An industrial robot fault diagnosis and recovery system

Abstract

The present invention discloses an industrial robot fault diagnosis and recovery system, which relates to the field of industrial robot fault diagnosis and recovery, and includes a data acquisition module, a maintenance personnel registration module, a data analysis module, a fault classification module, and an early warning module. The present invention analyzes historical faults of industrial robots, constructs a set of fault indication values corresponding to different fault types, and monitors and analyzes relevant parameters during the operation of the industrial robot in real time. This solves the problem in the prior art that an alarm can only be issued when a fault occurs in the industrial robot and the source of the fault cannot be accurately located, thereby reducing the maintenance cost of the enterprise and improving the efficiency of the enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robot fault diagnosis and recovery, and in particular to an industrial robot fault diagnosis and recovery system. Background Art

[0002] With the continuous improvement of industrial automation, industrial robots are increasingly used in production lines. However, due to the long-term high-intensity work of robots, various failures are prone to occur, causing considerable trouble to the production line.

[0003] Existing fault diagnosis technologies for industrial robots are mostly based on threshold-based diagnosis. They can only identify that an industrial robot has failed, but cannot identify the specific fault type. This requires maintenance personnel to visit the site for further confirmation, which can cause huge losses to the company. Furthermore, existing technologies cannot find the right maintenance personnel based on the specific fault type, wasting manpower and resources.

[0004] Therefore, an industrial robot fault diagnosis and recovery system is introduced. Summary of the Invention

[0005] In view of this, the present invention provides an industrial robot fault diagnosis and recovery system to solve the problems raised by the above background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An industrial robot fault diagnosis and recovery system, comprising:

[0007] Data acquisition module: Extracts historical fault information of industrial robots, takes the time point of each robot failure as the starting point, and obtains abnormal data packets within a set time interval before each industrial robot failure;

[0008] The acquired abnormal data packets are classified according to different fault types, including motor abnormality, communication abnormality and mechanical abnormality; the three groups of abnormal data packets include motor abnormality data packets, communication abnormality data packets and mechanical abnormality data packets; the motor abnormality data packets, communication abnormality data packets and mechanical abnormality data packets are set to correspond to a group of data sets respectively;

[0009] Maintenance personnel registration module: used to collect maintenance personnel information; the information includes the distance between the maintenance personnel and the industrial robot, the maintenance personnel's repair success rate for each fault type, and the maintenance personnel's educational level; educational level types include high school and below, technical secondary school, junior college, and undergraduate;

[0010] Data analysis module: used to analyze each abnormal data packet corresponding to each fault type to obtain the fault indication value corresponding to each fault type; the fault indication value includes the motor fault indication value DJC, the communication fault indication value TXC, and the mechanical fault indication value JXC; and send the fault indication value to the fault classification module;

[0011] Fault classification module: used to store each group of fault indication values corresponding to the historical fault information of the industrial robot, and classify each group of fault indication values into three abnormal fault sets according to the fault type: the motor abnormality fault set corresponding to the motor abnormality, the communication abnormality fault set corresponding to the communication abnormality, and the mechanical abnormality fault set corresponding to the mechanical abnormality;

[0012] If the absolute value of the difference between a set of fault indication values within a set time interval during the operation of the industrial robot and a set of fault indication values in the corresponding abnormal fault set is less than a preset threshold, a warning signal of the fault type corresponding to the abnormal fault set is triggered; the warning signals include motor abnormality warning signals, communication abnormality warning signals, and mechanical abnormality warning signals;

[0013] Early warning module: executes corresponding steps based on the corresponding early warning signal received.

[0014] In some embodiments, the motor abnormality data packet, the communication abnormality data packet, and the mechanical abnormality data packet are respectively set to correspond to a set of data sets, specifically:

[0015] The data set of the motor abnormality data packet is the robot motor temperature value in the set time interval and the working environment temperature value of the industrial robot; the data set of the communication abnormality data packet is the communication rate value in the set time interval; the data set of the mechanical abnormality data packet is the image information of the industrial robot's mechanical arm and the mechanical arm acceleration value.

[0016] In some embodiments, the motor abnormality data packet corresponding to the motor abnormality fault type is analyzed, specifically:

[0017] S1: Get the motor temperature value at each time node in the set time interval, and draw it into a broken line graph according to the time series. Preset the motor temperature value threshold, construct the threshold line corresponding to the motor temperature threshold on the broken line graph, mark the closed area surrounded by the threshold line and the broken line graph, and statistically sum the areas of the closed areas above and below the threshold line. The obtained values are recorded as M1 and M2 respectively. Use the formula Get overworked than MJ;

[0018] S2: In the line graph, the sum of the time when the motor temperature value is above the threshold line is counted and recorded as the high-temperature working time. The high-temperature working time and the set time interval are calculated as the ratio, and the obtained value is recorded as the high-temperature working ratio MK;

[0019] S3: Obtain the ambient temperature values of the industrial robot at each time point in the set time interval, take their average as the ring average, preset the ring average threshold of the industrial robot, subtract the ring average threshold from the ring average to obtain the environmental index, preset each environmental index interval corresponding to each group of ambient temperature scores, match the environmental index interval corresponding to the environmental index, and thus obtain the ambient temperature score TR corresponding to the environmental index; the larger the ambient temperature score TR, the more suitable the ambient temperature for the industrial robot to operate;

[0020] S4: Normalize the excess work ratio MJ, high temperature work ratio MK, and ambient temperature score TR and enter them into the formula: The motor fault indication value DJC is obtained, where a1, a2, and a3 are the weighted influencing factors corresponding to the ambient temperature score TR, excess working ratio MJ, and high temperature working ratio MK, respectively.

[0021] In some embodiments, the communication abnormality data packet corresponding to the communication abnormality fault type is analyzed, specifically:

[0022] L1: Obtain the network delay data values at each time node in the set time interval, calculate the network delay mean value N1 using the mean formula, and extract the peak value of the network delay data value as the network peak value N2;

[0023] L2: Obtain the broadband utilization rate at each time point in the set time interval and calculate the broadband utilization rate at each time point in the set time interval using the mean formula to obtain the average broadband utilization rate KD1. Extract the maximum broadband utilization rate, preset a broadband utilization rate threshold, and subtract the broadband utilization rate threshold from the maximum broadband utilization rate to obtain the broadband overload value KD2.

[0024] L3: Extract the network delay mean threshold N1 from the preset database 阈值 , network peak threshold N2 阈值 , width average rate KD1 阈值 And broadband overload rate threshold KD2 阈值 , the obtained network delay mean N1 and network delay mean threshold N1 阈值 , Network peak value N2, Network peak threshold N2 阈值 , width average rate KD1, width average rate KD1 阈值 , broadband overload value KD2, broadband overload rate threshold KD2 阈值 After normalization, enter the formula The communication fault indication value TXC is obtained, where b1, b2, b3, and b4 are the weighted influence factors corresponding to the network delay mean N1, network peak N2, bandwidth average rate KD1, and broadband overload value KD2, respectively.

[0025] In some embodiments, the mechanical abnormality data packet corresponding to the mechanical abnormality fault type is analyzed, specifically:

[0026] D1: Acquire images of the industrial robot's robotic arm at a set time interval and preprocess the images, including enhancement and noise reduction.

[0027] D2: Divide the obtained robotic arm image into x sub-parts and number the robotic arm images of each sub-part, with the number represented by i, i = 1, 2...x; obtain the original image corresponding to each sub-part of the robotic arm from the database, compare the obtained robotic arm images of each sub-part with the corresponding original image, and calculate based on the perceptual hash algorithm to obtain the similarity between the robotic arm images of each sub-part and the corresponding original image, which is recorded as the similarity score XS i ;

[0028] D3: Obtain the acceleration value of the industrial robot manipulator at each time node in the set time interval, preset the acceleration threshold of the industrial robot manipulator, count the number of acceleration values exceeding the acceleration threshold in the set time interval, record it as the super value GF, calculate the acceleration data using the standard deviation formula to obtain the super value JL, and normalize the super value GF and super value JL and then enter them into the formula The acceleration index JSD is obtained, where v1 and v2 are the weighted influence factors corresponding to the super value GF and the wave value JL respectively;

[0029] D4: Extract the corresponding maintenance times of each sub-part of the industrial robot arm from the historical maintenance data of the industrial robot, and calculate the ratio of the maintenance times of each sub-part of the industrial robot to the total maintenance times to obtain the maintenance factor μ i , preset maintenance factors μ for each group i The interval corresponds to the maintenance influence weight of each group, and the maintenance factor μ i Match the preset maintenance factor intervals to obtain the maintenance factor μ i The corresponding maintenance impact weight is used as the similarity score XS i The weight influence factor λ i .

[0030] D5: The obtained similarity score XS i , after normalization of the acceleration index JSD and the weight influence factors corresponding to each part of the robot are substituted into the formula The mechanical fault indication value JXC is obtained, where dj is the weight influence factor corresponding to the acceleration index JSD.

[0031] In some embodiments, based on the received communication anomaly warning signal, corresponding steps are performed, specifically:

[0032] Obtain the difference between the motor fault indication value and the motor fault indication value threshold. Preset the intervals of the difference between the motor fault indication value and the motor fault indication value threshold to correspond to the motor danger level. The danger levels are divided into mild, general, and severe. Match the intervals corresponding to the difference between the motor fault indication value and the motor fault indication value threshold to obtain the motor danger level. Perform corresponding processing according to the different motor danger levels:

[0033] When the danger level is minor: different power ranges are preset based on the different danger levels corresponding to the motor cooling fan and the motor. Based on the power ranges corresponding to the minor danger levels preset for the motor cooling fan and the motor, the motor cooling fan power and the motor power are adjusted to the preset ranges;

[0034] When the danger level is normal: Based on the power ranges corresponding to the motor cooling fan and motor preset normal danger levels, the motor cooling fan power and motor power are adjusted to the preset ranges. The target position of the current target industrial robot is sent to the mobile terminal of the maintenance personnel closest to the target position, along with the motor danger level. The maintenance personnel then make a secondary judgment and analyze whether the machine needs to be stopped for maintenance or shut down for rest.

[0035] When the danger level is severe: the industrial robot is immediately shut down and the motor temperature is monitored in real time. When the motor temperature drops to a safe temperature range, the nearest cooling area is obtained and the industrial robot is automatically controlled to move to the nearest cooling area.

[0036] In some embodiments, based on the received communication anomaly warning signal, corresponding steps are performed, specifically:

[0037] Acquire image information at the communication interface and perform preprocessing. Use graphic processing technology to obtain the ratio of the dust area in the image to the total area of the interface, which is recorded as the dust impact degree. A dust impact degree threshold is preset. When the dust impact degree is greater than the preset dust impact degree threshold, immediately obtain the cleaning staff closest to the current industrial robot, and send the cleaning instructions and the location of the industrial robot to the cleaning staff's mobile terminal, and the cleaning staff will perform the cleaning operation;

[0038] When the dust impact is less than the preset dust impact threshold, a circle is drawn with the current position of the industrial robot as the center and the specified area as the radius. The distance between each maintenance personnel and the industrial robot, the repair success rate of communication failures, and the educational level of each maintenance personnel within the circle are obtained. The educational level score corresponding to each type of educational level is preset. The educational level of each maintenance personnel is matched with the corresponding educational level score to obtain the educational level score of each maintenance personnel.

[0039] The distance between each maintenance worker and the industrial robot, the repair success rate of communication failures, and the educational level score are marked as C1, C2, and C3 respectively;

[0040] The distance C1 from the industrial robot to each maintenance personnel within the circle, the repair success rate C2 of communication failures, and the educational background type C3 are normalized and entered into the formula: The comprehensive maintenance index Wt of each maintenance personnel is obtained, where w1, w2, and w3 are the weighted influencing factors corresponding to the distance C1 between the maintenance personnel and the industrial robot, the repair success rate C2 of communication failures, and the maintenance personnel's educational level score C3, respectively. The maintenance personnel with the largest comprehensive maintenance index Wt is selected as the target maintenance personnel. The industrial robot fault information and the industrial robot's location information are packaged and sent to the target maintenance personnel terminal, who then performs maintenance operations.

[0041] In some embodiments, based on the received mechanical abnormality warning signal, corresponding steps are executed, specifically:

[0042] Draw a circle with the current industrial robot as the center and the specified distance as the radius. Select the staff member with the highest comprehensive maintenance index within the circle, and send the fault information and the location of the industrial robot to their mobile terminal at the same time. The staff member will conduct inspection and maintenance and decide whether to replace the robotic arm based on the actual situation.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention analyzes historical fault information of industrial robots to obtain a set of fault indication values corresponding to each fault type. By setting detection nodes during the operation of the industrial robots, the fault data is analyzed in real time and calculated and matched with the corresponding set of fault indication values. This enables early warning of industrial robot faults and precise location of the fault source. This solves the problem in the prior art that alarms are triggered only when an industrial robot fails, and maintenance personnel are required to conduct a secondary analysis of the specific fault type, which wastes the company's time and costs.

[0045] The present invention solves the problems in a targeted manner by providing different solutions to different fault types, thereby solving the problems in the prior art of only using one solution or strategy for various faults, resulting in low problem handling efficiency and excessive processing time. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0047] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0048] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0049] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless explicitly defined as such herein.

[0050] See also Figure 1 As shown, an industrial robot fault diagnosis and recovery system includes: a data acquisition module, a maintenance personnel registration module, a data analysis module, a fault classification module, and an early warning module;

[0051] Data acquisition module: Extracts historical fault information of industrial robots, takes the time point of each robot failure as the starting point, and obtains abnormal data packets within a set time interval before each industrial robot failure;

[0052] The acquired abnormal data packets are classified according to different fault types, including motor abnormality, communication abnormality and mechanical abnormality; the three groups of abnormal data packets include motor abnormality data packets, communication abnormality data packets and mechanical abnormality data packets; the motor abnormality data packets, communication abnormality data packets and mechanical abnormality data packets are set to correspond to a group of data sets respectively;

[0053] Set the motor abnormality data packet, communication abnormality data packet and mechanical abnormality data packet to correspond to a set of data sets respectively, specifically:

[0054] The data set of the motor abnormality data packet is the robot motor temperature value and the working environment temperature value of the industrial robot in the set time interval; the data set of the communication abnormality data packet is the communication rate value in the set time interval; the data set of the mechanical abnormality data packet is the image information of the industrial robot manipulator and the manipulator acceleration value;

[0055] Maintenance personnel registration module: used to collect information corresponding to maintenance personnel; the information includes the distance between the maintenance personnel and the industrial robot, the maintenance personnel's repair success rate for each fault type, and the maintenance personnel's educational level;

[0056] Data analysis module: used to analyze each abnormal data packet corresponding to each fault type to obtain the fault indication value corresponding to each fault type; the fault indication value includes the motor fault indication value DJC, the communication fault indication value TXC, and the mechanical fault indication value JXC; and send the fault indication value to the fault classification module;

[0057] The motor abnormality data packet corresponding to the motor abnormality fault type is analyzed to obtain the motor fault indication value DJC, which is specifically:

[0058] S1: Get the motor temperature value at each time node in the set time interval, and draw it into a broken line graph according to the time series. Preset the motor temperature value threshold, construct the threshold line corresponding to the motor temperature threshold on the broken line graph, mark the closed area surrounded by the threshold line and the broken line graph, and statistically sum the areas of the closed areas above and below the threshold line. The obtained values are recorded as M1 and M2 respectively. Use the formula Get overworked than MJ;

[0059] The area above the line graph and the area enclosed by the line graph can be simply understood as the high-temperature working time, while the area below the line graph and the area enclosed by the line graph can be simply understood as the low-temperature working time. Therefore, the larger the MJ value, the longer the high-temperature time of the industrial robot motor.

[0060] S2: In the line graph, the sum of the time when the motor temperature value is above the threshold line is counted and recorded as the high-temperature working time. The high-temperature working time and the set time interval are calculated as the ratio, and the obtained value is recorded as the high-temperature working ratio MK;

[0061] The high temperature working ratio MK can reflect the proportion of high temperature working time to the total time. The larger the MK value, the more likely the motor is to fail.

[0062] S3: Obtain the ambient temperature values of the industrial robot at each time point in the set time interval, take their average as the ring average, preset the ring average threshold of the industrial robot, subtract the ring average threshold from the ring average to obtain the environmental index, preset each environmental index interval corresponding to each group of ambient temperature scores, match the environmental index interval corresponding to the environmental index, and thus obtain the ambient temperature score TR corresponding to the environmental index; the larger the ambient temperature score TR, the more suitable the ambient temperature for the industrial robot to operate;

[0063] The environmental index may have negative values, but points can still be assigned to negative values. For example, when the environmental index is 0, the ambient temperature score is 10 points, and when the environmental index is -1, the ambient temperature score is 11 points.

[0064] S4: Normalize the excess work ratio MJ, high temperature work ratio MK, and ambient temperature score TR and enter them into the formula: The motor fault indication value DJC is obtained, where a1, a2, and a3 are the weighted influencing factors corresponding to the ambient temperature score TR, excess working ratio MJ, and high temperature working ratio MK, respectively.

[0065] The larger the calculated motor fault indication value DJC is, the greater the probability of motor failure is;

[0066] The communication abnormality data packet corresponding to the communication abnormality fault type is analyzed to obtain the communication fault indication value TXC, which is specifically:

[0067] L1: Obtain the network delay data values at each time node in the set time interval, calculate the network delay mean value N1 using the mean formula, and extract the peak value of the network delay data value as the network peak value N2;

[0068] The mean network delay N1 reflects the average level of network delay in a set time interval, while the peak network delay N2 reflects the extreme network delay in a set time interval. The smaller the values of both, the better.

[0069] L2: Obtain the broadband utilization rate at each time point in the set time interval and calculate the broadband utilization rate at each time point in the set time interval using the mean formula to obtain the average broadband utilization rate KD1. Extract the maximum broadband utilization rate, preset a broadband utilization rate threshold, and subtract the broadband utilization rate threshold from the maximum broadband utilization rate to obtain the broadband overload value KD2.

[0070] Broadband speed is limited. The higher the bandwidth utilization, the lower the bandwidth speed allocated to the industrial robot. The average bandwidth rate KD1 represents the average level of bandwidth utilization over a set time interval, while the bandwidth overload value KD2 represents the extreme bandwidth load level. The higher the bandwidth utilization rate, the worse the performance.

[0071] L3: Extract the network delay mean threshold N1 from the preset database阈值 , network peak threshold N2 阈值 , width average rate KD1 阈值 And broadband overload rate threshold KD2 阈值 , the obtained network delay mean N1 and network delay mean threshold N1 阈值 , Network peak value N2, Network peak threshold N2 阈值 , width average rate KD1, width average rate KD1 阈值 , broadband overload value KD2, broadband overload rate threshold KD2 阈值 After normalization, enter the formula Get the communication fault indication value TXC, where b1, b2, b3, and b4 are the weighted impact factors corresponding to the network delay mean N1, network peak N2, bandwidth average rate KD1, and broadband overload value KD2 respectively;

[0072] By calculating the ratio of the obtained data to the respective set thresholds and then multiplying them by their respective weighted influence factors, the different effects of four different factors on the communication fault indicator value TXC are scientifically considered, so that the communication fault indicator value TXC can more accurately reflect whether the industrial robot communication is stable;

[0073] The mechanical abnormality data packet corresponding to the mechanical abnormality fault type is analyzed to obtain the mechanical abnormality indication value JXC, which is specifically:

[0074] D1: Acquire images of the industrial robot's robotic arm at a set time interval and preprocess the images, including enhancement and noise reduction.

[0075] D2: Divide the obtained robotic arm image into x sub-parts and number the robotic arm images of each sub-part, with the number represented by i, i = 1, 2...x; obtain the original image corresponding to each sub-part of the robotic arm from the database, compare the obtained robotic arm images of each sub-part with the corresponding original image, and calculate based on the perceptual hash algorithm to obtain the similarity between the robotic arm images of each sub-part and the corresponding original image, which is recorded as the similarity score XS i ;

[0076] Similarity Score XS i The higher it is, the less wear and damage there is on the corresponding part of the robotic arm;

[0077] D3: Obtain the acceleration value of the industrial robot manipulator at each time node in the set time interval, preset the acceleration threshold of the industrial robot manipulator, count the number of acceleration values exceeding the acceleration threshold in the set time interval, record it as the super value GF, calculate the acceleration data using the standard deviation formula to obtain the super value JL, and normalize the super value GF and super value JL and then enter them into the formula The acceleration index JSD is obtained, where v1 and v2 are the weighted influence factors corresponding to the super value GF and the wave value JL respectively;

[0078] D4: Extract the corresponding maintenance times of each sub-part of the industrial robot arm from the historical maintenance data of the industrial robot, and calculate the ratio of the maintenance times of each sub-part of the industrial robot to the total maintenance times to obtain the maintenance factor μ i , preset maintenance factors μ for each group i The interval corresponds to the maintenance influence weight of each group, and the maintenance factor μ i Match the preset maintenance factor intervals to obtain the maintenance factor μ i The corresponding maintenance impact weight is used as the similarity score XS i The weight influence factor λ i ;

[0079] Maintenance factor μ i The larger the corresponding interval value is, the smaller the corresponding maintenance impact weight is;

[0080] D5: The obtained similarity score XS i , after normalization of the acceleration index JSD and the weight influence factors corresponding to each part of the robot are substituted into the formula The mechanical fault indication value JXC is obtained, where dj is the weighted influence factor corresponding to the acceleration index JSD;

[0081] Fault classification module: used to store each group of fault indication values corresponding to the historical fault information of the industrial robot, and classify each group of fault indication values into three abnormal fault sets according to the fault type: the motor abnormality fault set corresponding to the motor abnormality, the communication abnormality fault set corresponding to the communication abnormality, and the mechanical abnormality fault set corresponding to the mechanical abnormality;

[0082] If the absolute value of the difference between a set of fault indication values within a set time interval during the operation of the industrial robot and a set of fault indication values in the corresponding abnormal fault set is less than a preset threshold, a warning signal of the fault type corresponding to the abnormal fault set is triggered; the warning signals include motor abnormality warning signals, communication abnormality warning signals, and mechanical abnormality warning signals;

[0083] For different fault types, the threshold of the absolute value of the difference between the corresponding fault indication value and the data in the corresponding abnormal fault set should be set to different values. The specific value can be adjusted by the operator according to the actual situation;

[0084] Early warning module: executes corresponding steps based on the corresponding early warning signal received;

[0085] When the received result is a motor abnormality warning signal: obtaining the difference between the motor fault indication value and the motor fault indication value threshold, presetting intervals of the difference between the motor fault indication value and the motor fault indication value threshold to correspond to each motor danger level, and the danger levels are divided into mild, general and severe; matching the intervals corresponding to the difference between the motor fault indication value and the motor fault indication value threshold to obtain the motor danger level;

[0086] When the danger level is minor: different power ranges are preset based on the different danger levels corresponding to the motor cooling fan and the motor. Based on the power ranges corresponding to the minor danger levels preset for the motor cooling fan and the motor, the motor cooling fan power and the motor power are adjusted to the preset ranges;

[0087] When the danger level is normal: Based on the power ranges corresponding to the motor cooling fan and motor preset normal danger levels, the motor cooling fan power and motor power are adjusted to the preset ranges. The target position of the current target industrial robot is sent to the mobile terminal of the maintenance personnel closest to the target position, along with the motor danger level. The maintenance personnel then make a secondary judgment and analyze whether the machine needs to be stopped for maintenance or shut down for rest.

[0088] If the danger level is severe: the industrial robot is immediately shut down and the motor temperature is monitored in real time. When the motor temperature drops to a safe temperature range, the nearest cooling area is obtained and the industrial robot is automatically directed to the nearest cooling area.

[0089] When the received result is a communication abnormality warning signal: immediately obtain the image information at the communication interface and perform preprocessing, use graphic processing technology to obtain the ratio of the dust area in the image to the total area of the interface, record it as the dust impact degree, and preset a dust impact degree threshold. When the dust impact degree is greater than the preset dust impact degree threshold, immediately obtain the cleaning staff closest to the current industrial robot, and send the cleaning instructions and the location of the industrial robot to the cleaning staff's mobile terminal, and the cleaning staff will perform the cleaning operation;

[0090] It should be noted that due to the conductivity and micro-capacitance of dust, dust at the power interface can affect the communication strength of the industrial robot. The greater the dust impact, the stronger the impact and the lower the communication strength.

[0091] When the dust impact is less than the preset dust impact threshold, a circle is drawn with the current position of the industrial robot as the center and the specified area as the radius. The distance between each maintenance personnel and the industrial robot, the repair success rate of communication failures, and the educational level of each maintenance personnel within the circle are obtained. The educational level score corresponding to each type of educational level is preset. The educational level of each maintenance personnel is matched with the corresponding educational level score to obtain the educational level score of each maintenance personnel.

[0092] The educational level score can be assigned 1 to 4 points according to high school, technical secondary school, junior college, and undergraduate degrees respectively; the educational level of maintenance personnel is matched with the preset educational level score to obtain the educational level score of each maintenance personnel;

[0093] The distance between each maintenance worker and the industrial robot, the repair success rate of communication failures, and the educational level score are marked as C1, C2, and C3 respectively;

[0094] The distance C1 from the industrial robot to each maintenance personnel within the circle, the repair success rate C2 of communication failures, and the educational background type C3 are normalized and entered into the formula: The comprehensive maintenance index Wt of each maintenance personnel is obtained, where w1, w2, and w3 are the weighted influencing factors corresponding to the distance C1 between the maintenance personnel and the industrial robot, the repair success rate C2 of communication failures, and the maintenance personnel's educational level score C3, respectively. The maintenance personnel with the largest comprehensive maintenance index Wt is selected as the target maintenance personnel. The industrial robot fault information and the industrial robot's location information are packaged and sent to the target maintenance personnel terminal, who then performs maintenance operations.

[0095] When the received result is a mechanical abnormality warning signal: draw a circle with the current industrial robot as the center and the specified distance as the radius, select the staff member with the highest comprehensive maintenance index within the circle, and send the fault information and the location of the industrial robot to their mobile terminal at the same time. The staff member will conduct inspection and maintenance, and determine whether the robotic arm needs to be replaced based on the actual situation.

[0096] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An industrial robot fault diagnosis and recovery system, characterized in that: include: Data acquisition module: Extracts historical fault information of industrial robots, takes the time point of each robot failure as the starting point, and obtains abnormal data packets within a set time interval before each industrial robot failure; The acquired abnormal data packets are classified according to different fault types, including motor abnormality, communication abnormality and mechanical abnormality; the three groups of abnormal data packets include motor abnormality data packets, communication abnormality data packets and mechanical abnormality data packets; the motor abnormality data packets, communication abnormality data packets and mechanical abnormality data packets are set to correspond to a group of data sets respectively; The data set of the motor abnormality data packet is the robot motor temperature value in the set time interval and the working environment temperature value of the industrial robot; Maintenance personnel registration module: used to collect maintenance personnel information; the information includes the distance between the maintenance personnel and the industrial robot, the maintenance personnel's repair success rate for each fault type, and the maintenance personnel's educational level; educational level types include high school and below, technical secondary school, junior college, and undergraduate; Data analysis module: used to analyze each abnormal data packet corresponding to each fault type to obtain the fault indication value corresponding to each fault type; the fault indication value includes the motor fault indication value DJC, the communication fault indication value TXC, and the mechanical fault indication value JXC; and send the fault indication value to the fault classification module; The motor abnormality data packet corresponding to the motor abnormality fault type is analyzed as follows: S1: Get the motor temperature value at each time node in the set time interval, and draw it into a broken line graph according to the time series. Preset the motor temperature value threshold, construct the threshold line corresponding to the motor temperature threshold on the broken line graph, mark the closed area surrounded by the threshold line and the broken line graph, and statistically sum the areas of the closed areas above and below the threshold line. The obtained values are recorded as M1 and M2 respectively. Use the formula , got overworked than MJ; S2: In the line graph, the sum of the time when the motor temperature value is above the threshold line is counted and recorded as the high-temperature working time. The high-temperature working time and the set time interval are calculated as the ratio, and the obtained value is recorded as the high-temperature working ratio MK; S3: Obtain the ambient temperature values of the industrial robot at each time point in the set time interval, take the average as the ring mean, preset the ring mean threshold of the industrial robot, subtract the ring mean threshold from the ring mean to obtain the environmental index, preset each environmental index interval corresponding to each group of ambient temperature scores, match the environmental index interval corresponding to the environmental index, and thus obtain the ambient temperature score TR corresponding to the environmental index; S4: Normalize the excess work ratio MJ, high temperature work ratio MK, and ambient temperature score TR and enter them into the formula: The motor fault indication value DJC is obtained, where a1, a2, and a3 are the weighted influence factors corresponding to the ambient temperature score TR, excess duty ratio MJ, and high temperature duty ratio MK, respectively; Fault classification module: used to store each group of fault indication values corresponding to the historical fault information of the industrial robot, and classify each group of fault indication values into three abnormal fault sets according to the fault type: the motor abnormality fault set corresponding to the motor abnormality, the communication abnormality fault set corresponding to the communication abnormality, and the mechanical abnormality fault set corresponding to the mechanical abnormality; If the absolute value of the difference between a set of fault indication values within a set time interval during the operation of the industrial robot and a set of fault indication values in the corresponding abnormal fault set is less than a preset threshold, a warning signal of the fault type corresponding to the abnormal fault set is triggered; The early warning signals include motor abnormality early warning signals, communication abnormality early warning signals and mechanical abnormality early warning signals; Early warning module: executes corresponding steps based on the corresponding early warning signal received.

2. The industrial robot fault diagnosis and recovery system according to claim 1, characterized in that: Set the communication abnormal data packet and the mechanical abnormal data packet to correspond to a set of data sets, specifically: The data set of the communication abnormality data packet is the communication rate value in the set time interval; the data set of the mechanical abnormality data packet is the image information and acceleration value of the industrial robot arm.

3. The industrial robot fault diagnosis and recovery system according to claim 2, characterized in that: Analyze the abnormal communication data packets corresponding to the communication fault type, specifically: L1: Obtain the network delay data values at each time node in the set time interval, calculate the network delay mean value N1 using the mean formula, and extract the peak value of the network delay data value as the network peak value N2; L2: Obtain the broadband utilization rate at each time point in the set time interval and calculate the broadband utilization rate at each time point in the set time interval using the mean formula to obtain the average broadband utilization rate KD1. Extract the maximum broadband utilization rate, preset a broadband utilization rate threshold, and subtract the broadband utilization rate threshold from the maximum broadband utilization rate to obtain the broadband overload value KD2. L3: Extract the average network delay threshold from the preset database , network peak threshold , average width and broadband overload rate threshold , the obtained network delay mean N1 and network delay mean threshold , Network peak value N2, Network peak value threshold , width average rate KD1, width average rate , broadband overload value KD2, broadband overload rate threshold After normalization, enter the formula The communication fault indication value TXC is obtained, where b1, b2, b3, and b4 are the weighted influence factors corresponding to the network delay mean N1, network peak N2, bandwidth average rate KD1, and broadband overload value KD2, respectively.

4. The industrial robot fault diagnosis and recovery system according to claim 3, characterized in that: Analyze the mechanical abnormality data packets corresponding to the mechanical abnormality fault type, specifically: D1: Acquire the image of the industrial robot's robotic arm at a set time interval and preprocess the image of the industrial robot's robotic arm; D2: Divide the obtained robotic arm image into x sub-parts and number the robotic arm images of each sub-part, with the number represented by i, i=1,2...x; obtain the original image corresponding to each sub-part of the robotic arm from the database, compare the obtained robotic arm images of each sub-part with the corresponding original image, and calculate based on the perceptual hash algorithm to obtain the similarity between the robotic arm images of each sub-part and the corresponding original image, which is recorded as the similarity score ; D3: Obtain the acceleration value of the industrial robot manipulator at each time node in the set time interval, preset the acceleration threshold of the industrial robot manipulator, count the number of acceleration values exceeding the acceleration threshold in the set time interval, record it as the super value GF, calculate the acceleration data using the standard deviation formula to obtain the super value JL, and normalize the super value GF and super value JL and then enter them into the formula The acceleration index JSD is obtained, where v1 and v2 are the weighted influence factors corresponding to the super value GF and the wave value JL respectively; D4: Extract the corresponding maintenance times of each sub-part of the industrial robot arm from the historical maintenance data of the industrial robot, and calculate the ratio of the maintenance times of each sub-part of the industrial robot to the total maintenance times to obtain the maintenance factor. , preset maintenance factors for each group The interval corresponds to the maintenance influence weight of each group, and the maintenance factor Match the preset maintenance factor intervals to obtain the maintenance factor The corresponding maintenance impact weight, the maintenance impact weight of each sub-part of the robot arm is used as the similarity score The weighted impact factor ; D5: The similarity score obtained , after normalization of the acceleration index JSD and the weight influence factors corresponding to each part of the robot are substituted into the formula The mechanical fault indication value JXC is obtained, where dj is the weight influence factor corresponding to the acceleration index JSD.

5. The industrial robot fault diagnosis and recovery system according to claim 4, characterized in that: Based on the received motor abnormality warning signal, the corresponding steps are executed, specifically: Obtaining the difference between the motor fault indication value and the motor fault indication value threshold, and presetting intervals of the difference between the motor fault indication value and the motor fault indication value threshold to correspond to the danger levels of the motors, where the danger levels are divided into minor, general, and severe; Matching the interval corresponding to the difference between the motor fault indication value and the motor fault indication value threshold value, thereby obtaining the motor hazard level; When the danger level is minor, different power intervals are preset based on the different danger levels corresponding to the motor cooling fan and the motor. Based on the power intervals corresponding to the minor danger levels preset for the motor cooling fan and the motor, the motor cooling fan power and the motor power are adjusted to the preset intervals; When the danger level is normal, the motor cooling fan power and motor power are adjusted to the preset range based on the power range corresponding to the normal danger level preset for the motor cooling fan and the motor. The target position of the current target industrial robot is sent to the mobile terminal of the maintenance personnel closest to the target position, and the motor danger level is packaged and sent together. The staff will make a secondary judgment and analysis on whether it is necessary to stop the machine for maintenance or shut down for rest. When the danger level is serious, the industrial robot is immediately shut down and the motor temperature is monitored in real time. When the motor temperature is detected to drop to a safe temperature range, the cooling area closest to the current industrial robot is obtained and the industrial robot is controlled to automatically move to the nearest cooling area.

6. The industrial robot fault diagnosis and recovery system according to claim 5, characterized in that: Based on the received communication anomaly warning signal, execute the corresponding steps, specifically: Acquire image information at the communication interface and perform preprocessing. Use graphic processing technology to obtain the ratio of the dust area in the image to the total area of the interface, which is recorded as the dust impact degree. A dust impact degree threshold is preset. When the dust impact degree is greater than the preset dust impact degree threshold, immediately obtain the cleaning staff closest to the current industrial robot, and send the cleaning instructions and the location of the industrial robot to the cleaning staff's mobile terminal, and the cleaning staff will perform the cleaning operation; When the dust impact is less than the preset dust impact threshold, a circle is drawn with the current position of the industrial robot as the center and the specified area as the radius. The distance between each maintenance personnel and the industrial robot, the repair success rate of communication failures, and the educational level of each maintenance personnel within the circle are obtained. The educational level score corresponding to each type of educational level is preset. The educational level of each maintenance personnel is matched with the corresponding educational level score to obtain the educational level score of each maintenance personnel. The distance between each maintenance worker and the industrial robot, the repair success rate of communication failures, and the educational level score are marked as C1, C2, and C3 respectively; The distance C1 of each maintenance personnel from the industrial robot, the repair success rate C2 of communication failure, and the academic qualification score C3 within the circle are normalized and entered into the formula: The comprehensive maintenance index Wt of each maintenance personnel is obtained, where w1, w2, and w3 are the weighted influencing factors corresponding to the distance C1 between the maintenance personnel and the industrial robot, the repair success rate C2 of communication failures, and the maintenance personnel's educational level score C3, respectively. The maintenance personnel with the largest comprehensive maintenance index Wt is selected as the target maintenance personnel. The industrial robot fault information and the industrial robot's location information are packaged and sent to the target maintenance personnel terminal, who then performs maintenance operations.

7. The industrial robot fault diagnosis and recovery system according to claim 6, characterized in that: Based on the received mechanical abnormality warning signal, the corresponding steps are executed, specifically: Draw a circle with the current industrial robot as the center and the specified distance as the radius. Select the staff member with the highest comprehensive maintenance index within the circle, and send the fault information and the location of the industrial robot to their mobile terminal at the same time. The staff member will conduct inspection and maintenance and decide whether to replace the robotic arm based on the actual situation.

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