adjustment processing image according to a preset time interval, setting a storage bit corresponding to each pixel point in a preset linked list as a target gray value, storing the target gray value in the storage bit to obtain a target linked list, calculating the similarity between the target linked list and the preset standard linked list, and determining three types of execution modes according to the similarity;

the first type of execution mode is that when the similarity meets a first similarity condition, the machine tool is judged to have a type of fault, and a first alarm mode is used for alarming;

the second type of implementation mode is that when the similarity accords with a second similarity condition, the adjustment processing image is adjusted by using a preset adjustment mode so as to obtain an effective processing image;

The third type of execution mode is that when the similarity accords with a third similarity condition, the machine tool is judged to be normal, and the first correction coefficient is used for correcting the preset time interval;

the first similarity condition includes that the similarity is smaller than a first preset similarity, the second similarity condition includes that the similarity is larger than or equal to the first preset similarity and smaller than a second preset similarity, the third similarity condition includes that the similarity is larger than or equal to the second preset similarity, the first preset similarity is smaller than the second preset similarity, the type of faults includes that machine tool elements deform, the preset adjustment mode includes that edge extraction is conducted on the adjustment machining image by using an edge extraction formula, and the edge extraction formula is that

Wherein m is the number of rows of the pixel points, n is the number of columns of the pixel points, and G (m, n)And (3) for adjusting the gray value of the nth row and the nth column in the processed image, wherein sigma is the average value of the pixel points.

Further, calculating the similarity between the effective processing image and a preset risk image based on a neural network algorithm, and determining two control modes according to the check similarity as the check similarity;

The first control mode is that when the rechecking similarity accords with a first rechecking condition, the machine tool is judged to have a fault, and a first alarm mode is used for alarming;

the second control mode is that when the rechecking similarity accords with a second rechecking condition, the machine tool is judged to be normal, and a second correction coefficient is used for correcting the preset time interval;

the first rechecking condition comprises that the rechecking similarity is smaller than a third preset similarity, and the second rechecking condition comprises that the rechecking similarity is larger than or equal to the third preset similarity.

Further, acquiring an effective machining image for judging that a type of fault exists in the machine tool, acquiring a central coordinate of an abnormal region of the judging machining image as a judging machining image, generating an abnormal element track according to the motion condition of the central coordinate, generating a three-dimensional track model according to the abnormal element track and time, selecting track images corresponding to a preset number of time points in the three-dimensional track model, comparing the track images with preset model images to obtain abnormal proportions, and determining three types of management modes according to the abnormal proportions;

The first management mode is that when the abnormal proportion accords with a first abnormal condition, the tool is judged to work normally;

the second type of management mode is that when the abnormal proportion accords with a second abnormal condition, abnormal risk exists in the tool work, and a third correction coefficient is used for correcting the preset quantity;

the third type of management mode is that when the abnormal proportion accords with a third abnormal condition, the machine tool is judged to have a second type of faults, and a fourth correction coefficient is used for correcting the preset quantity;

the preset number is greater than or equal to two, the first abnormal condition comprises that the abnormal proportion is equal to zero, the second abnormal condition comprises that the abnormal proportion is smaller than the first preset proportion, the third abnormal condition comprises that the abnormal proportion is greater than or equal to the first preset proportion, and the second type of faults comprise that a machine tool element is clamped.

In a second aspect, the present invention provides a machine tool fault early warning system based on machining portrait analysis, comprising:

the information acquisition module is used for acquiring processing temperature information and processing image information of the machine tool according to the standard transmission speed, determining the safety level of the machine tool according to the processing temperature information, and determining an alarm mode according to the safety level;

The safety confirmation module is used for acquiring the pixel number of the processing image when the machine tool accords with a preset safety level, determining the image level of the processing image according to the pixel number, and processing the processing image according to the image level;

the image processing module is used for recovering the processing image when the processing image accords with a preset image grade so as to obtain a standard processing image;

the image preprocessing module is used for performing time-frequency conversion on the standard processing image and adjusting the standard processing image in a frequency domain to obtain an adjusted processing image;

the similarity judging module is used for storing the gray value of the adjustment processing image in a target linked list, calculating the similarity between the target linked list and a preset standard linked list, judging the abnormal condition of the machine tool according to the similarity, and extracting the edge of the adjustment processing image when the similarity meets the preset similarity condition so as to obtain an effective processing image;

the rechecking module is used for calculating the similarity between the effective processing image and the preset risk image, and judging the abnormal condition of the machine tool according to the rechecking similarity;

the track judging module is used for acquiring an effective machining image for judging that the machine tool has faults, generating a three-dimensional track model according to the judging machining image and time as a judging machining image, and judging the abnormal condition of the machine tool according to the three-dimensional track model.

Further, the safety confirmation module is configured to generate a processing thermal image according to the processing temperature information, acquire a gray level image of the processing thermal image, generate a gray level frequency histogram according to the gray level image, divide the frequency histogram into three parts along a direction parallel to an ordinate at positions 85 and 175 on an abscissa, wherein the three parts comprise a first part, a second part and a third part, determine a safety level of the machine tool according to a frequency area of the three parts, and determine three types of alarm modes for the machine tool according to the safety level;

Compared with the prior art, the invention has the beneficial effects that the processing temperature information and the processing image information of the machine tool are obtained according to the standard transmission speed; determining the safety level of the machine tool according to the processing temperature information, determining the alarm mode of the machine tool according to the safety level, and firstly judging the most urgent temperature fault to ensure the safety of the machine tool; when the machine tool accords with a preset safety level, determining the image level of the processed image according to the processed image information, analyzing and processing according to the image level, obtaining standard image information, enhancing and denoising an unclear image, and improving the identification accuracy; performing image preprocessing on the standard image information to obtain an adjustment processing image, and improving the quality of all images; storing the adjustment processing image in a target linked list, calculating the similarity between the target linked list and a preset standard linked list, and analyzing the abnormal condition of the element according to the similarity so as to realize rapid judgment of the abnormal condition; the adjustment processing image which accords with the preset similarity condition is adjusted to obtain an effective processing image, the abnormal condition of the element is rechecked, and the accuracy of fault identification is improved through secondary judgment; and determining the abnormal condition of the track of the machine tool element according to the effective machining image, and identifying various abnormal scenes.

Further, a processing thermal image is generated according to the processing temperature information, a gray level image of the processing thermal image is obtained, a gray level frequency histogram is generated according to the gray level image, the frequency histogram is divided into three parts at the positions of 85 and 175 of abscissa along the direction parallel to the ordinate, the three parts comprise a first part, a second part and a third part, the safety level of the machine tool is determined according to the frequency area of the three parts, and three types of alarm modes for the machine tool are determined according to the safety level; the first type of alarm mode is that when the frequency area of the first part accords with a first area condition, the machine tool is judged to be a first safety level, and no alarm is carried out; the second type of alarming mode is that when the frequency area of the second part accords with a second area condition, the machine tool is judged to be a second safety level, and alarming is carried out by using the first alarming mode; the third type of alarming mode is that when the frequency area of the third part accords with a third area condition, the machine tool is judged to be of a third safety level, alarming is carried out by using the second alarming mode, and emergency braking is carried out on the machine tool; the first area condition comprises that the frequency area of the first part is the highest in the frequency areas of the three parts, the second area condition comprises that the frequency area of the second part is the highest in the frequency areas of the three parts, the third area condition comprises that the frequency area of the third part is the highest in the frequency areas of the three parts, the safety of the first safety level is greater than that of the second safety level, the safety of the second safety level is greater than that of the third safety level, the emergency degree of the first alarm mode is less than that of the second alarm mode, machine tool safety accidents are effectively avoided, the most urgent temperature faults are judged at first, the safety of a machine tool is guaranteed, and the technical effect of accurately and timely detecting the machine tool faults is achieved.

Further, when the machine tool accords with a preset first safety level, acquiring the pixel number of the processing image according to the processing image information, determining the image level of the processing image according to the pixel number, and determining two types of processing modes according to the image level to obtain a standard processing image; the first processing mode is that when the pixel number accords with a first pixel condition, the processing image is judged to be a first image grade, and image restoration is carried out on the processing image so as to obtain the standard processing image; the second processing mode is that when the pixel number accords with a second pixel condition, the processing image is judged to be a second image grade, the processing image is directly used as the standard processing image, and the standard transmission speed is corrected by using a speed correction coefficient; the first pixel condition comprises that the number of pixels is smaller than the preset number of pixels, the first pixel condition comprises that the number of pixels is larger than or equal to the preset number of pixels, the definition of a first image grade is smaller than that of a second image grade, the enhancement denoising is carried out on an unclear image, the accuracy of recognition is improved, the safety of a machine tool is guaranteed, and the technical effect of accurately and timely detecting faults of the machine tool is achieved.

Further, when the processed image accords with a preset first image level, calculating an image variance of a gray value of the processed image, dividing the processed image into at least two partial images, calculating a partial variance of the gray value of the partial images, and determining three types of image restoration modes of the processed image according to the image variance and the partial images; the first type of image restoration method is that when the image variance and the local variance meet a first restoration condition, the processing image is restored by using the first restoration method; the second type of image restoration mode is that when the image variance and the local variance meet a second restoration condition, the processing image is restored by using a second restoration mode; the third type of image restoration method is that when the image variance and the local variance meet a third restoration condition, the processed image is restored by using a third restoration method; the first recovery condition comprises that the local variance is 0, the second recovery condition comprises that the local variance is not 0 and the local variance and the image variance are not equal, the third recovery condition comprises that the local variance is not 0 and the local variance and the image variance are equal, the first recovery mode comprises that the gray value of the local image is not changed, the second recovery mode comprises that the gray value of the local image is corrected by using a gray value correction coefficient, the third recovery mode comprises that the gray value of the local image is changed into the local variance, the quality of all images is improved, the safety of a machine tool is guaranteed, and the technical effect of accurately and timely detecting the faults of the machine tool is realized.

Further, performing Fourier transform on the standard processing image to obtain a processing spectrogram, determining three types of frequency adjustment modes for the processing spectrogram according to the processing spectrogram, adjusting the frequencies in the processing spectrogram according to the frequency adjustment modes to obtain an adjustment spectrogram, and performing inverse Fourier transform on the adjustment spectrogram to obtain an adjustment processing image; the first type of frequency adjustment is that when the processing spectrogram is signedWhen the first frequency condition is met, a first adjusting mode is used for adjusting the processing spectrogram; the second type of frequency adjustment mode is that when the processing spectrogram accords with a second frequency condition, the processing spectrogram is adjusted by using the second adjustment mode; the third type of frequency adjustment mode is that when the processing spectrogram accords with a third frequency condition, the processing spectrogram is adjusted by using the third adjustment mode; the first frequency condition includes that the frequency is smaller than a first preset frequency, the second frequency condition includes that the frequency is larger than or equal to the first preset frequency and smaller than the second preset frequency, the third frequency condition includes that the frequency is larger than or equal to the second preset frequency, the first preset frequency is smaller than the second preset frequency, the first adjustment mode includes that a denoising process is performed on the frequency by using a denoising formula, and the denoising formula is that

X is the abscissa value of the processing spectrogram, y is the ordinate value of the processing spectrogram, D (x, y) is the distance from the point with the spectral coordinates (x, y) to the center of the spectrogram, and D1 is the cut-off frequency; the second adjustment mode comprises not adjusting the frequency, the third adjustment mode comprises performing enhancement processing on the frequency by using an enhancement formula, wherein the enhancement formula is that

The method and the device realize rapid judgment of abnormal conditions, ensure the safety of the machine tool, and realize the technical effect of accurately and timely detecting the faults of the machine tool.

Further, acquiring a gray value of each pixel point in the adjustment processing image according to a preset time interval, setting a storage bit corresponding to each pixel point in a preset linked list as a target gray value, storing the target gray value in the storage bit to obtain a target linked list, calculating the similarity between the target linked list and the preset standard linked list, and determining three types of execution modes according to the similarity; the first type of execution mode is that when the similarity meets a first similarity condition, the judgment is carried outThe machine tool has a fault type, and alarms in a first alarm mode; the second type of implementation mode is that when the similarity accords with a second similarity condition, the adjustment processing image is adjusted by using a preset adjustment mode so as to obtain an effective processing image; the third type of execution mode is that when the similarity accords with a third similarity condition, the machine tool is judged to be normal, and the first correction coefficient is used for correcting the preset time interval; the first similarity condition includes that the similarity is smaller than a first preset similarity, the second similarity condition includes that the similarity is larger than or equal to the first preset similarity and smaller than a second preset similarity, the third similarity condition includes that the similarity is larger than or equal to the second preset similarity, the first preset similarity is smaller than the second preset similarity, the type of faults includes that machine tool elements deform, the preset adjustment mode includes that edge extraction is conducted on the adjustment machining image by using an edge extraction formula, and the edge extraction formula is that

Wherein m is the number of rows of the pixel points, n is the number of columns of the pixel points, G (m, n) is the gray value of the mth row and the nth column in the adjustment processing image, sigma is the mean value of the pixel points, the accuracy of fault identification is improved through secondary judgment, the safety of a machine tool is ensured, and the technical effect of accurately and timely detecting the faults of the machine tool is achieved.

Further, calculating the similarity between the effective processing image and a preset risk image based on a neural network algorithm, and determining two control modes according to the check similarity as the check similarity; the first control mode is that when the rechecking similarity accords with a first rechecking condition, the machine tool is judged to have a fault, and a first alarm mode is used for alarming; the second control mode is that when the rechecking similarity accords with a second rechecking condition, the machine tool is judged to be normal, and a second correction coefficient is used for correcting the preset time interval; the first checking condition comprises that the checking similarity is smaller than a third preset similarity, and the second checking condition comprises that the checking similarity is larger than or equal to the third preset similarity, so that the safety of the machine tool is ensured, and the technical effect of accurately and timely detecting the faults of the machine tool is achieved.

Further, acquiring an effective machining image for judging that a type of fault exists in the machine tool, acquiring a central coordinate of an abnormal region of the judging machining image as a judging machining image, generating an abnormal element track according to the motion condition of the central coordinate, generating a three-dimensional track model according to the abnormal element track and time, selecting track images corresponding to a preset number of time points in the three-dimensional track model, comparing the track images with preset model images to obtain abnormal proportions, and determining three types of management modes according to the abnormal proportions; the first management mode is that when the abnormal proportion accords with a first abnormal condition, the tool is judged to work normally; the second type of management mode is that when the abnormal proportion accords with a second abnormal condition, abnormal risk exists in the tool work, and a third correction coefficient is used for correcting the preset quantity; the third type of management mode is that when the abnormal proportion accords with a third abnormal condition, the machine tool is judged to have a second type of faults, and a fourth correction coefficient is used for correcting the preset quantity; the first abnormal condition comprises that the abnormal proportion is equal to zero, the second abnormal condition comprises that the abnormal proportion is smaller than the first preset proportion, the third abnormal condition comprises that the abnormal proportion is equal to or greater than the first preset proportion, the second type of faults comprise that machine tool elements are clamped, the safety of a machine tool can be guaranteed through identifying various abnormal scenes, and the technical effect of accurately and timely detecting the faults of the machine tool is achieved.

Drawings

FIG. 1 is a flow chart of a machine tool fault early warning method based on processing portrait analysis provided by an embodiment of the invention;

fig. 2 is a schematic structural diagram of a machine tool fault early warning system based on processing portrait analysis according to an embodiment of the present invention.

Detailed Description

In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.

It is noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and in the foregoing figures, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.

Referring to fig. 1, a flow chart of a machine tool fault early warning method based on processing portrait analysis according to an embodiment of the present invention includes:

s101, acquiring processing temperature information and processing image information of a machine tool according to a standard transmission speed, determining the safety level of the machine tool according to the processing temperature information, and determining an alarm mode according to the safety level;

s102, when the machine tool accords with a preset safety level, acquiring the pixel number of a processed image, determining the image level of the processed image according to the pixel number, and processing the processed image according to the image level;

s103, restoring the processing image to obtain a standard processing image when the processing image accords with a preset image grade;

s104, performing time-frequency conversion on the standard processing image and adjusting the standard processing image in a frequency domain to obtain an adjusted processing image;

s105, storing the gray value of the adjustment processing image in a target linked list, calculating the similarity between the target linked list and a preset standard linked list, judging the abnormal condition of a machine tool according to the similarity, and extracting edges of the adjustment processing image when the similarity meets a preset similarity condition so as to obtain an effective processing image;

S106, calculating the similarity between the effective processing image and a preset risk image, and judging the abnormal condition of the machine tool according to the rechecking similarity, wherein the similarity is used as rechecking similarity;

and S107, acquiring an effective machining image for judging that the machine tool has faults, generating a three-dimensional track model according to the judging machining image and time as a judging machining image, and judging the abnormal condition of the machine tool according to the three-dimensional track model.

The invention has the beneficial effects that the processing temperature information and the processing image information of the machine tool are obtained according to the standard transmission speed; determining the safety level of the machine tool according to the processing temperature information, determining the alarm mode of the machine tool according to the safety level, and firstly judging the most urgent temperature fault to ensure the safety of the machine tool; when the machine tool accords with a preset safety level, determining the image level of the processed image according to the processed image information, analyzing and processing according to the image level, obtaining standard image information, enhancing and denoising an unclear image, and improving the identification accuracy; performing image preprocessing on the standard image information to obtain an adjustment processing image, and improving the quality of all images; storing the adjustment processing image in a target linked list, calculating the similarity between the target linked list and a preset standard linked list, and analyzing the abnormal condition of the element according to the similarity so as to realize rapid judgment of the abnormal condition; the adjustment processing image which accords with the preset similarity condition is adjusted to obtain an effective processing image, the abnormal condition of the element is rechecked, and the accuracy of fault identification is improved through secondary judgment; and determining the abnormal condition of the track of the machine tool element according to the effective machining image, and identifying various abnormal scenes.

Specifically, a processing thermal image is generated according to the processing temperature information, a gray level image of the processing thermal image is obtained, a gray level frequency histogram is generated according to the gray level image, the frequency histogram is divided into three parts at the positions of 85 and 175 of abscissa along the direction parallel to the ordinate, the three parts comprise a first part, a second part and a third part, the safety level of the machine tool is determined according to the frequency area of the three parts, and three types of alarm modes for the machine tool are determined according to the safety level;

optionally, the ordinate of the gray frequency histogram is frequency, the abscissa is gray level, the gray level of 0-255 is equally divided into three equal parts, and the larger the frequency area of the part with higher gray level is, the larger the temperature range is, and the larger the probability of faults caused by abnormal machine tool operation is.

Specifically, when the machine tool accords with a preset first safety level, acquiring the pixel number of the processing image according to the processing image information, determining the image level of the processing image according to the pixel number, and determining two types of processing modes according to the image level to obtain a standard processing image;

Specifically, when the processed image accords with a preset first image level, calculating an image variance of a gray value of the processed image, dividing the processed image into at least two partial images, calculating a partial variance of the gray value of the partial images, and determining three types of image restoration modes of the processed image according to the image variance and the partial images;

Specifically, performing fourier transform on the standard processing image to obtain a processing spectrogram, determining three types of frequency adjustment modes for the processing spectrogram according to the processing spectrogram, adjusting frequencies in the processing spectrogram according to the frequency adjustment modes to obtain an adjustment spectrogram, and performing inverse fourier transform on the adjustment spectrogram to obtain an adjustment processing image;

。

Specifically, the gray value of each pixel point in the adjustment processing image is obtained according to a preset time interval and is used as a target gray value, a storage bit corresponding to each pixel point is set in a preset linked list, the target gray value is stored in the storage bit to obtain a target linked list, the similarity between the target linked list and the preset standard linked list is calculated, and three types of execution modes are determined according to the similarity;

the first similarity condition includes that the similarity is smaller than a first preset similarity, the second similarity condition includes that the similarity is larger than or equal to the first preset similarity and smaller than a second preset similarity, the third similarity condition includes that the similarity is larger than or equal to the second preset similarity, the first preset similarity is smaller than the second preset similarity, the type of faults includes that machine tool elements deform, the preset adjustment mode includes that edge extraction is conducted on the adjustment machining image by using an edge extraction formula, and the edge extraction is conductedThe formula is

Wherein m is the number of rows of the pixel points, n is the number of columns of the pixel points, G (m, n) is the gray value of the mth row and the nth column in the adjustment processing image, and σ is the average value of the pixel points.

Specifically, calculating the similarity of the effective processing image and a preset risk image based on a neural network algorithm, and determining two control modes according to the check similarity as the check similarity;

Specifically, an effective machining image for judging that a type of fault exists in the machine tool is obtained and used as a judging machining image, the central coordinates of an abnormal area of the judging machining image are obtained, an abnormal element track is generated according to the motion condition of the central coordinates, a three-dimensional track model is generated according to the abnormal element track and time, track images corresponding to a preset number of time points in the three-dimensional track model are selected, the track images are compared with preset model images, so that the obtained abnormal proportion is obtained, and three types of management modes are determined according to the abnormal proportion;

The first management mode is that when the abnormal proportion accords with a first abnormal condition, the machine tool element is judged to work normally;

the second type of management mode is that when the abnormal proportion accords with a second abnormal condition, abnormal risk exists in the work of the machine tool element, and a third correction coefficient is used for correcting the preset quantity;

Referring to fig. 2, which is a schematic structural diagram of a machine tool fault early warning system based on machining portrait analysis according to an embodiment of the present invention, the machine tool fault early warning system based on machining portrait analysis includes:

the information acquisition module 201 is configured to acquire processing temperature information and processing image information of a machine tool according to a standard transmission speed, determine a safety level of the machine tool according to the processing temperature information, and determine an alarm mode according to the safety level;

The safety confirmation module 202 is configured to obtain the number of pixels of a processed image when the machine tool meets a preset safety level, determine an image level of the processed image according to the number of pixels, and process the processed image according to the image level;

the image processing module 203 is configured to restore the processed image to obtain a standard processed image when the processed image meets a preset image level;

the image preprocessing module 204 is configured to perform time-frequency conversion on the standard processing image and adjust the standard processing image in a frequency domain to obtain an adjusted processing image;

the similarity judging module 205 is configured to store the gray value of the adjusted processing image in a target linked list, calculate the similarity between the target linked list and a preset standard linked list, judge the abnormal condition of the machine tool according to the similarity, and perform edge extraction on the adjusted processing image when the similarity meets a preset similarity condition, so as to obtain an effective processing image;

the rechecking module 206 is configured to calculate a similarity between the effective machining image and a preset risk image, and determine a machine tool abnormal condition according to the rechecking similarity as a rechecking similarity;

The track judging module 207 is configured to obtain an effective machining image for judging that the machine tool has a fault, generate a three-dimensional track model according to the judging machining image and time, and judge the abnormal condition of the machine tool according to the three-dimensional track model.

Further, the safety confirmation module 202 is configured to generate a processing thermal image according to the processing temperature information, obtain a gray level image of the processing thermal image, generate a gray level frequency histogram according to the gray level image, divide the frequency histogram into three parts along a direction parallel to an ordinate at the abscissa being 85 and 175, wherein the three parts include a first part, a second part and a third part, determine a safety level of the machine tool according to a frequency area of the three parts, and determine three types of alarm modes for the machine tool according to the safety level;

It should be noted that, in the embodiment of the machine tool fault early warning system based on the processing image analysis, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.

Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.

The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A machine tool fault early warning method based on machining portrait analysis is characterized by comprising the following steps:

2. The machine tool fault early warning method based on machining image analysis according to claim 1, characterized in that a machining thermal image is generated according to the machining temperature information, a gray level image of the machining thermal image is obtained, a gray level frequency histogram is generated according to the gray level image, the frequency histogram is divided into three parts at the positions of 85 and 175 on the abscissa along the direction parallel to the ordinate, the three parts comprise a first part, a second part and a third part, the safety level of the machine tool is determined according to the frequency area of the three parts, and three types of warning modes for the machine tool are determined according to the safety level;

3. The machine tool fault early warning method based on processing portrait analysis according to claim 2, characterized in that when the machine tool accords with a preset first security level, the number of pixels of the processing image is obtained according to the processing image information, the image level of the processing image is determined according to the number of pixels, and two types of processing modes are determined according to the image level so as to obtain a standard processing image;

the first pixel condition includes that the number of pixels is smaller than a preset number of pixels, the second pixel condition includes that the number of pixels is greater than or equal to the preset number of pixels, and the definition of the first image level is smaller than the second image level.

4. The machine tool fault early warning method based on the machined image analysis according to claim 3, wherein when the machined image accords with a preset first image level, calculating an image variance of a gray value of the machined image, dividing the machined image into at least two partial images, calculating a partial variance of the gray value of the partial images, and determining three types of image restoration modes of the machined image according to the image variance and the partial images;

5. The machine tool fault early warning method based on the machined image analysis according to claim 4, wherein the standard machined image is subjected to fourier transformation to obtain a machined spectrogram, three types of frequency adjustment modes for the machined spectrogram are determined according to the machined spectrogram, frequencies in the machined spectrogram are adjusted according to the frequency adjustment modes to obtain an adjusted spectrogram, and the adjusted spectrogram is subjected to inverse fourier transformation to obtain an adjusted machined image;

the first frequency condition includes that the frequency is smaller than a first preset frequency, the second frequency condition includes that the frequency is larger than or equal to the first preset frequency and smaller than a second preset frequency, the third frequency condition includes that the frequency is larger than or equal to the second preset frequency, the first preset frequency is smaller than the second preset frequency, the first adjustment mode includes that denoising processing is performed on the frequency by using a denoising formula, and the denoising formula is that

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6. The machine tool fault early warning method based on the machined image analysis according to claim 5, wherein the gray value of each pixel point in the machined image is obtained according to a preset time interval and is used as a target gray value, a storage bit corresponding to each pixel point is set in a preset linked list, the target gray value is stored in the storage bit to obtain a target linked list, the similarity of the target linked list and the preset standard linked list is calculated, and three types of execution modes are determined according to the similarity;

the first similarity condition includes that the similarity is smaller than a first preset similarity, the second similarity condition includes that the similarity is larger than or equal to the first preset similarity and smaller than a second preset similarity, the third similarity condition includes that the similarity is larger than or equal to the second preset similarity, the first preset similarity is smaller than the second preset similarity, the fault includes that machine tool elements deform, the preset adjustment mode includes that edge extraction is conducted on the adjustment processing image by using an edge extraction formula, and the edge extraction formula is that

7. The machine tool fault early warning method based on machining portrait analysis according to claim 6, which is characterized in that the similarity between the effective machining image and a preset risk image is calculated based on a neural network algorithm and used as a check similarity, and two control modes are determined according to the check similarity;

The first control mode is that when the rechecking similarity accords with a first rechecking condition, the machine tool is judged to have a fault, and an alarm is given by using a first alarm mode;

8. The machine tool fault early warning method based on machining image analysis according to claim 7, characterized in that an effective machining image for judging that a type of fault exists in the machine tool is obtained as a judging machining image, center coordinates of an abnormal area of the judging machining image are obtained, an abnormal element track is generated according to the motion condition of the center coordinates, a three-dimensional track model is generated according to the abnormal element track and time, track images corresponding to a preset number of time points in the three-dimensional track model are selected, the track images are compared with preset model images, so that obtained abnormal proportions are used, and three types of management modes are determined according to the abnormal proportions;

the first abnormal condition comprises that the abnormal proportion is equal to zero, the second abnormal condition comprises that the abnormal proportion is smaller than a first preset proportion, the third abnormal condition comprises that the abnormal proportion is equal to or larger than the first preset proportion, and the second type faults comprise that machine tool elements are clamped.

9. Machine tool fault early warning system based on processing portrait analysis, characterized by comprising:

10. The machine tool fault pre-warning system based on machining representation analysis of claim 9, comprising:

the safety confirmation module is used for generating a processing thermal image according to the processing temperature information, acquiring a gray level image of the processing thermal image, generating a gray level frequency histogram according to the gray level image, dividing the frequency histogram into three parts at the positions of 85 and 175 of abscissa along the direction parallel to the ordinate, wherein the three parts comprise a first part, a second part and a third part, determining the safety level of the machine tool according to the frequency area of the three parts, and determining three types of alarm modes of the machine tool according to the safety level;