Computer vision bearing fault diagnosis system based on data processing

Abstract

The invention discloses a computer vision bearing fault diagnosis system based on data processing, which relates to the technical field of data processing and comprises an influence judgment end, an influence correction end and a collaborative diagnosis end. The influence judgment end is used for detecting noise parameters and illumination parameters of bearing fault diagnosis in real time and setting standard noise parameters and standard illumination parameters which do not influence the bearing fault diagnosis; the influence correction end is used for adjusting noise parameters in real time according to a difference value when the noise and illumination are judged to influence the definition and accuracy of bearing fault diagnosis; and the collaborative diagnosis end is used for collaborative comparison and collaborative optimization in combination with the noise parameters and the illumination parameters. According to the computer vision bearing fault diagnosis system based on data processing, whether bearing fault diagnosis is affected by abnormities or not can be judged in real time, the abnormities occurring in the bearing diagnosis process can be corrected in time, and bearing fault causes can be diagnosed cooperatively.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a computer vision bearing fault diagnosis system based on data processing. Background Art

[0002] Computer vision bearing fault diagnosis based on data processing is a method that uses advanced computer vision and artificial intelligence technologies to identify and diagnose potential faults by analyzing the data generated during the operation of the bearing. It uses high-precision sensors (such as accelerometers, microphones or high-speed cameras) to monitor the operating status of the bearing in real time, collect relevant data, and clean, denoise and filter the collected data to improve data quality and prepare for subsequent feature extraction. It uses time domain analysis, frequency domain analysis or time-frequency analysis to extract feature information reflecting the operating status of the bearing from the preprocessed data, so that users can intuitively understand the health status and potential fault conditions of the bearing.

[0003] Publication No. CN117606802A discloses a bearing fault diagnosis method and system based on order analysis, the method includes assembling the tested bearing with a mandrel on a test device, determining the candidate characteristic order according to the speed order analysis spectrum, and determining the candidate fault frequency according to the frequency order analysis spectrum; determining the fault diagnosis result of the tested bearing according to the candidate characteristic order and the candidate fault frequency. The tested bearing is subjected to fault vibration through order analysis, and the bearing fault and the location of the fault in the rolling bearing during the variable speed process can be quickly analyzed through order analysis, and the speed signal and the vibration acceleration signal are synchronously analyzed to improve the accuracy of bearing fault diagnosis.

[0004] After searching the above patents, it was found that computer vision bearing fault diagnosis based on data processing still has some shortcomings: 1. Images collected by computer vision and signal transmission data are often easily affected by noise and uneven lighting. Although these noises and light intensities can be detected in real time in the existing technology, it is impossible to timely determine whether these noises and lights have problems affecting the clarity and accuracy of bearing fault diagnosis; 2. When noise and light affect the clarity and accuracy of bearing fault diagnosis, it is impossible to reduce noise interference and adjust the lighting angle in real time, resulting in abnormalities in the bearing diagnosis process cannot be corrected in real time, affecting the convenience of bearing diagnosis; 3. When noise and lighting are normal, the image information captured by vision cannot jointly diagnose the cause of the bearing fault, resulting in deviations in the bearing fault diagnosis and the inability to guarantee the accuracy of the bearing fault diagnosis.

[0005] Therefore, a computer vision bearing fault diagnosis system based on data processing is proposed to solve the above problems. Summary of the invention

[0006] The main purpose of the present invention is to provide a computer vision bearing fault diagnosis system based on data processing to solve the problems raised in the above background.

[0007] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a computer vision bearing fault diagnosis system based on data processing, including an impact judgment end, an impact correction end and a collaborative diagnosis end;

[0008] The impact judgment end, the impact correction end and the collaborative diagnosis end are jointly provided with a voice alarm unit;

[0009] The impact judgment end is used to detect the noise parameters and light parameters of the bearing fault diagnosis in real time, set the standard noise parameters and standard light parameters that will not affect the bearing fault diagnosis, calculate the difference between the noise parameters and light parameters and the standard noise parameters and standard light parameters in real time, and judge whether these noises and lights affect the bearing fault diagnosis in time according to the difference;

[0010] The impact correction end is used to receive noise and light parameters in real time, and adjust the noise parameters and the illumination angle in real time according to the difference when it is determined that the noise and light affect the clarity and accuracy of the bearing fault diagnosis;

[0011] The collaborative diagnosis end is used to generate image information through information captured by computer vision when it is determined that the noise parameters and illumination parameters in the process of bearing fault diagnosis are normal, and to perform collaborative diagnosis of bearing faults in real time by combining collaborative comparison and collaborative optimization of noise parameters and illumination parameters;

[0012] The voice alarm unit is used to send out a voice alarm signal in real time through a voice alarm when the system is abnormal, and feedback is given to manual processing.

[0013] The impact judgment end includes a region monitoring module, a data detection module and an impact judgment module;

[0014] The area monitoring module includes a bearing area unit and an area monitoring unit;

[0015] The bearing area unit is used to set the visual capture area range of the bearing, and set the camera to rotate the bearing at multiple angles through a timer;

[0016] The area monitoring unit is used to perform real-time monitoring of the bearing in the corresponding visual capture area through a camera to obtain an appearance image of the bearing.

[0017] The data detection module includes a bearing noise unit and an illumination parameter unit;

[0018] The bearing noise unit is used to calculate the noise parameters at the current moment in real time through a calculation formula, and the calculation formula is as follows:

[0019]

[0020] Among them, MSN represents the noise parameter at the current moment, xi is the data observation value, is the true value of the data, n is the number of data points;

[0021] The illumination parameter unit is used to calculate the illumination parameters within the visual capture area in real time through a calculation formula, and the calculation formula is as follows:

[0022]

[0023] Where Ii is the illumination parameter of the measurement point within the i-th visual capture area, and n is the number of measurement points within the visual capture area.

[0024] The impact judgment module includes a standard parameter unit, a noise detection unit, a light detection unit and an impact judgment unit;

[0025] The standard parameter unit is used to set standard noise parameters and standard illumination parameters;

[0026] The noise detection unit is used to calculate the difference between the noise parameter at the current moment and the standard noise parameter;

[0027] The illumination detection unit is used to determine whether the illumination within the visual capture area is uniform by using a uniformity ratio calculation formula, and the calculation formula is as follows:

[0028]

[0029] Among them, Uniformity represents the uniformity ratio within the visual capture area. If the uniformity ratio is less than or equal to 1 and greater than or equal to 0.9, it means that the lighting is uniform. If the uniformity ratio is less than 0.9, it means that the lighting is uneven.

[0030] The impact judgment unit is used to judge that the bearing fault diagnosis is affected when there is a difference between the noise parameter and the standard noise parameter and the illumination is uneven, and to judge that the bearing fault diagnosis is not affected when there is no difference between the noise parameter and the standard noise parameter and the illumination is uniform.

[0031] The impact correction end includes an abnormal receiving module, a data correction module and a correction tracking module;

[0032] The abnormality receiving module is used to receive data abnormalities in real time through a data receiver.

[0033] The data correction module includes a difference calculation unit, a parameter sorting unit and a parameter correction unit;

[0034] The difference calculation unit is used for recalculating the difference between the noise parameter and the standard noise parameter and the difference between the illumination parameter and the standard illumination parameter;

[0035] The parameter sorting unit is used to arrange the calculated differences in order, using Arabic numerals to sort them in ascending order;

[0036] The parameter correction unit is used to correct the noise parameter and the illumination parameter in real time according to the calculated difference. If the difference between the noise parameter and the standard noise parameter is a positive number, the noise parameter is correspondingly reduced through a filter. If the difference between the noise parameter and the standard noise parameter is a negative number, the noise parameter is correspondingly increased through a filter. If the illumination is uneven, the corresponding illumination intensity Imin or Iavg is adjusted.

[0037] The correction tracking module includes a correction tracking unit and a tracking early warning unit;

[0038] The correction tracking unit is used to recalculate the adjusted noise parameters and illumination parameters through a calculation formula, and perform difference calculation between the noise parameters and illumination parameters calculated once and the noise parameters and illumination parameters calculated twice. If the difference is equal to 0, it indicates that the correction is invalid, and if the difference is not equal to 0, it indicates that the correction is valid;

[0039] The tracking and early warning unit is used to report that the system sends out a voice alarm signal and feedbacks manual processing when it is judged that the correction is invalid.

[0040] The collaborative diagnosis terminal includes an information acquisition module, a fault update module, a collaborative diagnosis module and a diagnosis verification module;

[0041] The information acquisition module includes a parameter receiving unit, an image generating unit and an initial bearing unit;

[0042] The parameter receiving unit is used to collect the current bearing shape parameters in real time through a data receiver, and receive the noise parameters and the light parameters in real time;

[0043] The image generation unit is used to automatically generate a picture of the bearing state captured by the camera through the image sensor;

[0044] The initial bearing unit is used to set data parameters of the initial state of the bearing, and the data parameters include standard temperature parameters of the bearing, standard roughness of the bearing surface, standard bonding parameters of the bearing, and standard color of the bearing material.

[0045] The fault update module is used to record and update the cause of the bearing failure in real time through a data recorder. The cause of the bearing failure includes fatigue shedding, increased surface roughness, gluing of the bearing, and corrosion of the bearing material;

[0046] The collaborative diagnosis module is used to collaboratively diagnose the cause of the bearing failure through noise parameters, light parameters and pictures. The collaborative diagnosis steps are as follows:

[0047] Step 1: Use a temperature sensor to detect the current bearing temperature in real time, and calculate the difference between the current bearing temperature and the standard bearing temperature parameter. If the difference is greater than 0 and less than 0.02, it indicates that the bearing is normal. If the difference is greater than 0.02, it indicates that the cause of the bearing failure is fatigue shedding.

[0048] Step 2: Similarly, through image recognition, the bearing surface roughness, bearing bonding parameters and bearing material color are compared in real time. If an abnormality occurs, it means that the bearing is the corresponding cause of the failure, and the bearing bonding parameters and bearing material color can be diagnosed collaboratively to comprehensively determine that the cause of the bearing failure is increased wear.

[0049] The diagnostic verification module includes a parameter verification unit and a verification warning unit;

[0050] The parameter verification unit is used to perform data verification in real time according to the diagnosed bearing fault cause, and calculate whether the difference between the parameter corresponding to the bearing fault cause and the standard parameter is equal to 0. If it is equal to 0, it verifies that the bearing fault diagnosis is normal; if it is not equal to 0, it verifies that the bearing fault diagnosis is abnormal;

[0051] The verification warning unit is used to report the abnormality of the bearing fault diagnosis to the reporting system to issue a warning reminder and feedback for manual processing.

[0052] The present invention has the following beneficial effects:

[0053] 1. In the present invention, by setting an impact judgment end, during computer vision bearing fault diagnosis based on data processing, by performing real-time difference calculation between noise parameters and illumination parameters and standard noise parameters and standard illumination parameters, it is timely determined whether these noises and illuminations affect the clarity and accuracy of the bearing fault diagnosis based on the differences. This not only enables real-time reception of effective data for the bearing fault diagnosis, but also enables real-time determination of whether the bearing fault diagnosis is affected by abnormalities.

[0054] 2. In the present invention, by setting an impact correction end, during computer vision bearing fault diagnosis based on data processing, when it is determined that noise and light affect the clarity and accuracy of the bearing fault diagnosis, the noise parameters are adjusted in real time according to the difference, and the lighting angle is adjusted in real time to prevent uneven lighting, and abnormalities occurring during the bearing diagnosis process can be corrected in a timely manner, thereby increasing the convenience of bearing diagnosis and the timeliness of abnormality correction.

[0055] 3. In the present invention, by setting a collaborative diagnosis terminal, during the computer vision bearing fault diagnosis based on data processing, when the noise parameters and illumination parameters in the bearing fault diagnosis process are determined to be normal, the collaborative comparison and collaborative optimization of the noise parameters and illumination parameters are combined to perform parameter verification in real time, so that the visually captured image information can be used to collaboratively diagnose the cause of the bearing fault, avoid deviations in the bearing fault diagnosis, and further ensure the accuracy of the bearing fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the overall system architecture of a computer vision bearing fault diagnosis system based on data processing according to the present invention;

[0057] Figure 2 It is a schematic diagram of the structure of the impact judgment end of the computer vision bearing fault diagnosis system based on data processing of the present invention;

[0058] Figure 3 It is a schematic diagram of the structure of the impact correction end of the computer vision bearing fault diagnosis system based on data processing of the present invention;

[0059] Figure 4 The present invention is a schematic diagram of the architecture of a collaborative diagnosis terminal of a computer vision bearing fault diagnosis system based on data processing. DETAILED DESCRIPTION

[0060] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0061] Embodiment 1

[0062] Please refer to Figure 1-Figure 2 As shown: A computer vision bearing fault diagnosis system based on data processing, including an impact judgment end, an impact correction end and a collaborative diagnosis end;

[0063] The impact judgment end, the impact correction end and the collaborative diagnosis end are jointly provided with a voice alarm unit;

[0064] The influence judgment end is used to detect the noise parameters and light parameters of the bearing fault diagnosis in real time, set the standard noise parameters and standard light parameters that will not affect the bearing fault diagnosis, calculate the difference between the noise parameters and light parameters and the standard noise parameters and standard light parameters in real time, and judge whether these noises and lights affect the bearing fault diagnosis in time according to the difference;

[0065] The impact correction end is used to receive noise and light parameters in real time and adjust the noise parameters and the light angle in real time according to the difference when it is determined that noise and light affect the clarity and accuracy of bearing fault diagnosis;

[0066] The collaborative diagnosis end is used to generate image information through the information captured by computer vision when it is determined that the noise parameters and illumination parameters in the process of bearing fault diagnosis are normal, and to perform collaborative diagnosis of bearing faults in real time by combining the collaborative comparison and collaborative optimization of noise parameters and illumination parameters;

[0067] The voice alarm unit is used to send out voice alarm signals in real time through the voice alarm when the system is abnormal, and feedback is given to manual processing.

[0068] The impact judgment end includes a regional monitoring module, a data detection module and an impact judgment module;

[0069] The area monitoring module includes a bearing area unit and an area monitoring unit;

[0070] The bearing area unit is used to set the visual capture area range of the bearing and set the camera to rotate the bearing at multiple angles through a timer.

[0071] The area monitoring unit is used to perform real-time monitoring of the bearing in the corresponding visual capture area through a camera to obtain an appearance image of the bearing.

[0072] The data detection module includes a bearing noise unit and an illumination parameter unit;

[0073] The bearing noise unit is used to calculate the current noise parameters in real time through the calculation formula. The calculation formula is as follows:

[0074]

[0075] Among them, MSN represents the noise parameter at the current moment, xi is the data observation value, is the true value of the data, n is the number of data points;

[0076] The illumination parameter unit is used to calculate the illumination parameters within the visual capture area in real time through a calculation formula. The calculation formula is as follows:

[0077]

[0078] Among them, Ii is the illumination parameter of the measuring point within the i-th visual capture area, n is the number of measuring points within the visual capture area, and the difference between the noise parameters and illumination parameters and the standard noise parameters and standard illumination parameters is calculated in real time. According to the difference, it is timely judged whether these noises and illuminations affect the clarity and accuracy of bearing fault diagnosis.

[0079] The impact judgment module includes a standard parameter unit, a noise detection unit, a light detection unit and an impact judgment unit;

[0080] The standard parameter unit is used to set standard noise parameters and standard illumination parameters;

[0081] The noise detection unit is used to calculate the difference between the noise parameter at the current moment and the standard noise parameter;

[0082] The illumination detection unit is used to determine whether the illumination within the visual capture area is uniform by using the uniformity ratio calculation formula. The calculation formula is as follows:

[0083]

[0084] Among them, Uniformity represents the uniformity ratio within the visual capture area. If the uniformity ratio is less than or equal to 1 and greater than or equal to 0.9, it means that the lighting is uniform. If the uniformity ratio is less than 0.9, it means that the lighting is uneven.

[0085] The impact judgment unit is used to judge that the bearing fault diagnosis is affected when there is a difference between the noise parameters and the standard noise parameters and the lighting is uneven, and to judge that the bearing fault diagnosis is not affected when there is no difference between the noise parameters and the standard noise parameters and the lighting is uniform. It can receive valid data for bearing fault diagnosis in real time, and can also judge in real time whether the bearing fault diagnosis is affected by abnormalities.

[0086] Embodiment 2

[0087] Please refer to Figure 3 As shown: Based on the first embodiment, the impact correction end includes an abnormal receiving module, a data correction module and a correction tracking module;

[0088] The abnormality receiving module is used to receive data abnormalities in real time through a data receiver.

[0089] The data correction module includes a difference calculation unit, a parameter sorting unit and a parameter correction unit;

[0090] The difference calculation unit is used for secondary calculation of the difference between the noise parameter and the standard noise parameter and between the illumination parameter and the standard illumination parameter;

[0091] The parameter sorting unit is used to arrange the calculated differences in order from small to large using Arabic numerals;

[0092] The parameter correction unit is used to correct the noise parameters and illumination parameters in real time according to the calculated difference. If the difference between the noise parameter and the standard noise parameter is a positive number, the noise parameter is reduced accordingly through a filter. If the difference between the noise parameter and the standard noise parameter is a negative number, the noise parameter is increased accordingly through a filter. If the illumination is uneven, the corresponding illumination intensity of Imin or Iavg is adjusted. When it is determined that the noise and illumination affect the clarity and accuracy of the bearing fault diagnosis, the noise and illumination parameters are received in real time, and the noise parameters are adjusted in real time according to the difference, and the illumination angle is adjusted in real time to prevent uneven illumination.

[0093] The correction tracking module includes a correction tracking unit and a tracking warning unit;

[0094] The correction tracking unit is used to recalculate the adjusted noise parameters and illumination parameters through a calculation formula, and perform difference calculation between the noise parameters and illumination parameters calculated once and the noise parameters and illumination parameters calculated twice. If the difference is equal to 0, it indicates that the correction is invalid, and if the difference is not equal to 0, it indicates that the correction is valid.

[0095] The tracking and early warning unit is used to report that the system sends out a voice alarm signal when it determines that the correction is ineffective, and feedback is sent to manual processing. After adjustment, the adjusted noise parameters and light parameters are detected in real time, so that abnormalities occurring during the bearing diagnosis process can be corrected in time, increasing the convenience of bearing diagnosis and the timeliness of abnormality correction.

[0096] Embodiment 3

[0097] Please refer to Figure 4 As shown: Based on the first embodiment, the collaborative diagnosis end includes an information acquisition module, a fault update module, a collaborative diagnosis module and a diagnosis verification module;

[0098] The information acquisition module includes a parameter receiving unit, an image generating unit and an initial bearing unit;

[0099] The parameter receiving unit is used to collect the current bearing shape parameters in real time through the data receiver, and receive the noise parameters and the light parameters in real time;

[0100] The image generation unit is used to automatically generate an image of the bearing state captured by the camera through an image sensor;

[0101] The initial bearing unit is used to set the data parameters of the initial state of the bearing. The data parameters include standard bearing temperature parameters, standard bearing surface roughness, standard bearing bonding parameters and standard bearing material color. When the noise parameters and lighting parameters in the bearing fault diagnosis process are determined to be normal, the information captured by computer vision is used to generate image information, and the collaborative comparison and collaborative optimization of noise parameters and lighting parameters are combined to perform collaborative diagnosis of bearing faults in real time.

[0102] The fault update module is used to record and update the causes of bearing failures in real time through a data recorder. The causes of bearing failures include fatigue shedding, increased surface roughness, bearing bonding, and corrosion of bearing materials.

[0103] The collaborative diagnosis module is used to collaboratively diagnose the cause of bearing failure through noise parameters, light parameters and pictures. The collaborative diagnosis steps are as follows:

[0104] Step 1: Use a temperature sensor to detect the current bearing temperature in real time, and calculate the difference between the current bearing temperature and the standard bearing temperature parameter. If the difference is greater than 0 and less than 0.02, it indicates that the bearing is normal. If the difference is greater than 0.02, it indicates that the cause of the bearing failure is fatigue shedding.

[0105] Step 2: Similarly, through image recognition, the bearing surface roughness, bearing bonding parameters and bearing material color are compared in real time. If an abnormality occurs, it means that the bearing is the corresponding cause of the failure, and the bearing bonding parameters and bearing material color can be diagnosed collaboratively to comprehensively determine that the cause of the bearing failure is increased wear.

[0106] The diagnostic verification module includes a parameter verification unit and a verification warning unit;

[0107] The parameter verification unit is used to perform data verification in real time according to the diagnosed bearing fault cause, and calculate whether the difference between the parameter corresponding to the bearing fault cause and the standard parameter is equal to 0. If it is equal to 0, it verifies that the bearing fault diagnosis is normal; if it is not equal to 0, it verifies that the bearing fault diagnosis is abnormal;

[0108] The verification and early warning unit is used to report abnormal bearing fault diagnosis, issue an early warning reminder to the reporting system, provide feedback for manual processing, and perform parameter verification in real time after the bearing fault diagnosis, so that the visually captured image information can help diagnose the cause of the bearing fault, avoid deviation in the bearing fault diagnosis, and further ensure the accuracy of the bearing fault diagnosis.

[0109] In the present invention, a computer vision bearing fault diagnosis system based on data processing is provided. When the system is in operation, the system first detects the noise parameters and illumination parameters of the bearing fault diagnosis in real time, sets standard noise parameters and standard illumination parameters that will not affect the bearing fault diagnosis, calculates the difference between the noise parameters and illumination parameters and the standard noise parameters and standard illumination parameters in real time, and judges in time whether these noises and illuminations affect the bearing fault diagnosis according to the difference. It can judge in time whether these noises and illuminations affect the clarity and accuracy of the bearing fault diagnosis, so that when the computer vision bearing fault diagnosis is performed, it can not only receive the valid data of the bearing fault diagnosis in real time, but also judge in real time whether the bearing fault diagnosis is affected by abnormalities; when it is determined that the noise and illumination affect the clarity and accuracy of the bearing fault diagnosis, the noise and illumination parameters are received in real time, and the noise parameters are adjusted in real time according to the difference, and the illumination angle is adjusted in real time; when it is determined that the noise and illumination affect the clarity and accuracy of the bearing fault diagnosis, the noise and illumination parameters are received in real time, and the noise parameters are adjusted in real time according to the difference, and the illumination angle is adjusted in real time. number, and adjust the noise parameters in real time according to the difference, and adjust the illumination angle in real time to prevent uneven illumination, and detect the adjusted noise parameters and illumination parameters in real time after the adjustment, so that the abnormalities in the bearing diagnosis process can be corrected in time, increasing the convenience of bearing diagnosis and the timeliness of abnormality correction; when it is determined that the noise parameters and illumination parameters in the bearing fault diagnosis process are normal, the information captured by computer vision is used to generate image information, and the coordinated comparison and coordinated optimization of the noise parameters and illumination parameters are combined to perform a collaborative diagnosis of the bearing fault in real time. When it is determined that the noise parameters and illumination parameters in the bearing fault diagnosis process are normal, the information captured by computer vision is used to generate image information, and the coordinated comparison and coordinated optimization of the noise parameters and illumination parameters are combined to perform a collaborative diagnosis of the bearing fault in real time. After the bearing fault diagnosis, parameter verification is performed in real time, so that the image information captured by vision can collaboratively diagnose the cause of the bearing fault, avoid deviation in the bearing fault diagnosis, and further ensure the accuracy of the bearing fault diagnosis.

[0110] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A computer vision bearing fault diagnosis system based on data processing, characterized in that: The computer vision bearing fault diagnosis system based on data processing includes an impact judgment end, an impact correction end and a collaborative diagnosis end; The impact judgment end, the impact correction end and the collaborative diagnosis end are jointly provided with a voice alarm unit; The impact judgment end is used to detect the noise parameters and light parameters of the bearing fault diagnosis in real time, set the standard noise parameters and standard light parameters that will not affect the bearing fault diagnosis, calculate the difference between the noise parameters and light parameters and the standard noise parameters and standard light parameters in real time, and judge whether these noises and lights affect the bearing fault diagnosis in time according to the difference; The impact correction end is used to receive noise and light parameters in real time, and adjust the noise parameters and the illumination angle in real time according to the difference when it is determined that the noise and light affect the clarity and accuracy of the bearing fault diagnosis; The collaborative diagnosis end is used to generate image information through information captured by computer vision when it is determined that the noise parameters and illumination parameters in the process of bearing fault diagnosis are normal, and to perform collaborative diagnosis of bearing faults in real time by combining collaborative comparison and collaborative optimization of noise parameters and illumination parameters; The voice alarm unit is used to send out a voice alarm signal in real time through a voice alarm when the system is abnormal, and feedback is given to manual processing.

2. The system according to claim 1, characterized in that The impact judgment end includes a region monitoring module, a data detection module and an impact judgment module; The area monitoring module includes a bearing area unit and an area monitoring unit; The bearing area unit is used to set the visual capture area range of the bearing, and set the camera to rotate the bearing at multiple angles through a timer; The area monitoring unit is used to perform real-time monitoring of the bearing in the corresponding visual capture area through a camera to obtain an appearance image of the bearing.

3. The system according to claim 2, characterized in that The data detection module includes a bearing noise unit and an illumination parameter unit; The bearing noise unit is used to calculate the noise parameters at the current moment in real time through a calculation formula, and the calculation formula is as follows: Among them, MSN represents the noise parameter at the current moment, xi is the data observation value, is the true value of the data, n is the number of data points; The illumination parameter unit is used to calculate the illumination parameters within the visual capture area in real time through a calculation formula, and the calculation formula is as follows: Where Ii is the illumination parameter of the measurement point within the i-th visual capture area, and n is the number of measurement points within the visual capture area.

4. The system according to claim 3, characterized in that The impact judgment module includes a standard parameter unit, a noise detection unit, an illumination detection unit and an impact judgment unit; The standard parameter unit is used to set standard noise parameters and standard illumination parameters; The noise detection unit is used to calculate the difference between the noise parameter at the current moment and the standard noise parameter; The illumination detection unit is used to determine whether the illumination within the visual capture area is uniform by using a uniformity ratio calculation formula, and the calculation formula is as follows: Among them, Uniformity represents the uniformity ratio within the visual capture area. If the uniformity ratio is less than or equal to 1 and greater than or equal to 0.9, it means that the lighting is uniform. If the uniformity ratio is less than 0.9, it means that the lighting is uneven. The impact judgment unit is used to judge that the bearing fault diagnosis is affected when there is a difference between the noise parameter and the standard noise parameter and the illumination is uneven, and to judge that the bearing fault diagnosis is not affected when there is no difference between the noise parameter and the standard noise parameter and the illumination is uniform.

5. The system according to claim 1, characterized in that The impact correction end includes an abnormal receiving module, a data correction module and a correction tracking module; The abnormality receiving module is used to receive data abnormalities in real time through a data receiver.

6. The system according to claim 5, characterized in that The data correction module includes a difference calculation unit, a parameter sorting unit and a parameter correction unit; The difference calculation unit is used for recalculating the difference between the noise parameter and the standard noise parameter and the difference between the illumination parameter and the standard illumination parameter; The parameter sorting unit is used to arrange the calculated differences in order, using Arabic numerals to sort them in ascending order; The parameter correction unit is used to correct the noise parameter and the illumination parameter in real time according to the calculated difference. If the difference between the noise parameter and the standard noise parameter is a positive number, the noise parameter is correspondingly reduced through a filter. If the difference between the noise parameter and the standard noise parameter is a negative number, the noise parameter is correspondingly increased through a filter. If the illumination is uneven, the corresponding illumination intensity Imin or Iavg is adjusted.

7. The system according to claim 6, characterized in that The correction tracking module includes a correction tracking unit and a tracking early warning unit; The correction tracking unit is used to recalculate the adjusted noise parameters and illumination parameters through a calculation formula, and perform difference calculation between the noise parameters and illumination parameters calculated once and the noise parameters and illumination parameters calculated twice. If the difference is equal to 0, it indicates that the correction is invalid, and if the difference is not equal to 0, it indicates that the correction is valid; The tracking and early warning unit is used to report that the system sends out a voice alarm signal and feedbacks manual processing when it is judged that the correction is invalid.

8. The system according to claim 1, characterized in that The collaborative diagnosis terminal includes an information acquisition module, a fault update module, a collaborative diagnosis module and a diagnosis verification module; The information acquisition module includes a parameter receiving unit, an image generating unit and an initial bearing unit; The parameter receiving unit is used to collect the current bearing shape parameters in real time through a data receiver, and receive the noise parameters and the light parameters in real time; The image generation unit is used to automatically generate a picture of the bearing state captured by the camera through the image sensor; The initial bearing unit is used to set data parameters of the initial state of the bearing, and the data parameters include standard temperature parameters of the bearing, standard roughness of the bearing surface, standard bonding parameters of the bearing, and standard color of the bearing material.

9. The system according to claim 8, characterized in that The fault update module is used to record and update the cause of the bearing failure in real time through a data recorder. The cause of the bearing failure includes fatigue shedding, increased surface roughness, gluing of the bearing, and corrosion of the bearing material; The collaborative diagnosis module is used to collaboratively diagnose the cause of the bearing failure through noise parameters, light parameters and pictures. The collaborative diagnosis steps are as follows: Step 1: Use a temperature sensor to detect the current bearing temperature in real time, and calculate the difference between the current bearing temperature and the standard bearing temperature parameter. If the difference is greater than 0 and less than 0.02, it indicates that the bearing is normal. If the difference is greater than 0.02, it indicates that the cause of the bearing failure is fatigue shedding. Step 2: Similarly, through image recognition, the bearing surface roughness, bearing bonding parameters and bearing material color are compared in real time. If an abnormality occurs, it means that the bearing is the corresponding cause of the failure, and the bearing bonding parameters and bearing material color can be diagnosed collaboratively to comprehensively determine that the cause of the bearing failure is increased wear.

10. The system according to claim 9, characterized in that The diagnostic verification module includes a parameter verification unit and a verification warning unit; The parameter verification unit is used to perform data verification in real time according to the diagnosed bearing fault cause, and calculate whether the difference between the parameter corresponding to the bearing fault cause and the standard parameter is equal to 0. If it is equal to 0, it verifies that the bearing fault diagnosis is normal; if it is not equal to 0, it verifies that the bearing fault diagnosis is abnormal; The verification warning unit is used to report the abnormality of the bearing fault diagnosis to the reporting system to issue a warning reminder and feedback for manual processing.

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