An industrial robot intelligent control and operation and maintenance method and system based on an MES system

Abstract

The application discloses an industrial robot intelligent control and operation and maintenance method and system based on an MES system, relates to the technical field of industrial automation and intelligent manufacturing, and comprises an MES system main body, a robot intelligent detection module, a robot PLC control module, an execution module, a prewarning module and a data interaction module. The MES system main body is internally provided with a robot health analysis unit and a dynamic regulation and control unit, health index calculation, fault mode matching and health grade division are carried out through real-time acquisition and processing of robot operation data; regulation and control rules are dynamically matched in combination with production plans, regulation and control instructions are generated and sent to a robot controller, and intelligent adjustment of robot motion parameters is realized. The application realizes deep integration of the MES system and the industrial robot, has the abilities of online state evaluation, intelligent regulation and control, predictive maintenance and collaborative operation and maintenance, significantly improves equipment operation reliability and production collaborative efficiency, is compatible with mainstream robot brands, and is convenient for popularization and application in intelligent manufacturing production lines.

Description

Technical Field

[0001] This invention relates to the field of industrial automation and intelligent manufacturing technology. More specifically, this invention relates to an intelligent control and operation and maintenance method and system for industrial robots based on a MES system, suitable for real-time status monitoring, health assessment, dynamic regulation, and predictive maintenance of industrial robots. Background Technology

[0002] Industrial robots, as core equipment in intelligent manufacturing, are widely used in processes such as welding, assembly, handling, and painting. With the increasing automation of production lines, the number of industrial robots is constantly increasing, and their operational reliability directly affects production efficiency and product quality. However, current industrial robot maintenance mainly relies on periodic upkeep or post-failure repair, which presents the following problems: Disconnect between status monitoring and production management: Industrial robot control systems (such as robot controllers) usually operate independently from the upper-level MES (Manufacturing Execution System). The robot's operating status cannot be fed back to the production scheduling layer in real time, which means that the production plan cannot be dynamically adjusted according to the health status of the equipment.

[0003] Lack of predictive maintenance capabilities: The degradation process of critical robot components (such as reducers, servo motors, and controllers) is difficult to detect online, often resulting in sudden failures and prolonged downtime. Traditional maintenance methods cannot predict the remaining lifespan based on real-time data.

[0004] Lagging control response: When the robot malfunctions (such as joint overheating or increased vibration), it cannot automatically adjust motion parameters (speed, acceleration, trajectory) to slow down the development of the fault, and lacks closed-loop intelligent control methods.

[0005] Data silos: Robot operation data and alarm records are not effectively integrated with MES and ERP systems, failing to provide data support for enterprise equipment management and production optimization.

[0006] Therefore, how to combine the production management capabilities of the MES system with the real-time control of industrial robots to achieve online assessment of robot status, intelligent regulation and collaborative operation and maintenance has become a technical problem that urgently needs to be solved in the current field of industrial robot applications. Summary of the Invention

[0007] One object of the present invention is to solve at least the above-mentioned problems and / or defects, and to provide at least the advantages described below.

[0008] To achieve these objectives and other advantages of the present invention, an intelligent control and maintenance method for industrial robots based on a MES system is provided, comprising: S1. The robot intelligent detection module collects the operating data of the industrial robot in real time, and uploads it to the main body of the MES system after filtering. The robot health analysis unit of the S2 and MES systems performs feature extraction, health index calculation, fault mode matching, and health level classification on the operating data, and generates a health assessment report. S3. If the health level is normal or slightly abnormal, the dynamic control unit matches the control rules, calculates the target motion parameters, generates control instructions, and proceeds to S4 based on the health assessment report and the current production plan; otherwise, proceeds to S5. S4. The robot PLC control module receives the control command, modifies the target motion parameters of the robot through the robot controller, and then proceeds to S6. S5. If the health level is moderately abnormal or severely abnormal, the early warning module will trigger an early warning and push information, and then proceed to S6. S6, the data interaction module synchronizes robot operation data, control records, and early warning logs to the enterprise ERP system; The health index includes: the overall health index of the robot that makes system-level decisions. HI robot To locate the joint health index of the faulty joint HI joint ; The overall health index HI robot It is characterized by the following formula: In the above formula, n is the total number of joints. For the first j The health index of each joint. β j For the first j The weight of each joint, and ; The health index of each joint HI joint It is characterized by the following formula: In the above formula, w 1. w 2. w 3. w 4 represents the weight of each sub-item scoring function. f vib For vibration scoring, f T Rate the temperature. f I Rate the current. f P For hydraulic pressure rating.

[0009] Preferably, in S2, the extracted features include: synthetic vibrations. RMS xyz Vibration peak value peak vibration kurtosis Kurt Joint temperature T j Temperature change rate dT j / dt Motor current I RMS Current distortion rate THD Hydraulic P Oil pressure fluctuation P std .

[0010] Preferably, vibration scoring f vib It is characterized by the following formula: In the above formula, min is the function for finding the minimum value. V limit The preset effective vibration value safety threshold, α Peak impact factor, 0≤ α ≤1; Temperature rating f T It is characterized by the following formula: Current rating f I It is characterized by the following formula: In the above formula, I rated This is the effective value of the rated current of the robot drive motor. THD limit The preset total harmonic distortion rate (THD) threshold for the current. Oil pressure rating f P It is characterized by the following formula: In the above formula, P normal The rated oil pressure for the lubrication system of industrial robots. P std_limit This is the preset safety threshold for the standard deviation of oil pressure.

[0011] Preferably, in S2, the fault mode matching is achieved through cosine similarity matching as follows: In the above formula, sim kLet cosine similarity be the current fault feature vector and the k-th fault mode feature vector. F now This is the fault feature vector obtained through current acquisition and processing. F k Let be the feature vector of the k-th standard fault mode in the fault mode library, and ||·|| be the Euclidean norm of the vector; when sim k > θ When determining the current joint matching number... k There are several failure modes, among which θ This is the preset matching threshold.

[0012] Preferably, in S3, the rule base of the dynamic control unit includes: rule ID, condition, action, and priority, and its control rules are as follows: The condition for rule ID R01 is: all joints are at normal health level and the task priority is high; the corresponding action matching is: each joint maintains its original movement speed; the corresponding priority is set to: 3. The condition for R02 in rule ID is: when the health level of any joint is slightly abnormal and the temperature of the corresponding joint is greater than 60℃; the corresponding action matching is: adjust the speed of the corresponding joint to 80% of the original speed and the acceleration to 70% of the original acceleration; the corresponding priority is set to: 2. The condition for rule ID R03 is: when the health level of any joint is moderately abnormal; the corresponding action matching is: if there is a backup robot, switch the task to the backup robot, otherwise reduce the speed to 50%; the corresponding priority is set to: 1. The condition for rule ID R04 is: when the health level of any joint is severely abnormal; the corresponding action matching is: then immediately stop the robot and lock it; the corresponding priority is set to: 0. The condition for rule ID R05 is: when a collision feature is detected; the corresponding action is: immediately stop and back up 10mm; the corresponding priority is set to: 0. Among them, when matching the control rules, the rules that meet the conditions are selected based on the current health level of each joint and the priority of the global production plan, and the rules with the highest priority are executed. If multiple rules have the same priority, the action with the least impact on the device will be selected.

[0013] An intelligent control and maintenance system for industrial robots, comprising: The main body of the MES system used for production planning management and equipment collaborative scheduling; A robot intelligent detection module for collecting robot operation data is installed on the industrial robot body and controller. The robot intelligent detection module includes: sensor I for collecting joint vibration, sensor II for collecting motor current, sensor III for collecting joint temperature and controller temperature, and sensor VI for collecting reducer lubricating oil pressure. A robot PLC control module that communicates with the robot controller to receive control commands and modify robot motion parameters; The execution module for responding to control commands includes: robot servo drivers, brakes, and lubrication solenoid valves; An early warning module used to issue warning signals based on health levels; This data interaction module is used for encrypted data transmission between modules and synchronization with the enterprise ERP system, supporting industrial Ethernet and wireless networks. The main body of the MES system is bidirectionally connected to the robot intelligent detection module, the robot PLC control module, and the early warning module through the data interaction module. The robot's PLC control module and execution module are unidirectionally electrically connected. The robot intelligent detection module is bidirectionally connected to the industrial robot to collect robot operation data in real time and feed it back to the main body of the MES system.

[0014] Preferably, the main body of the MES system incorporates a robot health analysis unit and a dynamic control unit; The robot health analysis unit includes: A data preprocessing subunit used to filter and normalize raw sensor data; A feature extraction subunit used to extract characteristic quantities reflecting the robot's health status; A health index calculation subunit that calculates the overall health index of the robot and its individual joints; Fault mode matching subunit for identifying potential fault types; The robot's health level is divided into sub-units based on its health index.

[0015] Preferably, the dynamic control unit includes: Stores a rule base containing multiple rules for controlling industrial robots; A rule matching subunit that matches control rules based on the robot's health level, production task priority, and current working status; The target parameter calculation sub-unit calculates the robot target motion parameters according to the matching rules; The target motion parameters are encapsulated into control commands and sent to the robot PLC control module via the data interaction module.

[0016] The present invention has at least the following beneficial effects: Firstly, this invention achieves deep integration of the MES system and industrial robots, constructing a complete closed loop from data acquisition, health assessment, intelligent control to operation and maintenance early warning, thereby improving the reliability of robot operation and production collaboration.

[0017] Secondly, by conducting online health assessments of key robot components (joints, motors, and reducers), this invention can detect potential faults in advance, enabling predictive maintenance and reducing unplanned downtime.

[0018] Third, the dynamic control unit of the present invention can automatically adjust motion parameters according to the robot's health status, delay the deterioration of the fault, ensure continuous production, and avoid safety accidents caused by sudden faults.

[0019] Fourth, this invention, through its modular system design, is compatible with mainstream industrial robot brands (such as FANUC, KUKA, ABB, Yaskawa, etc.), making it easy to deploy and integrate in existing automated production lines.

[0020] Fifth, this invention achieves data synchronization with the ERP system through a data interaction module, providing data support for equipment lifecycle management and production optimization.

[0021] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0022] Figure 1 This is a block diagram of the overall structure of the system of the present invention; Figure 2 This is a structural block diagram of the robot intelligent detection module of the present invention; Figure 3 This is a block diagram of the internal structure of the main body of the MES system of the present invention; Figure 4 This is a block diagram of the internal structure of the early warning module of the present invention; Figure 5 This is a flowchart of the robot health analysis unit algorithm of the present invention; The MES system consists of the following components: main body-1, robot intelligent detection module-2, robot PLC control module-3, execution module-4, early warning module-5, data interaction module-6, industrial robot-7, edge sensor-8, signal filtering unit-9, robot health analysis unit-10, dynamic control unit-11, graded early warning unit-12, and information push unit-13. Detailed Implementation

[0023] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0024] An intelligent control and operation and maintenance system for industrial robots based on a MES system is mainly used to realize real-time monitoring, health assessment, dynamic regulation and collaborative operation and maintenance of industrial robots. It mainly includes the following aspects: I. System Overall Framework like Figure 1 As shown, the present invention provides an intelligent control and maintenance system for industrial robots based on a Manufacturing Execution System (MES), comprising an MES system main body 1, a robot intelligent detection module 2, a robot PLC control module 3, an execution module 4, an early warning module 5, and a data interaction module 6. The MES system main body 1 is bidirectionally connected to the robot intelligent detection module 2, the robot PLC control module 3, and the early warning module 5 via the data interaction module 6; the robot PLC control module 3 is unidirectionally electrically connected to the execution module 4; the robot intelligent detection module 2 is bidirectionally signal connected to the industrial robot 7 (including the robot controller) for real-time acquisition of robot operation data and feedback to the MES system main body 1.

[0025] The data interaction module 6 supports dual-mode transmission of industrial Ethernet (such as Profinet, EtherCAT) and wireless networks (Wi-Fi, 5G), has a built-in hardware encryption chip, uses the AES-256 encryption algorithm to ensure communication security, and achieves data synchronization with the upper-level ERP system.

[0026] II. Detailed Implementation of the Robot Intelligent Detection Module like Figure 2 As shown, the robot intelligent detection module 2 includes multiple edge sensors and a signal filtering unit 9. Edge sensors 8 are installed at key locations on the industrial robot 7: vibration sensors (triaxial accelerometers) and temperature sensors are installed at each joint; current sensors are installed at the motor driver; and oil pressure sensors are installed in the reducer lubrication circuit. The sensor selection in this embodiment is as follows: Vibration sensor: PCB 356A16 triaxial ICP accelerometer, range ±50g, frequency response 0.5Hz~5kHz.

[0027] Temperature sensor: PT100 surface mount platinum resistance thermometer, measuring range -50℃~200℃, accuracy ±0.1℃.

[0028] Current sensor: LEM CKSR 6-NP closed-loop Hall current sensor, range 0~50A, bandwidth >100kHz.

[0029] Oil pressure sensor: MIK-P300 diffused silicon pressure sensor, range 0~5MPa.

[0030] The signal filtering unit 220 employs hardware low-pass filtering (cutoff frequency 1kHz) combined with software moving average filtering (window N=5) to reduce ambient noise. Filtered data is uploaded at 10ms intervals.

[0031] III. Internal Units of the MES System

[0032] like Figure 3 As shown, the main body 1 of the MES system has a built-in robot health analysis unit 10 and a dynamic control unit 11: (1) The robot health analysis unit is used to process the data collected by the intelligent detection module and generate a health assessment report by combining the robot's historical fault records; (2) The dynamic control unit is used to send control instructions to the robot PLC control module according to the health assessment report and the current production plan.

[0033] 3.1 Robot Health Analysis Unit The robot health analysis unit 10 analyzes the uploaded data and generates a health assessment report, including: ① Data preprocessing subunit, used to filter and normalize the raw sensor data; ② Feature extraction subunit, used to extract feature quantities reflecting the robot's health status, including joint vibration RMS, vibration peak value, vibration kurtosis, joint temperature, temperature change rate, motor current RMS, current distortion rate, lubricating oil pressure, oil pressure fluctuation, etc. ③ The health index calculation subunit uses a weighted multi-index fusion method to calculate the overall robot health index HI_robot and the health index HI_joint of each joint; ④ Fault mode matching subunit: Matches the current feature vector with the fault modes in the historical fault database to identify potential fault types, including gearbox wear, motor bearing failure, poor lubrication, controller overheating, etc. ⑤ Health level classification sub-unit: The robot's health level is classified according to the health index, including four levels: normal, mildly abnormal, moderately abnormal, and severely abnormal.

[0034] The processing flow of the robot health analysis unit is as follows: Figure 5 As shown, it includes the following sub-steps: (1) Data preprocessing Raw data set for each joint D ( t Normalize, where: The definitions of each component are as follows: v x (t ), v y ( t ), v z ( t () represents the instantaneous vibration velocity of the joint in the x, y, and z directions (unit: mm / s); T j ( t ( ) represents the instantaneous temperature inside the joint (unit: °C); I m ( t (A) represents the instantaneous current of the drive motor. P ( t () represents the instantaneous oil pressure value of the joint lubrication system (unit: MPa).

[0035] Normalization is performed using the min-max method, with the following formula: (1) In the above formula, x norm The normalized dimensionless value, ranging from [0,1]; x For a certain component in the original data (e.g.) v x ( t )); x min and x max These are the minimum and maximum values ​​of the component as specified in the robot manual or in historical statistics, respectively.

[0036] According to typical industrial robot technical manuals, the reference ranges for each physical quantity are as follows: ① Vibration RMS 0~20mm / s; ② Joint temperature: 0~80℃; ③ Motor current 0~30A; ④ Oil pressure 0~4MPa.

[0037] The normalized data is mainly used as input for subsequent machine learning models (such as fault classifiers), while fault scoring and cosine similarity matching are calculated based on the original physical quantities to ensure that the physical meaning of the scoring function is clear.

[0038] (2) Feature extraction The following features were extracted from the original data sequence of each joint (e.g., all sampling points within a 1-second time window), and are summarized in Table 1.

[0039] Table 1: Summary Table of Feature Extraction Example 1: Taking joint 1 as an example: Assuming 1000 sets of raw data are collected within 1 second, the first step is to start with the raw vibration velocity. v x ( t ), v y ( t ), v z ( t Calculate the RMS value of each component, and then combine them to obtain the final RMS value. xyz Simultaneously from the same set of raw current data I m ( t )calculate I rms and THD From raw oil pressure data P ( t Calculate the current value and standard deviation. P std Normalization uses the same original data source, but its results are only used for non-scoring modules (such as clustering or neural network inputs).

[0040] (3) Health Index Calculation Calculate the health index of each joint of the robot separately HI joint and overall health index HI robot The overall health index is a weighted average of the health indices of each joint: Where n is the total number of joints. For the first j The health index of each joint. β j For the first j The importance weight of each joint (e.g., the base joint has a higher weight, the wrist joint has a lower weight), and Overall health index HI robot Used for system-level decision-making, when HI robot When the value falls below a preset threshold (e.g., 0.7), a full-machine overhaul warning is triggered; the health index of each joint is used to locate the faulty joint.

[0041] The health index of a single joint is calculated using a weighted summation model: (2) In the above formula, w 1.w 2. w 3. w 4 represents the weight of each sub-item scoring function. f vib For vibration scoring, f T Rate the temperature. f I Rate the current. f P For oil pressure scoring, the weights are determined using the AHP method, and the sum of all weights is 1. Example values: w 1 = 0.4 w 2 = 0.3, w 3 = 0.2 w 4 = 0.1.

[0042] Scoring functions for each item: 1) Vibration score: (3) In the above formula, V limit The preset effective vibration value safety threshold, and V limit =15 mm / s ; α It is the peak impact factor, and α =0.2.

[0043] 2) Temperature rating: (4) Temperature change rate penalty: if dT j / dt >2℃ / min, then f T = f T ×0.9.

[0044] 3) Current rating: (5) In the above formula, I rated This is the effective value of the rated current of the robot drive motor, and I rated =25 A , THD limit The preset total harmonic distortion rate (THD) threshold is used, and THD limit =10%.

[0045] 4) Hydraulic pressure rating: (6) In the above formula, P normal The rated oil pressure for the lubrication system of industrial robots, and P normal =3.5 MPa , P std_limit The preset oil pressure standard deviation safety threshold is, and P std_limit =0.3 MPa .

[0046] (4) Fault mode matching Build a robot failure mode library, where each failure mode k F is represented by an eigenvector k The components of this vector correspond one-to-one with the features extracted in Table 1, in the following order: Example 2 The fault database stores several typical fault modes. This embodiment 2 lists the following four: ① Gearbox wear: vibration RMS xyz Increase ( RMS xyz >10mm / s), kurtosis Kurt Increase ( Kurt >5) Oil pressure P is normal (P≈2.5) MPa The remaining characteristics are basically normal.

[0047] ② Motor bearing failure: peak vibration peak Increase ( peak >15mm / s), current distortion rate THD Increase ( THD >5%), motor current RMS I rms Slight fluctuations are possible.

[0048] ③ Poor lubrication: Oil pressure P decreases (<2.0) MPa ), synthetic vibration RMS xyz Slowly rising ( RMS xyz >8 mm / s Oil pressure fluctuation P std Increase ( P std >0.15 MPa ).

[0049] ④ Controller overheating: The controller temperature rises (requires an additional temperature sensor), joint temperature T j Increase ( T j >70℃), temperature change rate dT j / dt> 1℃ / s.

[0050] Real-time matching process: The feature vector extracted from the current joint is denoted as... F now ， The degree of matching with the k-th fault mode is calculated using cosine similarity: (7) in, sim k For the current feature vector and the first k The cosine similarity of the feature vectors of each fault mode takes a value in the range [−1, 1]. When the value is 1, the directions are exactly the same (the feature ratios are exactly the same); when the value is 0, they are orthogonal (no correlation); when the value is -1, the directions are completely opposite (rarely occurs in actual fault matching). F now The current real-time calculated feature vector (each component is calculated according to Table 1); F k The first in the fault mode library k The feature vectors of each pattern; ||·|| is the Euclidean norm of the vector.

[0051] when sim k > θ When determining the current joint matching number... k There are several failure modes, among which θ The preset matching threshold ranges from 0.6 to 0.9; in this patent, it is set to... θ =0.75. This threshold can be determined through ROC curve analysis of historical fault data or on-site debugging.

[0052] Feature vector definition: According to Table 1, assuming the current order of extracted feature vectors for a certain joint... F now for: Feature vector of the "motor bearing failure" pattern in the fault database F 2 (After normalization) it is: Calculate the dot product: Calculate the modulus: Cosine similarity: sim k Since the value is greater than 0.75, it is determined to be a "motor bearing failure".

[0053] 1) Health Index Calculation (Mild Abnormalities) ① Vibration score: Take V limit =15 mm / s α=0.2, RMS xyz =12.0, peak =20.0: Therefore: .

[0054] 2) Temperature rating f T By consulting the piecewise function, we can find: f T =10.

[0055] 3) Current rating f I Random selection I rated =15, I rms =14.2, THD =3.2, I limit =5; Therefore: f I =0.36.

[0056] 4) Hydraulic pressure rating f P Pick P normal =2.5 MPa , P std_limit =0.2 MPa, P =2.6 MPa , P std =0.4 MPa ; Therefore: f P =0.8.

[0057] 5) Joint Health Index HI joint Define the weights: w 1 = 0.4 w 2 = 0.3, w 3 = 0.2 w If 4 = 0.1, then: Therefore: HI joint =0.80 6) Health level determination: According to the health level classification table in Table 2: HI joint =0.80 [0.7, 0.9) indicates mild abnormality.

[0058] (5) The health level classification is shown in Table 2: Table 2: Health Level Classification Table 3.2 Dynamic Control Unit The dynamic control unit 11 generates control instructions based on the health assessment report and the current production plan, including: ① Rule base: Stores multiple control rules for industrial robots. Each rule includes conditions, actions, and priorities. ② The rule matching sub-unit matches applicable control rules based on the robot's health level, production task priority, and current working status; ③ The target parameter calculation subunit calculates the robot's target motion parameters according to the matching rules, including joint velocity, acceleration, motion trajectory smoothness coefficient, stop command, etc. ④ The instruction generation subunit encapsulates the target parameters into control instructions and sends them to the robot PLC control module through the data interaction module.

[0059] In practical applications, the specific implementation steps of the dynamic control unit 11 are as follows: (1) The swapping rules in the rule base are shown in Table 3: Table 3: Regulation Rules Table (2) Rule matching Based on real-time data such as the health level of each joint, current joint temperature, task priority, and accelerometer values, the system iterates through the rule base to filter all rules that meet the conditions, selecting the rule with the highest priority for execution. If multiple rules have the highest priority, the action with the least impact on the device is selected according to the aforementioned principles.

[0060] (3) Calculation of target parameters Calculate the new control parameters based on the selected rule: ①Regarding speed adjustment: V curre This represents the actual operating speed of the industrial robot at the current moment (usually derived from controller feedback, in units such as m / s or rad / s). V new This is the target speed value that the robot is expected to achieve after adjustment. K v The speed adjustment factor (dimensionless) and K v Determined according to regulatory rules.

[0061] ②Acceleration adjustment similar to speed: a currene Set the current acceleration value (m / s²). a new This is the adjusted target acceleration that the robot is expected to achieve after adjustment. K a The acceleration adjustment coefficient (dimensionless) is... K a They have the same value.

[0062] Examples of speed and acceleration adjustments: 1) If the rule is "all joint health index ≥ 0.9 (normal level)", then the speed should remain at the original speed. K v =1.0, acceleration K a =1.0; 2) If the rule is "health index of a certain joint" If the value is "[0.7, 0.9) (mildly abnormal)," then the speed adjustment is: Target speed of this joint = Current speed × K v At this time K v=0.8. Acceleration adjustment is: Target acceleration of this joint = Current acceleration × K a At this time K a =0.7.

[0063] ③ If you need to switch tasks, the command should include the target robot ID.

[0064] ④ The stop command is sent directly to the PLC.

[0065] (4) Command generation and transmission Command format example: {"robot_id":"R1","cmd":"set_speed","joint":3,"value":1.2,"mode":"gradual","time":1000} (gradually changes to 1.2 rad / s within 1 second).

[0066] The data is sent to the robot PLC control module 3 via the data interaction module 6.

[0067] IV. Robot PLC Control Module and Execution Module The robot PLC control module 3 communicates with the robot controller (such as the R-30iB model) via Profinet and simultaneously supports EtherCAT and OPC UA protocols to achieve data interaction with robot controllers from different brands. It directly reads and writes motion parameters (such as speed ratio, acceleration, trajectory points, etc.) in the robot controller using the aforementioned industrial Ethernet protocols, without relying on the manufacturer's proprietary SDK; it can also be used as an alternative for older controllers that only support the SDK (such as FANUC PCInterface). The system modifies the robot's motion parameters through this communication link and provides real-time feedback on the actual operating status.

[0068] The execution module 4 includes a servo driver (receiving speed commands), a brake (for emergency stop), and a lubrication solenoid valve (adjusting the lubrication frequency according to oil pressure).

[0069] V. Early Warning Module like Figure 4 As shown, the early warning module 5 includes a tiered early warning unit 12 and an information push unit 13. The tiered early warning unit 12 triggers different levels of early warnings based on the health level output by the main body 1 of the MES system. Minor anomaly: The central control interface will prompt the operator to pay attention.

[0070] Moderate anomaly: Issues an audible and visual alarm and automatically pushes a repair work order to the maintenance terminal.

[0071] Serious abnormality: Immediately notify the safety officer via SMS / email, and at the same time, the safety PLC will force the robot to shut down.

[0072] Information push unit 13 pushes early warning information and handling suggestions to the MES system terminal (central control room) and the field operation and maintenance terminal (handheld PDA) via wireless communication. The push content includes robot name, anomaly level, fault type, and suggested measures. The push protocol uses MQTT to ensure real-time performance and reliability.

[0073] VI. Work Process Combination Figures 1-5 The workflow of the system of this invention is as follows: A method for intelligent control and operation and maintenance of industrial robots based on MES system includes the following steps: S1. Data Acquisition: The robot intelligent detection module collects data such as joint vibration, temperature, motor current, and lubricating oil pressure of the industrial robot in real time, and uploads the filtered data to the main body of the MES system. S2. Health Analysis: The robot health analysis unit, the main body of the MES system, performs data preprocessing, feature extraction, health index calculation, fault mode matching, and health level classification to generate a health assessment report. S3. Control Decision: The dynamic control unit matches control rules, calculates target motion parameters, and generates control instructions based on the health assessment report and the current production plan. S4. Command Execution: Control commands are sent to the robot PLC control module via the data interaction module. The robot PLC control module modifies parameters such as the robot's speed, acceleration, and trajectory through the robot controller and executes the module response commands. The adjusted actual operating parameters are fed back to the main MES system through the PLC, forming a closed-loop control. S5. Early Warning and Notification: Health level assessment information is pushed through the graded early warning unit. If the health level is moderate or severe abnormal, the early warning module 5 triggers the corresponding level of early warning signal and pushes the early warning information to relevant personnel through the information push unit. S6. Data Synchronization: The data interaction module synchronizes robot operation data, control records, and early warning logs to the enterprise ERP system, providing data support for enterprise production decisions and forming a management closed loop.

[0074] The above solution is merely an illustration of a preferred embodiment, and is not limited thereto. When implementing this invention, appropriate substitutions and / or modifications can be made according to user needs.

[0075] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Other modifications can be readily made by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. A method for intelligent control and operation and maintenance of industrial robots based on a MES system, characterized in that, include: S1. The robot intelligent detection module collects the operating data of the industrial robot in real time, and uploads it to the main body of the MES system after filtering. The robot health analysis unit of the S2 and MES systems performs feature extraction, health index calculation, fault mode matching, and health level classification on the operating data, and generates a health assessment report. S3. If the health level is normal or slightly abnormal, the dynamic control unit matches the control rules, calculates the target motion parameters, generates control instructions, and proceeds to S4 based on the health assessment report and the current production plan; otherwise, proceeds to S5. S4. The robot PLC control module receives the control command, modifies the target motion parameters of the robot through the robot controller, and then proceeds to S6. S5. If the health level is moderately abnormal or severely abnormal, the early warning module will trigger an early warning and push information, and then proceed to S6. S6, the data interaction module synchronizes robot operation data, control records, and early warning logs to the enterprise ERP system; The health index includes: the overall health index of the robot that makes system-level decisions. HI robot To locate the joint health index of the faulty joint HI joint ; The overall health index HI robot It is characterized by the following formula: In the above formula, n is the total number of joints. For the first j The health index of each joint. β j For the first j The weight of each joint, and ; The health index of each joint HI joint It is characterized by the following formula: In the above formula, w 1. w 2. w 3. w 4 represents the weight of each sub-item scoring function. f vib For vibration scoring, f T Rate the temperature. f I Rate the current. f P For hydraulic pressure rating; The health level classification is based on the range of the health index and is matched with the corresponding level description.

2. The intelligent control and operation and maintenance method for industrial robots based on a MES system as described in claim 1, characterized in that, In S2, the extracted features include: synthetic vibrations. RMS xyz Vibration peak value peak vibration kurtosis Kurt Joint temperature T j Temperature change rate dT j / dt Motor current I rms Current distortion rate THD Hydraulic P Oil pressure fluctuation P std .

3. The intelligent control and operation and maintenance method for industrial robots based on a MES system as described in claim 2, characterized in that, Vibration score f vib It is characterized by the following formula: In the above formula, min To find the minimum function, V limit The preset effective vibration value safety threshold, α Peak impact factor, 0≤ α ≤1; Temperature rating f T It is characterized by the following formula: Current rating f I It is characterized by the following formula: In the above formula, I rated This is the effective value of the rated current of the robot drive motor. THD limit The preset total harmonic distortion rate (THD) threshold for the current. Oil pressure rating f P It is characterized by the following formula: In the above formula, P normal The rated oil pressure for the lubrication system of industrial robots. P std_limit This is the preset safety threshold for the standard deviation of oil pressure.

4. The intelligent control and operation and maintenance method for industrial robots based on a MES system as described in claim 1, characterized in that, In S2, the fault mode matching is achieved through cosine similarity matching as follows: In the above formula, sim k Let cosine similarity be the current fault feature vector and the k-th fault mode feature vector. F now This is the fault feature vector obtained through current acquisition and processing. F k The first in the fault mode library k The feature vectors of a standard failure mode, where ||·|| is the Euclidean norm of the vector; when sim k > θ When determining the current joint matching number... k There are several failure modes, among which θ This is the preset matching threshold.

5. The intelligent control and operation and maintenance method for industrial robots based on a MES system as described in claim 1, characterized in that, In S3, the rule base of the dynamic control unit includes: rule ID, condition, action, and priority, and its control rules are as follows: The condition for rule ID R01 is: all joints are at normal health level and the task priority is high; the corresponding action matching is: each joint maintains its original movement speed; the corresponding priority is set to:

3. The condition for R02 in rule ID is: when the health level of any joint is slightly abnormal and the temperature of the corresponding joint is greater than 60℃; the corresponding action matching is: adjust the speed of the corresponding joint to 80% of the original speed and the acceleration to 70% of the original acceleration; the corresponding priority is set to:

2. The condition for rule ID R03 is: when the health level of any joint is moderately abnormal; the corresponding action matching is: if there is a backup robot, switch the task to the backup robot, otherwise reduce the speed to 50%; the corresponding priority is set to:

1. The condition for rule ID R04 is: when the health level of any joint is severely abnormal; the corresponding action matching is: then immediately stop the robot and lock it; the corresponding priority is set to:

0. The condition for rule ID R05 is: when a collision feature is detected; the corresponding action is: immediately stop and back up 10mm; the corresponding priority is set to:

0. Among them, when matching the control rules, the rules that meet the conditions are selected based on the current health level of each joint and the priority of the global production plan, and the rules with the highest priority are executed. If multiple rules have the same priority, the action with the least impact on the device will be selected.

6. An intelligent control and maintenance system for industrial robots, applied in the intelligent control and maintenance method for industrial robots based on a MES system as described in any one of claims 1-5, characterized in that, include: The main body of the MES system used for production planning management and equipment collaborative scheduling; A robot intelligent detection module for collecting robot operation data is installed on the industrial robot body and controller. The robot intelligent detection module includes: sensor I for collecting joint vibration, sensor II for collecting motor current, sensor III for collecting joint temperature and controller temperature, and sensor VI for collecting reducer lubricating oil pressure. A robot PLC control module that communicates with the robot controller to receive control commands and modify robot motion parameters; The execution module for responding to control commands includes: robot servo drivers, brakes, and lubrication solenoid valves; An early warning module used to issue warning signals based on health levels; This data interaction module is used for encrypted data transmission between modules and synchronization with the enterprise ERP system, supporting industrial Ethernet and wireless networks. The main body of the MES system is bidirectionally connected to the robot intelligent detection module, the robot PLC control module, and the early warning module through the data interaction module. The robot's PLC control module and execution module are unidirectionally electrically connected. The robot intelligent detection module is bidirectionally connected to the industrial robot to collect robot operation data in real time and feed it back to the main body of the MES system.

7. The industrial robot intelligent control and operation and maintenance system based on MES system as described in claim 6, characterized in that, The main body of the MES system incorporates a robot health analysis unit and a dynamic control unit. The robot health analysis unit includes: A data preprocessing subunit used to filter and normalize raw sensor data; A feature extraction subunit used to extract characteristic quantities reflecting the robot's health status; A health index calculation subunit that calculates the overall health index of the robot and its individual joints; Fault mode matching subunit for identifying potential fault types; The robot's health level is divided into sub-units based on health indices. The dynamic control unit includes: Stores a rule base containing multiple rules for controlling industrial robots; A rule matching subunit that matches control rules based on the robot's health level, production task priority, and current working status; The target parameter calculation sub-unit calculates the robot target motion parameters according to the matching rules; The target motion parameters are encapsulated into control commands and sent to the robot PLC control module via the data interaction module.

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