Industrial robot fault analysis system based on the Internet

Abstract

The present invention discloses an industrial robot fault analysis system based on the Internet, wherein a controller diagnosis module comprises a dynamic monitoring unit for the industrial robot; the dynamic detection unit calculates the operation data of the industrial robot to obtain the fault point and fault parameters of the industrial robot, and obtains the dumping time of the industrial robot based on the fault parameters, wherein the dumping time is the time for troubleshooting during the operation of the industrial robot; the fault parameters are sent to a diagnosis processor, and the diagnosis processor performs diagnosis and analysis. The present invention stores a fault case library based on the Internet in the diagnosis processor, matches the fault diagnosis data of the industrial robot with the fault case library by the controller diagnosis module, and preferably obtains a suitable fault case, so that the technician can quickly find the fault point of the robot and handle it based on the fault case, thereby improving the efficiency of industrial robot fault handling.

Description

Technical Field

[0001] The present invention relates to the field of robot technology, and in particular to an industrial robot fault analysis system based on the Internet. Background Art

[0002] With the rapid development of the Internet, more and more intelligent devices are replacing humans to complete various complex operations. Robots are one of the typical examples of such intelligent devices. Especially in the application scenarios of intelligent warehousing, robots are required to transport and process tasks within the warehouse. Only when robots operate efficiently and safely can warehouse backlogs be effectively avoided.

[0003] However, robot failures are inevitable during long-term operation, so it is particularly important to analyze industrial robot failures in a timely and accurate manner. Existing industrial robot failure analysis solutions are not accurate enough, contain a lot of misleading information, require a high level of experience from technicians, and have a slow maintenance speed for industrial robots. Summary of the invention

[0004] The object of the present invention is to provide an Internet-based industrial robot fault analysis system, in which a controller diagnostic module is used to monitor data during the operation of an industrial robot and send the data of the industrial robot to a diagnostic processor; the diagnostic processor is used to receive the data of the industrial robot sent by the controller diagnostic module, and match corresponding fault cases in the diagnostic processor based on the data of the industrial robot. When a fault case matching the industrial robot data is stored in the diagnostic processor, the fault case matching the industrial robot data is used as a target case, and the diagnostic information of the target case is sent to a terminal device to facilitate technical personnel to handle the faults of the industrial robot.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An Internet-based industrial robot fault analysis system, including a controller diagnostic module for the robot;

[0007] The controller diagnostic module includes a dynamic monitoring unit for industrial robots;

[0008] The dynamic detection unit calculates the operation data of the industrial robot to obtain the fault point and fault parameters of the industrial robot, and obtains the dumping time of the industrial robot based on the fault parameters, the dumping time being the fault elimination time during the operation of the industrial robot;

[0009] sending the fault parameters to a diagnostic processor, which performs diagnostic analysis;

[0010] The troubleshooting time is sent to the terminal device, allowing the technician to complete the fault diagnosis within the troubleshooting time.

[0011] As a further solution of the present invention: the dynamic monitoring unit includes a pressure sensor, a height sensor, a first acceleration sensor and a second acceleration sensor. There are four pressure sensors, and the four pressure sensors are respectively arranged on the four machine feet of the industrial robot. A first speed sensor is arranged on the left front foot of the industrial robot, and a second speed sensor is arranged on the left rear foot of the industrial robot. There are two height sensors, one height sensor is arranged between the two front machine feet of the industrial robot, and the other height sensor is arranged between the two rear machine feet of the industrial robot.

[0012] As a further solution of the present invention: the specific detection steps of the dynamic monitoring unit for the operation data of the industrial robot are as follows:

[0013] Step 1: The pressure sensor is used to monitor the pressure information of the industrial robot in real time, obtain the pressure information values obtained by the four machine feet of the industrial robot in the same walking cycle, delete the maximum value and / or minimum value in this group of data, calculate the average pressure value of the remaining data in this group of data, and mark it as Fi, i = 1,..., n;

[0014] Step 2: Compare the average pressure value during the operation of the industrial robot in Step 1 with the preset pressure value uploaded by the terminal device, and perform a difference calculation to obtain the pressure difference Pi. Within the time of the same cycle, if Pi < Ki, where Ki is the preset pressure threshold, it is determined that the industrial robot is moving forward normally;

[0015] If Pi ≥ Ki, judge the operating state of the industrial robot;

[0016] Step 3: When judging the operating state of the industrial robot, the first speed sensor is used to obtain the first speed of the left front foot of the industrial robot in real time and mark it as Vi1, and the second speed sensor is used to obtain the second speed of the left rear foot of the industrial robot in real time and mark it as Vi2. Compare the difference between the first speed Vi1 and the second speed Vi2;

[0017] If Vi1 - Vi2 ≠ 0, it is determined that the industrial robot has a walking fault and there is a risk of tipping over;

[0018] Step 4: When it is determined that the industrial robot has a walking fault, within the same walking cycle, the height sensor between the two front machine feet of the industrial robot is used to obtain the front end height value in real time and mark it as Hi1, and the height sensor between the two rear machine feet of the industrial robot is used to obtain the rear end height value in real time and mark it as Hi2;

[0019] Step 5: Calculate the difference between the front height value H11 and the rear height value H12 in the same walking cycle of the industrial robot, that is, Z1 = |H11-H12|;

[0020] And take the difference between the front height value H21 and the rear height value H22 in the adjacent walking cycle, that is, Z2 = |H21-H22|;

[0021] Compare the difference between Z1 and Z2, and calculate the height difference Ch between two adjacent industrial robots during walking according to the formula Ch=|Z1-Z2|;

[0022] Step 6: Set the difference between the critical height of the industrial robot tipping over during walking and the height of the industrial robot in normal state as D, according to the formula The dumping time of the industrial robot is calculated.

[0023] As a further solution of the present invention: in step 4, the front end height value Hi1 and the rear end height value Hi2 are compared for difference;

[0024] If Hi1-Hi2>0, the rear robot foot of the industrial robot fails;

[0025] If Hi1-Hi2<0, the front mechanical foot of the industrial robot fails.

[0026] As a further solution of the present invention: the controller diagnosis module also includes a data acquisition unit based on the diagnosis result of the industrial robot, and the data acquisition unit collects operating parameters of the industrial robot.

[0027] As a further solution of the present invention: the operating parameters of the industrial robot include the amount of power stored in the industrial robot, the total operating cycle of the industrial robot and the number of failures of the industrial robot;

[0028] The data acquisition unit processes the data in the following steps:

[0029] S1: Obtain the power storage of the industrial robot and mark the power storage of the industrial robot as Xi;

[0030] S2: Obtain the total operation cycle of the industrial robot and mark the total operation cycle of the industrial robot as Ni;

[0031] S3: Obtain the number of failures of the industrial robot and mark the number of failures of the industrial robot as Mi;

[0032] S4: Obtain the operating environment parameters of the industrial robot through the formula Ji = β × (Xi × d1 + Ni × d2 + Mi × d3) × e 1.1564, where d1, d2 and d3 are all preset proportional coefficients, and d1>d2>d3, and e is a natural constant;

[0033] S5: Compare the industrial robot operating environment parameter Ji with the normal reference threshold of the industrial robot;

[0034] If the operating environment parameters of the industrial robot are less than the reference threshold for normal operation of the industrial robot, the operating state of the industrial robot is not ideal, and abnormal information is generated and fed back to the terminal device;

[0035] If the operating environment parameters of the industrial robot are higher than the reference threshold value for normal operation of the industrial robot, the operating status of the industrial robot is normal.

[0036] As a further solution of the present invention: the diagnostic processor stores an industrial robot fault case library, the diagnostic processor receives diagnostic data of the industrial robot from the diagnostic device, and matches fault cases in the diagnostic processor according to the diagnostic data, generates early warning information from the fault cases and feeds back to the terminal device for fault analysis.

[0037] The beneficial effects of the present invention are as follows: the present invention monitors the dynamic data of the industrial robot in real time during operation through the controller diagnostic module, processes the pressure value of the industrial robot in advance during operation, analyzes the travel status of the industrial robot, and simultaneously performs difference processing on the speed of the front and rear feet during the operation of the industrial robot to obtain the fault point of the industrial robot, and calculates the tipping time of the industrial robot by processing the difference in adjacent walking cycles of the industrial robot, so that maintenance personnel can troubleshoot the industrial robot before the industrial robot tips over. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Figure 1 It is a structural schematic diagram of the principle diagram of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] See also Figure 1 As shown, the present invention is an Internet-based industrial robot fault analysis system, a controller diagnostic module, a diagnostic processor and a terminal device;

[0042] The controller diagnosis module is used to monitor the data during the operation of the industrial robot and send the data of the industrial robot to the diagnosis processor;

[0043] The diagnosis processor is used to receive the data of the industrial robot sent by the controller diagnosis module, match the appropriate fault cases in the diagnosis processor according to the data of the industrial robot. When there is a fault case in the diagnosis processor that matches the data of the industrial robot, the fault case adapted to the data of the industrial robot is used as the target case, and the diagnosis information of the target case is sent to the terminal device;

[0044] The terminal device is used for fault diagnosis of the industrial robot by analyzing the diagnosis information.

[0045] The controller diagnosis module includes a dynamic monitoring unit for the industrial robot. The dynamic monitoring unit is used to monitor the dynamic data of the industrial robot in real time during operation;

[0046] Among them, the dynamic monitoring unit includes a pressure sensor, a height sensor, a first acceleration sensor and a second acceleration sensor. There are four pressure sensors, and the four pressure sensors are respectively arranged on the four machine soles of the industrial robot. A first speed sensor is arranged on the left front foot of the industrial robot, and a second speed sensor is arranged on the left rear foot of the industrial robot. There are two height sensors. One height sensor is arranged between the two front machine feet of the industrial robot, and the other height sensor is arranged between the two rear machine feet of the industrial robot. The specific detection steps of the dynamic monitoring unit for the operation data of the industrial robot are as follows:

[0047] Step 1: The pressure sensor is used to monitor the pressure information of the industrial robot in real time, obtain the pressure information values obtained by the four machine feet of the industrial robot in the same walking cycle, delete the maximum value and / or the minimum value in this group of data, calculate the average pressure value of the remaining data in this group of data, and mark it as Fi, i = 1,..., n;

[0048] Step 2: Compare the average pressure value during the operation of the industrial robot in Step 1 with the preset pressure value uploaded by the terminal device, and calculate the difference to obtain the pressure difference Pi. Within the time of the same cycle, if Pi < Ki, where Ki is the preset pressure threshold, it is determined that the industrial robot is moving forward normally;

[0049] If Pi ≥ Ki, judge the operating state of the industrial robot;

[0050] Step 3: When judging the running state of the industrial robot, the first speed sensor is used to obtain the first speed of the left front foot of the industrial robot in real time and marked as Vi1, and the second speed sensor is used to obtain the second speed of the left rear foot of the industrial robot in real time and marked as Vi2, and the difference between the first speed Vi1 and the second speed Vi2 is compared;

[0051] If Vi1-Vi2≠0, it is determined that the industrial robot has a walking failure and there is a risk of tipping over;

[0052] Step 4: When it is determined that the industrial robot has a walking failure, in the same walking cycle, the front height value is obtained in real time through the height sensor between the two front machine feet of the industrial robot and marked as Hi1, and the rear height value is obtained in real time through the height sensor between the two rear machine feet of the industrial robot and marked as Hi2, and the difference between the front height value Hi1 and the rear height value Hi2 is compared;

[0053] If Hi1-Hi2>0, the rear robot foot of the industrial robot fails;

[0054] If Hi1-Hi2<0, the front robot foot of the industrial robot fails;

[0055] Step 5: Calculate the difference between the front height value H11 and the rear height value H12 in the same walking cycle of the industrial robot, that is, Z1 = |H11-H12|;

[0056] And take the difference between the front height value H21 and the rear height value H22 in the adjacent walking cycle, that is, Z2 = |H21-H22|;

[0057] Compare the difference between Z1 and Z2, and calculate the height difference Ch between two adjacent industrial robots during walking according to the formula Ch=|Z1-Z2|;

[0058] Step 6: Set the difference between the critical height of the industrial robot tipping over during walking and the height of the industrial robot in normal state as D, according to the formula The dumping time of the industrial robot is calculated.

[0059] The controller diagnosis module also includes a data acquisition unit based on the diagnosis result of the industrial robot, and the data acquisition unit collects the operating parameters of the industrial robot, including the power storage capacity of the industrial robot, the total operating cycle of the industrial robot and the number of faults of the industrial robot;

[0060] The data acquisition unit processes the data in the following steps:

[0061] S1: Obtain the power storage of the industrial robot and mark the power storage of the industrial robot as Xi;

[0062] S2: Obtain the total operation cycle of the industrial robot and mark the total operation cycle of the industrial robot as Ni;

[0063] S3: Obtain the number of failures of the industrial robot and mark the number of failures of the industrial robot as Mi;

[0064] S4: Obtain the operating environment parameters of the industrial robot through the formula Ji = β × (Xi × d1 + Ni × d2 + Mi × d3) × e 1.1564 , where d1, d2 and d3 are all preset proportional coefficients, and d1>d2>d3, and e is a natural constant;

[0065] S5: Compare the industrial robot operating environment parameter Ji with the normal reference threshold of the industrial robot;

[0066] If the operating environment parameters of the industrial robot are less than the reference threshold for normal operation of the industrial robot, the operating state of the industrial robot is not ideal, and abnormal information is generated and fed back to the terminal device;

[0067] If the operating environment parameters of the industrial robot are higher than the reference threshold value for normal operation of the industrial robot, the operating status of the industrial robot is normal.

[0068] The diagnostic processor stores an industrial robot fault case library, which includes all public fault cases based on the Internet;

[0069] The diagnostic processor receives the diagnostic data of the industrial robot from the diagnostic device, and matches the corresponding fault case in the diagnostic processor according to the diagnostic data. When the match is consistent, the fault case generates warning information and feeds it back to the terminal device to facilitate the user to perform fault analysis;

[0070] Among them, the fault case warning information includes the date when the fault case was made public, the searchable URL and the diagnostic record, etc.

[0071] Among them, when the fault case library of the diagnostic processor cannot match the diagnostic data of the industrial robot, an early warning message is also generated, the early warning message is sent to the terminal device, and the fault diagnosis data of the industrial robot is stored in the fault case library.

[0072] The terminal device receives the early warning information transmitted by the diagnostic processor and performs fault processing on the industrial robot within a time range before the industrial robot falls over.

[0073] One of the core points of the present invention: the present invention monitors the dynamic data of the industrial robot in the operation process in real time through the controller diagnosis module, processes the pressure value of the industrial robot in the operation process in advance, judges the travel state of the industrial robot, and synchronously processes the difference of the speed of the front and rear feet of the industrial robot in the operation process to obtain the fault point of the industrial robot, and calculates the dumping time of the industrial robot by processing the difference in adjacent walking cycles of the industrial robot, so that the maintenance personnel can troubleshoot the industrial robot before the industrial robot dumps;

[0074] The second core point of the present invention is that the present invention stores a fault case library based on the Internet in the diagnostic processor, matches the fault diagnosis data of the industrial robot with the fault case library by the controller diagnostic module, and optimally obtains suitable fault cases, so that technicians can quickly find the fault point of the robot and handle it based on the fault case, thereby improving the efficiency of industrial robot fault handling.

[0075] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The Internet-based industrial robot fault analysis system is characterized by: It includes a controller diagnosis module for a robot; The controller diagnosis module includes a dynamic monitoring unit for an industrial robot; The dynamic detection unit calculates the operation data of the industrial robot to obtain the fault points and fault parameters of the industrial robot, and obtains the tipping time of the industrial robot based on the fault parameters. The tipping time is the troubleshooting time during the operation of the industrial robot; Send the fault parameters to the diagnosis processor, and the diagnosis processor performs diagnostic analysis; Send the troubleshooting time to the terminal device so that technicians can complete fault diagnosis within the troubleshooting time; The specific detection steps of the dynamic monitoring unit for the operation data of the industrial robot are as follows: Step 1: Use a pressure sensor to monitor the pressure information of the industrial robot in real time, obtain the pressure information values obtained by the four robot feet of the industrial robot in the same walking cycle, and delete the maximum and / or minimum values in the pressure information values obtained by the four robot feet in the same walking cycle. Calculate the average pressure value of the remaining data and mark it as Fi, i = 1,..., n; Step 2: Compare the average pressure value during the operation of the industrial robot in Step 1 with the preset pressure value uploaded by the terminal device, and perform a difference calculation to obtain the pressure difference Pi. Within the time of the same cycle, if Pi < Ki, where Ki is the preset pressure threshold, it is determined that the industrial robot is moving forward normally; If Pi ≥ Ki, judge the operating state of the industrial robot; Step 3: When judging the operating state of the industrial robot, use the first speed sensor to obtain the first speed of the left front foot of the industrial robot in real time and mark it as Vi1, and use the second speed sensor to obtain the second speed of the left rear foot of the industrial robot in real time and mark it as Vi2. Compare the difference between the first speed Vi1 and the second speed Vi2; If Vi1 - Vi2 ≠ 0, it is determined that the industrial robot has a walking fault and there is a risk of tipping; Step 4: When it is determined that the industrial robot has a walking fault, within the same walking cycle, use the height sensor between the two front robot feet of the industrial robot to obtain the front-end height value in real time and mark it as Hi1, and use the height sensor between the two rear robot feet of the industrial robot to obtain the rear-end height value in real time and mark it as Hi2; Step 5: Calculate the difference between the front-end height value Hi1 and the rear-end height value Hi2 within the same walking cycle of the industrial robot, that is, Z1 = |Hi1 - Hi2|; And take the difference between the front-end height value H21 and the rear-end height value H22 in adjacent walking cycles, that is, Z2 = |H21 - H22|; Compare the difference between Z1 and Z2, and calculate the height difference Ch between the adjacent two industrial robot walking processes according to the formula Ch = |Z1 - Z2|; Step 6: Set the difference between the critical height of the industrial robot tipping over during walking and the height of the industrial robot in normal state as D, according to the formula The dumping time of the industrial robot is calculated.

2. The Internet-based industrial robot fault analysis system according to claim 1, characterized in that: The dynamic monitoring unit includes a pressure sensor, a height sensor, a first acceleration sensor and a second acceleration sensor. Four pressure sensors are provided, and the four pressure sensors are respectively arranged on the bottom surfaces of the four machine feet of the industrial robot, and a first speed sensor is arranged on the left front foot of the industrial robot, and a second speed sensor is arranged on the left rear foot of the industrial robot. Two height sensors are provided, one height sensor is arranged between the two front machine feet of the industrial robot, and the other height sensor is arranged between the two rear machine feet of the industrial robot.

3. The Internet-based industrial robot fault analysis system according to claim 1, characterized in that: In step 4, the difference between the front-end height value Hi1 and the rear-end height value Hi2 is compared; If Hi1-Hi2>0, the rear robot foot of the industrial robot fails; If Hi1-Hi2<0, the front robot foot of the industrial robot fails.

4. The Internet-based industrial robot fault analysis system according to claim 1, characterized in that: The controller diagnosis module also includes a data acquisition unit based on the diagnosis result of the industrial robot, and the data acquisition unit collects the operating parameters of the industrial robot.

5. The Internet-based industrial robot fault analysis system according to claim 4, characterized in that: The operating parameters of industrial robots include the amount of electricity stored in the industrial robot, the total operating cycle of the industrial robot, and the number of failures of the industrial robot; The data acquisition unit processes the data in the following steps: S1: Obtain the power storage of the industrial robot and mark the power storage of the industrial robot as Xi; S2: Obtain the total operation cycle of the industrial robot and mark the total operation cycle of the industrial robot as Ni; S3: Get the number of failures of the industrial robot and mark the number of failures of the industrial robot as Mi; S4: Obtaining the operating environment parameters of industrial robots through formulas , where d1, d2 and d3 are all preset proportional coefficients, and d1>d2>d3, and e is a natural constant; S5: Compare the industrial robot operating environment parameter Ji with the normal reference threshold of the industrial robot; If the operating environment parameters of the industrial robot are less than the reference threshold for normal operation of the industrial robot, the operating state of the industrial robot is not ideal, and abnormal information is generated and fed back to the terminal device; If the operating environment parameters of the industrial robot are higher than the reference threshold value for normal operation of the industrial robot, the operating status of the industrial robot is normal.

6. The Internet-based industrial robot fault analysis system according to claim 1, characterized in that: The diagnostic processor stores an industrial robot fault case library. The diagnostic processor receives diagnostic data of the industrial robot from the diagnostic device, matches fault cases in the diagnostic processor according to the diagnostic data, generates early warning information from the fault cases and feeds back to the terminal device for fault analysis.

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