CN114842011B - Bearing wear detection method and system based on image processing - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, in particular to a bearing wear detection method and a bearing wear detection system based on image processing, wherein the detection method comprises the following steps: acquiring a surface image to be measured of a bearing wear surface, wherein the bearing wear surface is a side surface worn by a bearing; traversing and matching the surface image to be detected by utilizing the template image block to obtain a similarity sequence, wherein the area of the surface image to be detected, which corresponds to the similarity greater than a preset similarity threshold value in the similarity sequence, is a wear area; the template image block is a pre-generated image block, and the scale of the template image block is smaller than that of the surface image to be detected; the step of acquiring the template image block comprises the steps of randomly acquiring a wear surface image with bearing wear defects according to history, extracting a defect area in the wear surface image, wherein the defect area is the template image block, and the problem of influence of an irrelevant defect area on defect identification precision caused by using an integral template in the traditional template matching is solved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a bearing wear detection method and system based on image processing.
Background
At present, most of electric appliances comprise motors, such as an engine, a generator and the like, the motors are the most prone to failure due to long-term high-speed operation, and the main shaft failure is mostly caused by bearing failure. Consequently, can carry out corresponding maintenance to the bearing when main shaft motor breaks down, disassemble main shaft motor and overhaul the trouble one by one, and the wearing and tearing that detect the bearing rely on artifical naked eye to detect mostly, detection efficiency is lower and cause the false detection easily, especially to defects such as the skin damage broken string of stator coil. Patent publication No. CN110246122A proposes that a similarity measurement is performed on a captured bearing image through a template image in an image database, and a defect region is identified by setting a defect threshold.
In practice, the inventors found that the above prior art has the following disadvantages:
the whole bearing image is used as a template image for similarity calculation, compared background features of the whole image have large image matching calculation amount, high requirement on defect threshold precision, sensitive response to noise points and poor detection effect on local small defects.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a bearing wear detection method and system based on image processing, which adopts the following technical solutions:
in a first aspect, an embodiment of the present invention provides a bearing wear detection method based on image processing, where the detection method includes:
acquiring a surface image to be measured of a bearing wear surface, wherein the bearing wear surface is a side surface worn by a bearing;
traversing and matching the surface image to be detected by utilizing a template image block to obtain a similarity sequence, wherein the area of the surface image to be detected, which corresponds to the similarity greater than a preset similarity threshold value in the similarity sequence, is a wear area;
the template image block is a pre-generated image block, and the scale of the template image block is smaller than that of the surface image to be detected; the step of obtaining the template image block comprises the steps of extracting a defect area in a wear surface image according to the wear surface image with bearing wear defects which is collected randomly according to history, wherein the defect area is the template image block.
Further, the detection method comprises the step of optimizing the template image block: and obtaining a wear index according to the weighted summation of the texture feature and the color feature of the defect region, and screening the defect region according to the wear index to obtain the optimized template image block.
Further, the step of obtaining the texture features of the defect area includes: traversing each pixel point in the defect region to obtain a first distribution probability of the pixel points with the same gray scale feature, and obtaining a texture information entropy of the defect region according to the first distribution probability of each pixel point, wherein the texture information entropy is the texture feature; the gray feature is that the pixel point is taken as a central pixel point, and a domain pixel point of the central pixel point is obtained; forming a first binary group by the central pixel point and the neighborhood pixel points; and obtaining the first distribution probability according to the occurrence times of the first binary group.
Further, the step of obtaining the color characteristics of the defect area comprises: converting the defect area into an HSV color space to obtain an HSV image, traversing each pixel point of the defect area in the HSV image to obtain a second distribution probability of pixel points with the same color characteristic, and obtaining a color information entropy of the defect area according to the second distribution probability of each pixel point, wherein the color information entropy is the color characteristic; forming a second binary group by using hue information and brightness information of each pixel point in the defect area of the HSV image; and obtaining the second distribution probability according to the occurrence times of the second binary group.
Further, after the step of screening the defect area according to the wear indicator to obtain the template image block, the method further comprises an optimization step of: and carrying out corrosion operation on the template image block for multiple times, stopping corrosion when the abrasion index changes after each corrosion operation, and selecting a corrosion image before the current corrosion operation as the optimized template image block.
Further, the step of extracting a defect region in the wear surface image includes: and performing semantic segmentation on the wear surface image to obtain the defect area.
Further, the step of performing traversal matching on the surface image to be detected by using the template image block to obtain a similarity sequence further comprises an optimization matching step: and adaptively adjusting the matching step length according to the change trend of the similarity.
Further, the step of performing traversal matching on the surface image to be detected by using the template image block to obtain a similarity sequence further comprises an optimization matching step: and increasing the matching step length when the change trends of a plurality of continuous similarity are the same.
Further, the method for obtaining the similarity sequence by traversing and matching the surface image to be detected by using the template image block adopts a normalized cross-correlation algorithm to obtain the similarity between the template image block and each matching block in the surface image to be detected.
In a second aspect, an embodiment of the present invention provides an image processing-based bearing wear detection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any one of the above methods when executing the computer program.
The invention has the following beneficial effects:
according to the embodiment of the invention, the surface image to be detected of the bearing wear surface is acquired, the template image block is utilized to perform traversal matching on the surface image to be detected to obtain the similarity sequence, the area of the surface image to be detected corresponding to the similarity which is greater than the preset threshold in the similarity sequence is the wear area, a standard image library is not required to be constructed, the template matching is performed on the acquired surface image to be detected through the template image block, the defect identification precision is improved, and the influence of an irrelevant defect area on the defect identification precision caused by the utilization of an integral template in the traditional template matching is avoided. The template image block obtained by using the defect image acquired by history can generate a defect template image block in a self-adaptive manner according to the defect characteristics of the actual defect image, and the generalization capability of the system can be improved occasionally.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a bearing wear detection method based on image processing according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given to a method and a system for detecting bearing wear based on image processing according to the present invention, with reference to the accompanying drawings and preferred embodiments, and the detailed implementation, structure, features and effects thereof are described below. In the following description, the different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The motor comprises a stator, a rotor and a bearing for driving the rotor to rotate, the bearing precision is reduced due to other reasons in the rotating process of the bearing, the rotor can abrade the surface of the stator under the driving action of the bearing, and obvious abrasion traces can appear on the surface of the stator. For the spindle motor to be overhauled, after disassembly, a surface image of the stator is collected and analyzed in a machine vision mode, and whether the surface image is a defect or not is judged according to the characteristics of the image.
The following describes a specific scheme of a bearing wear detection method and system based on image processing in detail with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a bearing wear detection method based on image processing according to an embodiment of the present invention is shown, where the method includes the following steps:
s001, acquiring a surface image to be detected of a bearing wear surface, wherein the bearing wear surface is a side surface worn by a bearing;
a camera is arranged at an opening of the bearing, the position and posture of the camera can be guaranteed to acquire images of an easily worn area at the edge of the bearing, and a stable light source and a fixed camera position and posture are adopted to acquire images of different bearings.
S002, traversing and matching the surface image to be detected by using the template image block to obtain a similarity sequence, wherein the area of the surface image to be detected, which corresponds to the similarity greater than a preset threshold value in the similarity sequence, is a wear area; the template image block is a pre-generated image block, and the scale of the template image block is smaller than that of the surface image to be detected; the step of obtaining the template image block comprises the steps of randomly collecting a wear surface image with bearing wear defects according to history, and extracting a defect area in the wear surface image, wherein the defect area is the template image block.
In order to extract more accurate defect characteristics, a large number of wear surface images with bearing wear defects are collected, defect areas in the wear surface images are extracted, and the purpose of extracting the characteristics more accurately is achieved by extracting characteristic information of the defect areas.
Wherein the step of extracting the defect region in the wear surface image comprises: and performing semantic segmentation on the wear surface image to obtain a defect area. Specifically, before semantic segmentation, preprocessing is performed on an acquired wear surface image, wherein the preprocessing includes performing projection transformation on the wear surface image to obtain a planar image of the wear surface image, and performing graying processing on the planar image to obtain a grayscale image. Because the image shot by the camera has the characteristic of big-end-up and small-end-up, the image of the worn surface is projected and transformed into a front-view plane image so as to eliminate the phenomenon of big-end-up and small-end-up. Taking gray-scale images corresponding to all historically acquired wear surface images as training set images, and marking the wear defects in the gray-scale images: and marking the wear defect pixel point as 1 and other pixel points as 0. And inputting the marked gray level image into a semantic segmentation network for image convolution to extract a feature vector of the gray level image, and obtaining a defect segmentation image of the gray level image through deconvolution. The semantic segmentation network adopts an Encoder-Decoder structure, and the loss function adopts a cross entropy loss function.
And taking the defect segmentation image as a mask to extract a corresponding defect image in the surface image, taking the defect image corresponding to the surface image as a template image block, and identifying the defect characteristics of the surface image to be detected by using the template image block.
In the embodiment of the present invention, a Normalized cross-correlation (NCC) algorithm is used to obtain the similarity between the template image block and each matching block in the surface image to be measured. The NNC algorithm is a commonly used algorithm in template matching, and other template matching algorithms capable of realizing the same function may also be used in other embodiments. In the embodiment of the invention, the step length of traversing and matching the surface image to be measured by using the over-template image block by using the NCC algorithm is 3.
Preferably, in order to achieve the purpose of accurately matching and identifying the local defects of the bearing, the template image block is obtained by performing multi-local-area fusion according to the defect characteristics of each local defect area. Specifically, the step of optimizing the template image block includes: and obtaining a wear index according to the weighted summation of the texture feature and the color feature of the defect region, and screening the defect region according to the wear index to obtain the optimized template image block.
The method comprises the following steps of obtaining the texture features of the defect area: traversing each pixel point in the defect region to obtain a first distribution probability of the pixel points with the same gray scale feature, and obtaining a texture information entropy of the defect region according to the first distribution probability of each pixel point, wherein the texture information entropy is a texture feature; the gray feature is that a field pixel point of a central pixel point is obtained by taking the pixel point as the central pixel point; forming a first binary group by the central pixel point and the neighborhood pixel point; and obtaining a first distribution probability according to the occurrence times of the first binary group.
Specifically, the method comprises the following steps: and traversing each pixel point in the defect area, taking the traversed pixel point as a central pixel point, and calculating the gray average value of eight neighborhood pixel points of the central pixel point. The first binary set composed of the average gray levels of the central pixel and the field pixels is recorded as
Wherein, in the step (A),
is shown as
The gray value of each of the center pixel points,
is shown as
And the gray average value of the eight neighborhood pixels of each central pixel. Counting the frequency duplet corresponding to the occurrence frequency of the first duplet in the current defect area
According to the total number of the pixel points contained in the defect area, the distribution probability of different binary groups in the wear area is counted
，
Denotes the first
The distribution probability of the frequency bins corresponding to the first bin,
denotes the first
The frequency bins corresponding to the first bins are the frequency bins of the defect area,
the subscript that represents the pixel point is,
expressing the number of pixel points of the defect area, and obtaining the texture information entropy of the defect area according to the distribution probability of the first binary group
. The larger the texture information entropy is, the more complex the texture of the defect region is, the more gray-level pixel points exist, and the larger the corresponding wear degree of the region is.
The step of acquiring the color characteristics of the defect area comprises the following steps: converting the defect area into an HSV color space to obtain an HSV image, traversing each pixel point of the defect area in the HSV image to obtain a second distribution probability of the pixel points with the same color characteristic, and obtaining a color information entropy of the defect area according to the second distribution probability of each pixel point, wherein the color information entropy is the color characteristic; forming a second binary group by using hue information and brightness information of each pixel point in a defect area of the HSV image; and obtaining a second distribution probability according to the occurrence times of the second binary group.
Specifically, gray scale inverse transformation is carried out on the image of the defect area to obtain an RGB image, and HSV color space transformation is carried out on the RGB image to obtain an HSV image. Each pixel point of the corresponding wear area has three channel values of hue (H), saturation (S) and lightness (V), and the bearing wear characteristics comprise: the method comprises the steps of enabling the surface of a bearing to have red rust spots, enabling a part of smooth area generated due to abrasion to be slightly abraded and enabling a defect surface generated due to abrasion to be severely abraded, enabling the surface of the bearing abrasion area to possibly present various hue values and brightness differences of different degrees according to the abrasion characteristics of the bearing, and enabling hue information and brightness information of each pixel point in the defect area of an HSV image to form a second binary group to be recorded as a second binary group
Wherein, in the step (A),
denotes the first
The hue of each of the pixels is determined,
is shown as
The brightness of each pixel. Counting the frequency binary group corresponding to the occurrence frequency of the second binary group in the current defect area
According to lack ofThe total number of pixel points contained in the sunk area is used for counting the distribution probability of different second binary groups in the worn area
，
Is shown as
The distribution probability of the frequency doublet corresponding to the second doublet,
is shown as
The frequency bin corresponding to the second bin is the frequency bin of the defect area,
the subscript that represents the pixel point is,
the number of pixels representing the defective area. Obtaining the color information entropy of the defect area according to the spatial distribution characteristics and the color richness of the color
. The larger the entropy of the color information is, the more complicated the color of the defect area is, the more likely there is combined wear of a plurality of wear degrees, and the larger the wear degree of the corresponding curve area is.
Further, since the texture information entropy may only reflect the gray distribution change of the surface under different wear degrees, the wear at different levels can be evaluated more accurately by means of the color complexity. Thus, combining the two characteristics to obtain a wear index, the wear index is labeled as
Then, there are:
wherein, the first and the second end of the pipe are connected with each other,
is the weight of the texture feature(s),
is the weight of the color feature.
In the embodiment of the present invention, it is,
the value of (a) is 0.6,
is 0.4.
Screening all defect areas according to the wear indexes, and enabling the wear indexes in all defect areas to be maximum
And taking the corresponding abrasion area as an optimized template image, and matching the surface image to be detected by using the optimized template image.
Preferably, because the surface of the stator has uniformly distributed lines, the groove part of the line is not easy to wear. In order to remove the evaluation influence of the correlation area between the adjacent defect areas, the precision of subsequent template matching is improved. After the step of screening the defect area according to the wear index to obtain the template image block, the method also comprises an optimization step: and carrying out corrosion operation on the template image block for multiple times, stopping corrosion when the abrasion index changes after each corrosion operation, and selecting a corrosion image before the current corrosion operation as the optimized template image block. And when the abrasion index is not changed after each corrosion operation, continuously corroding for at most four times, and taking the corroded image as a final optimized template image.
Preferably, in order to improve the matching efficiency of the template and realize the rapid defect detection of the surface image to be detected, the step of performing traversal matching on the surface image to be detected by using the template image block to obtain a similarity sequence further comprises the step of optimizing matching: and the matching step length is adjusted in a self-adaptive mode according to the change trend of the similarity.
Specifically, based on the characteristics of bearing wear, when a certain area is worn, the adjacent areas are worn to different degrees, so that a defect area can be an aggregation area; accordingly, the adjacent area of the normal area is not easily worn. In order to improve the speed of template matching, in the process of template image block matching, when the change trends of a plurality of continuous similarity degrees are the same, the step length of matching is increased. Specifically, according to the similarity between the template image block and the corresponding region of the surface image to be measured
The size of the step length is self-adaptively adjusted and matched; when the traversal is carried out along the same direction, the step length of the traversal is changed and the matching is continued in the process of continuously increasing the similarity obtained by the continuous adjacent template image blocks; and when the image similarity is smaller than the step size adjusting threshold value, increasing the step size. Specifically, the adjusted step length is recorded as
The regulated amount is recorded as
Then adjusted step size
Wherein, in the embodiments of the present invention
The step size adjustment threshold is 0.1.
In order to ensure the matching precision of the template image blocks, the template image is matched according to the change trend of the similarityRolling back the matching step length of the image block; when the similarity corresponding to the continuous adjacent template image blocks meets the process of continuously increasing again, the template image blocks roll back by one step length, the matching of the template image blocks is carried out again, and the initial step length is recovered
。
When the template image block is matched, the area corresponding to the template image block in the plane image is marked as a defect area when the similarity corresponding to the template image block is greater than the similarity threshold value, so that the defect area is quickly detected.
In the embodiment of the present invention, the value of the similarity threshold is 0.5, and in other embodiments, the similarity threshold may be set as needed.
And defect image analysis and feature extraction are not required to be carried out on all bearing images, and rapid defect detection can be realized by utilizing a minimized optimization template.
To sum up, in the embodiment of the invention, the surface image to be detected of the bearing wear surface is acquired, the template image block is used for traversing and matching the surface image to be detected to obtain the similarity sequence, the area of the surface image to be detected corresponding to the similarity greater than the preset threshold in the similarity sequence is the wear area, a standard image library is not required to be constructed, the template matching is carried out on the acquired surface image to be detected through the template image block, the defect identification precision is improved, and the influence of an irrelevant defect area on the defect identification precision caused by using an integral template in the traditional template matching is avoided. The template image blocks obtained by using the historically collected defect images can generate defect template image blocks in a self-adaptive manner according to the defect characteristics of the actual defect images, and meanwhile, the generalization capability of the system can be improved.
Based on the same inventive concept as the method embodiment, the embodiment of the present invention further provides an image processing-based bearing wear detection system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the image processing-based bearing wear detection method according to any one of the above embodiments when executing the computer program. A bearing wear detection method based on image processing has been described in detail in the above embodiments, and is not described again.
It should be noted that: the sequence of the above embodiments of the present invention is only for description, and does not represent the advantages or disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A bearing wear detection method based on image processing is characterized by comprising the following steps:
acquiring a surface image to be measured of a bearing wear surface, wherein the bearing wear surface is a side surface worn by a bearing;
traversing and matching the surface image to be detected by utilizing a template image block to obtain a similarity sequence, wherein the area of the surface image to be detected, which corresponds to the similarity greater than a preset similarity threshold value in the similarity sequence, is a wear area;
the template image block is a pre-generated image block, and the scale of the template image block is smaller than that of the surface image to be detected; the acquiring step of the template image block comprises the steps of extracting a defect area in a wear surface image according to the wear surface image with bearing wear defects which is randomly acquired according to history, wherein the defect area is the template image block;
the detection method comprises the following steps of optimizing the template image block:
obtaining a wear index according to the weighted summation of the texture feature and the color feature of the defect region, and screening the defect region according to the wear index to obtain an optimized template image block;
the step of acquiring the color characteristics of the defect area comprises the following steps:
converting the defect area into an HSV color space to obtain an HSV image, traversing each pixel point of the defect area in the HSV image to obtain a second distribution probability of the pixel points with the same color characteristic, and obtaining a color information entropy of the defect area according to the second distribution probability of each pixel point, wherein the color information entropy is the color characteristic;
forming a second binary group by using hue information and brightness information of each pixel point in the defect area of the HSV image; obtaining the second distribution probability according to the occurrence times of the second binary group;
the step of obtaining the texture features of the defect area comprises the following steps:
traversing each pixel point in the defect region to obtain a first distribution probability of the pixel points with the same gray scale feature, and obtaining a texture information entropy of the defect region according to the first distribution probability of each pixel point, wherein the texture information entropy is the texture feature;
the gray feature is that the pixel point is taken as a central pixel point, and a domain pixel point of the central pixel point is obtained; forming a first binary group by the central pixel point and the neighborhood pixel points; obtaining the first distribution probability according to the occurrence times of the first binary group;
the obtaining step of obtaining the wear index according to the weighted summation of the texture feature and the color feature of the defect area comprises the following steps:
the wear index is calculated by adopting the following calculation formula:
wherein the content of the first and second substances,
is a wear index, R is a texture feature, C is a color feature,
is the weight of the texture feature and is,
is the weight of the color feature.
2. The method according to claim 1, wherein after the step of screening the defect area according to the wear indicator to obtain the template image block, the method further comprises an optimization step of:
and carrying out corrosion operation on the template image block for multiple times, stopping corrosion when the abrasion index changes after each corrosion operation, and selecting a corrosion image before the current corrosion operation as the optimized template image block.
3. The image processing-based bearing wear detection method according to claim 1, wherein the step of extracting the defect region in the wear surface image comprises: and performing semantic segmentation on the wear surface image to obtain the defect area.
4. The image processing-based bearing wear detection method according to claim 1, wherein the step of performing traversal matching on the image of the surface to be detected by using the template image block to obtain a similarity sequence further comprises an optimization matching step of: and adaptively adjusting the matching step length according to the change trend of the similarity.
5. The image processing-based bearing wear detection method according to claim 1, wherein the step of performing traversal matching on the image of the surface to be detected by using the template image block to obtain a similarity sequence further comprises an optimization matching step of: and increasing the matching step length when the change trends of a plurality of continuous similarity are the same.
6. The method for detecting bearing wear based on image processing as claimed in claim 1, wherein the method for obtaining the similarity sequence by traversing and matching the surface image to be detected with the template image block employs a normalized cross-correlation algorithm to obtain the similarity between the template image block and each matching block in the surface image to be detected.
7. An image processing based bearing wear detection system comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor when executing the computer program implementing the steps of the image processing based bearing wear detection method according to any one of claims 1-6.
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| CN115082466B (en) * | 2022-08-22 | 2023-09-01 | 倍利得电子科技(深圳)有限公司 | PCB surface welding spot defect detection method and system |
| CN116152249B (en) * | 2023-04-20 | 2023-07-07 | 济宁立德印务有限公司 | Intelligent digital printing quality detection method |
| CN117664984A (en) * | 2023-12-01 | 2024-03-08 | 上海宝柏新材料股份有限公司 | Defect detection method, device, system and storage medium |
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