Servo motor dynamic overload detection and protection method based on heat accumulation model

Abstract

The invention discloses a servo motor dynamic overload detection and protection method based on a heat accumulation model, which relates to the technical field of servo system fault protection application, and comprises the steps of constructing a heat accumulation equivalent model, dynamically detecting an overload state, executing a heat dissipation recovery strategy, implementing a multi-stage protection mechanism, self-adaptive environment compensation and a historical data learning function. Through discretization processing of overload curve data and a nonlinear acceleration calculation strategy, the problems of protection lag and false triggering caused by traditional single-parameter detection are avoided, it is ensured that the motor can respond in time in light load, heavy load and load fluctuation scenes, the burn-out risk of the motor is reduced, and through integration of an environment temperature sensor and a heat dissipation condition evaluation unit, the reliability of the motor is improved. The heat accumulation coefficient and the heat dissipation attenuation parameter are dynamically adjusted, the problem of model deviation caused by environment interference is solved, when heat dissipation deterioration and temperature feedback abnormity are detected, the heat dissipation recovery period is automatically prolonged, calculation logic is corrected, and the continuous operation reliability of the system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of servo system fault protection application, in particular to a servo motor dynamic overload detection and protection method based on thermal accumulation model. BACKGROUND

[0002] The servo motor can control the speed, the position accuracy is very accurate, the voltage signal is converted into torque and speed to drive the control object, the servo motor rotor speed is controlled by the input signal, and can quickly react, in the automatic control system, used as an execution element, and has small electromechanical time constant, high linearity, can convert the received electric signal into angular displacement and angular velocity output on the motor shaft.

[0003] In the field of servo motor operation control, the traditional overload detection method has defects: unable to quantize the combined effect of load duration on thermal accumulation in real time, only relying on current threshold and temperature sensor for single parameter judgment, resulting in insufficient protection sensitivity under light load conditions and protection lag caused by thermal inertia under heavy load; Lack of dynamic adaptability, fixed protection threshold is difficult to match the sharp fluctuations of load in industrial robot and numerical control machine tool scenes, resulting in reduced equipment utilization and accelerated motor insulation aging; Ignoring the closed-loop management of the heat dissipation stage, the residual heat continues to accumulate after the motor recovers from overload, the risk of secondary overload increases and there is no accurate calibration mechanism for heat dissipation rate; Environmental temperature and heat dissipation conditions are not included in the compensation system, resulting in the expansion of thermal accumulation calculation deviation, and the deviation between the measured temperature and the model prediction value cannot be self-corrected; The historical overload mode is not effectively mined and utilized, the protection threshold is rigid and cannot realize dynamic optimization of early warning parameters through periodic regularity of load.

[0004] Therefore, the present application provides a servo motor dynamic overload detection and protection method based on thermal accumulation model to solve the above problems. SUMMARY

[0005] The main purpose of the present application is to provide a servo motor dynamic overload detection and protection method based on thermal accumulation model to solve the problems raised in the above background.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is as follows: a servo motor dynamic overload detection and protection method based on thermal accumulation model, comprising the following steps:

[0007] S1, build a thermal accumulation equivalent model: obtain motor overload curve data, discretize the data into a load-time corresponding table, establish a mapping model of load value and equivalent heat generation coefficient, set a total heat capacity threshold, and real-time accumulate heat value, and trigger protection when the proportion is greater than 1;

[0008] S2. Dynamic overload detection: The load value is acquired in real time through current sampling and encoder conversion. The allowed duration is obtained by querying the corresponding table and starting the timer. When the load continuously exceeds the rated value, a nonlinear acceleration model is used to accumulate the heat accumulation. To address load fluctuations, the sliding window method is used to calculate the root mean square of the load value within the window as the equivalent load value.

[0009] S3. Execute the heat dissipation recovery strategy: calibrate the natural heat dissipation rate of the motor and establish a temperature-time decay curve. When the load drops below the rated value, the thermal accumulation decay amount changes according to the preset decay rate. A safe thermal accumulation threshold is set. Overload operation is allowed again only when the real-time value is lower than the threshold.

[0010] S4. Implement a multi-level protection mechanism: set warning thresholds and protection thresholds, and calculate the thermal accumulation through closed-loop correction of thermistor measured temperature;

[0011] S5, Adaptive environmental compensation: The integrated temperature sensor dynamically adjusts the heat accumulation coefficient, monitors the heat dissipation conditions, and increases the heat dissipation correction coefficient when deterioration occurs;

[0012] S6. Historical data learning function: record overload event parameters, analyze patterns using time series algorithms, and optimize warning and protection thresholds based on machine learning.

[0013] Preferably, the discretization process in step S1 specifically includes:

[0014] The motor overload curve data is obtained through experimental calibration and manufacturer's technical documents. The curve uses torque percentage as the load unit and the allowed duration as the time unit;

[0015] The continuous curve is divided into preset load intervals, and the load interval is set to 5% rated torque increment to generate a discretized load-time correspondence table;

[0016] Each set of data corresponds to the safety duration threshold under the load value. When the load value is 110% of the rated torque, the allowed duration is 10 seconds. When the load value is 150% of the rated torque, the allowed duration is 3 seconds.

[0017] The discretization process uses a linear interpolation algorithm to compensate for the nonlinear region of the curve, ensuring that the accuracy of the data points is within 1%.

[0018] Preferably, the mapping model between the load value and the equivalent heating coefficient in step S1 is specifically:

[0019] There is a linear positive correlation between the load value and the equivalent heating coefficient, that is, when the load value increases by 10%, the equivalent heating coefficient increases by 0.13 times;

[0020] The corresponding coefficient for rated load is 1.0, and the coefficient for 120% overload is 1.2;

[0021] In the calculation of heat accumulation per unit time, the time increment is set to a millisecond sampling period, and the heat accumulation increment is proportional to the load value;

[0022] The mapping model is obtained by fitting the experimental data using the least squares method, and the fitting error is controlled within 2% to ensure the adaptability of the model under light-load and heavy-load conditions.

[0023] Preferably, in the acceleration model accumulation in step S2:

[0024] The nonlinear acceleration model is used to calculate the heat accumulation increment. The specific formula is: ;

[0025] in is the cumulative heat increment per unit time, is the real-time motor load value, is the rated load value of the motor, is the time increment. This formula is only activated when the load exceeds the rated load by more than 10%. When the load is 110%, the heat accumulation rate is 1.21 times the baseline value, and when the load is 150%, it is 2.25 times, to reflect the square law nonlinear effect of load increase on heat accumulation.

[0026] The activation of the nonlinear acceleration model requires two conditions to be met:

[0027] Load threshold: real-time load is greater than 110% of rated load;

[0028] Time threshold: The overload state lasts longer than 100ms;

[0029] When load fluctuations cause the state to be intermittent, the cumulative duration of continuous overload is used for judgment. The time threshold is determined by the motor thermal inertia constant to ensure that the model is only activated during the effective heat accumulation stage.

[0030] During the accumulation process, the ratio of the heat accumulation amount to the total heat capacity threshold is compared in real time, and the sampling frequency of the ratio calculation is 100 Hz to ensure timeliness.

[0031] Preferably, the sliding window method in step S2 specifically includes:

[0032] Dynamic window duration: The basic duration is 5 seconds, which is dynamically adjusted based on the standard deviation σ of load fluctuations within the window;

[0033] Duration adjustment formula:

[0034] ;

[0035] in is the rated load value, and σ is calculated in real time through the variance of the load value in the window;

[0036] The root mean square of the load sampling values ​​within the calculation window is taken as the equivalent load value. The calculation formula is:

[0037] ;

[0038] in is the equivalent load value, is the number of sampling points in the window, is the load value of the i-th sampling point;

[0039] RMS value calculation is applicable to load fluctuation scenarios to reduce false triggering;

[0040] When the equivalent load value is used in the heat accumulation calculation, it is linked to the load-time correspondence table to ensure the stability of the protection action when the load fluctuates.

[0041] Preferably, the thermal cumulative attenuation in step S3 is specifically:

[0042] ;

[0043] in is the cumulative thermal attenuation, is the heat dissipation rate constant, is the time increment;

[0044] The heat dissipation rate constant is calibrated by no-load test, and the basic value is set to 0.05 / s at an ambient temperature of 25°C;

[0045] The attenuation model uses a piecewise function: linear attenuation is used in the high temperature zone, and exponential attenuation is used in the low temperature zone to match the thermal inertia of the motor. The high temperature zone is >40°C, and the low temperature zone is ≤40°C.

[0046] The safety heat accumulation threshold is set at 40% of the total heat capacity threshold and is dynamically adjusted through temperature feedback.

[0047] Preferably, in the multi-level protection mechanism of step S4:

[0048] The warning threshold is set at 75% of the total thermal capacity threshold. When triggered, the motor output power is limited to 80% of the rated value and the warning signal is activated;

[0049] The protection threshold is set at 97% of the total thermal capacity. When triggered, the machine is forced to shut down and an audible and visual alarm is generated.

[0050] The closed-loop correction uses a thermistor to collect the actual temperature value of the stator winding. When the deviation between the measured temperature and the calculated heat accumulation exceeds 10%, the model correction module is activated and the correction coefficient is calculated based on the deviation ratio.

[0051] The sampling frequency of the correction process is 50Hz to ensure real-time performance.

[0052] Preferably, in the environmental compensation in step S5:

[0053] For every 10°C increase in ambient temperature, the heat accumulation calculation coefficient increases by 0.15 times;

[0054] Heat dissipation condition assessment is achieved through fan speed monitoring. When the fan speed drops by 20%, it is determined that the heat dissipation has deteriorated. At this time, the heat dissipation rate constant is reduced to 60% of the normal value.

[0055] The compensation coefficients are stored in a lookup table. The table data is generated based on thermal simulation experiments and covers temperatures up to 60°C.

[0056] The compensation mechanism is linked to the historical data learning function, and the correction coefficient is automatically adjusted when heat dissipation deterioration events occur frequently.

[0057] Preferably, in the historical data learning function of step S6:

[0058] Overload event parameters include load value, duration, and ambient temperature three-dimensional data, which are stored in the operation log;

[0059] The time series analysis algorithm uses an autoregressive integrated moving average model to explore the periodicity of loads;

[0060] The machine learning algorithm uses a support vector machine. Input parameters include historical load peaks, daily average overload frequency, and average heat dissipation conditions. It outputs dynamically adjusted warning and protection thresholds, with the adjustment range being less than 15% of the initial value.

[0061] The training dataset covers 1000 hours of running data, and the model update cycle is 24 hours.

[0062] Preferably, a fault diagnosis linkage strategy is also included:

[0063] When the cumulative protection threshold is triggered 3 times / 24 hours, a motor insulation aging warning report is automatically generated. The report includes the thermal accumulation peak value, event time, and environmental conditions.

[0064] Overload events are stored in conjunction with motor operating conditions to form a thermal stress life map. The map uses load-time as the coordinate axis to visualize the heat accumulation history.

[0065] The present invention has the following beneficial effects:

[0066] 1. In the present invention, by setting up a thermal accumulation modeling module, during the servo motor overload detection process, a thermal accumulation equivalent model is constructed based on the combined influence of load and time, the dynamic change of load is converted into thermal accumulation in real time, and a multi-level protection threshold is set to improve the accuracy of overload status judgment; through the discretization processing of overload curve data and nonlinear acceleration calculation strategy, the protection lag and false triggering problems caused by traditional single parameter detection are avoided, ensuring that the motor can respond in time under light load, heavy load and load fluctuation scenarios, reducing the risk of motor burnout.

[0067] 2. In the present invention, by setting up a dynamic compensation module, the natural heat dissipation rate is calibrated in real time during the heat dissipation recovery stage and the heat accumulation attenuation mechanism is started, and the heat accumulation calculation value is corrected in combination with the temperature feedback closed loop; by integrating the ambient temperature sensor and the heat dissipation condition evaluation unit, the heat accumulation coefficient and the heat dissipation attenuation parameters are dynamically adjusted to solve the model deviation problem caused by environmental interference; when heat dissipation deterioration and temperature feedback abnormalities are detected, the heat dissipation recovery period is automatically extended and the calculation logic is corrected to avoid the risk of secondary overload from the root and improve the reliability of the system's continuous operation.

[0068] 3. In the present invention, an adaptive learning module is set up to record the load spectrum, duration and environmental parameters of overload events in the historical operation data analysis, and a time series algorithm is used to explore the load periodicity law; the warning threshold and protection threshold are dynamically optimized based on the machine learning model, so that the protection strategy can adaptively match the requirements of different working conditions; by constructing a thermal stress life map to visualize the historical overload distribution, intelligent prediction of the motor health status and maintenance decision support are achieved, fundamentally improving the adaptability and life management capabilities of the overload protection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flow chart of a servo motor dynamic overload detection and protection method based on a thermal accumulation model of the present invention. DETAILED DESCRIPTION

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0071] See also Figure 1 The method for dynamic overload detection and protection of a servo motor based on a thermal accumulation model comprises the following steps:

[0072] S1. Build a heat accumulation equivalent model: Obtain motor overload curve data, discretize it into a load-time correspondence table, establish a mapping model between load value and equivalent heating coefficient, set the total heat capacity threshold, accumulate heat value in real time, and trigger protection when the ratio is greater than 1;

[0073] S2. Dynamic overload detection: The load value is acquired in real time through current sampling and encoder conversion. The allowed duration is obtained by querying the corresponding table and starting the timer. When the load continuously exceeds the rated value, a nonlinear acceleration model is used to accumulate the heat accumulation. To address load fluctuations, the sliding window method is used to calculate the root mean square of the load value within the window as the equivalent load value.

[0074] S3. Execute the heat dissipation recovery strategy: calibrate the natural heat dissipation rate of the motor and establish a temperature-time decay curve. When the load drops below the rated value, the thermal accumulation decay amount changes according to the preset decay rate. A safe thermal accumulation threshold is set. Overload operation is allowed again only when the real-time value is lower than the threshold.

[0075] S4. Implement a multi-level protection mechanism: set warning thresholds and protection thresholds, and calculate the thermal accumulation through closed-loop correction of thermistor measured temperature;

[0076] S5, Adaptive environmental compensation: The integrated temperature sensor dynamically adjusts the heat accumulation coefficient, monitors the heat dissipation conditions, and increases the heat dissipation correction coefficient when deterioration occurs;

[0077] S6. Historical data learning function: record overload event parameters, analyze patterns using time series algorithms, and optimize warning and protection thresholds based on machine learning.

[0078] The discretization process in step S1 specifically includes:

[0079] The motor overload curve data is obtained through experimental calibration and manufacturer's technical documents. The curve uses torque percentage as the load unit and the allowed duration as the time unit;

[0080] The continuous curve is divided into preset load intervals, and the load interval is set to 5% rated torque increment to generate a discretized load-time correspondence table;

[0081] Each set of data corresponds to the safety duration threshold under the load value. When the load value is 110% of the rated torque, the allowed duration is 10 seconds. When the load value is 150% of the rated torque, the allowed duration is 3 seconds.

[0082] The discretization process uses a linear interpolation algorithm to compensate for the nonlinear region of the curve, ensuring that the accuracy of the data points is within 1%.

[0083] The mapping model between the load value and the equivalent heating coefficient in step S1 is specifically:

[0084] There is a linear positive correlation between the load value and the equivalent heating coefficient, that is, when the load value increases by 10%, the equivalent heating coefficient increases by 0.13 times;

[0085] The corresponding coefficient for rated load is 1.0, and the coefficient for 120% overload is 1.2;

[0086] In the calculation of heat accumulation per unit time, the time increment is set to a millisecond sampling period, and the heat accumulation increment is proportional to the load value;

[0087] The mapping model is obtained by fitting the experimental data using the least squares method, and the fitting error is controlled within 2% to ensure the adaptability of the model under light-load and heavy-load conditions.

[0088] In the acceleration model accumulation of step S2:

[0089] The nonlinear acceleration model is used to calculate the heat accumulation increment. The specific formula is:

[0090] ;

[0091] in is the cumulative heat increment per unit time, is the real-time motor load value, is the rated load value of the motor, is the time increment. This formula is only activated when the load exceeds the rated load by more than 10%. When the load is 110%, the heat accumulation rate is 1.21 times the baseline value, and when the load is 150%, it is 2.25 times, to reflect the square law nonlinear effect of load increase on heat accumulation.

[0092] The activation of the nonlinear acceleration model requires two conditions to be met:

[0093] Load threshold: real-time load is greater than 110% of rated load;

[0094] Time threshold: The overload state lasts longer than 100ms;

[0095] During the accumulation process, the ratio of the heat accumulation amount to the total heat capacity threshold is compared in real time, and the sampling frequency of the ratio calculation is 100 Hz to ensure timeliness.

[0096] The sliding window method of step S2 specifically includes:

[0097] Dynamic window duration: The basic duration is 5 seconds, which is dynamically adjusted based on the standard deviation σ of load fluctuations within the window;

[0098] Duration adjustment formula:

[0099] ;

[0100] in is the rated load value, and σ is calculated in real time through the variance of the load value in the window;

[0101] The root mean square of the load sampling values ​​within the calculation window is taken as the equivalent load value. The calculation formula is:

[0102] ;

[0103] in is the equivalent load value, is the number of sampling points in the window, is the load value of the i-th sampling point;

[0104] RMS value calculation is applicable to load fluctuation scenarios to reduce false triggering;

[0105] When the equivalent load value is used in the heat accumulation calculation, it is linked to the load-time correspondence table to ensure the stability of the protection action when the load fluctuates.

[0106] The thermal cumulative attenuation in step S3 is specifically:

[0107] ;

[0108] in is the cumulative thermal attenuation, is the heat dissipation rate constant, is the time increment;

[0109] The heat dissipation rate constant is calibrated by no-load test, and the basic value is set to 0.05 / s at an ambient temperature of 25°C;

[0110] The attenuation model uses a piecewise function: linear attenuation is used in the high temperature zone, and exponential attenuation is used in the low temperature zone to match the thermal inertia of the motor. The high temperature zone is >40°C, and the low temperature zone is ≤40°C.

[0111] The safety heat accumulation threshold is set at 40% of the total heat capacity threshold and is dynamically adjusted through temperature feedback.

[0112] In the multi-level protection mechanism of step S4:

[0113] The warning threshold is set at 75% of the total thermal capacity threshold. When triggered, the motor output power is limited to 80% of the rated value and the warning signal is activated;

[0114] The protection threshold is set at 97% of the total thermal capacity. When triggered, the machine is forced to shut down and an audible and visual alarm is generated.

[0115] The closed-loop correction uses a thermistor to collect the actual temperature value of the stator winding. When the deviation between the measured temperature and the calculated heat accumulation exceeds 10%, the model correction module is activated and the correction coefficient is calculated based on the deviation ratio.

[0116] The sampling frequency of the correction process is 50Hz to ensure real-time performance.

[0117] In the environmental compensation of step S5:

[0118] For every 10°C increase in ambient temperature, the heat accumulation calculation coefficient increases by 0.15 times;

[0119] Heat dissipation condition assessment is achieved through fan speed monitoring. When the fan speed drops by 20%, it is determined that the heat dissipation has deteriorated. At this time, the heat dissipation rate constant is reduced to 60% of the normal value.

[0120] The compensation coefficients are stored in a lookup table. The table data is generated based on thermal simulation experiments and covers temperatures up to 60°C.

[0121] The compensation mechanism is linked to the historical data learning function, and the correction coefficient is automatically adjusted when heat dissipation deterioration events occur frequently.

[0122] In the historical data learning function of step S6:

[0123] Overload event parameters include load value, duration, and ambient temperature three-dimensional data, which are stored in the operation log;

[0124] The time series analysis algorithm uses an autoregressive integrated moving average model to explore the periodicity of loads;

[0125] The machine learning algorithm uses a support vector machine. Input parameters include historical load peaks, daily average overload frequency, and average heat dissipation conditions. It outputs dynamically adjusted warning and protection thresholds, with the adjustment range being less than 15% of the initial value.

[0126] The training dataset covers 1000 hours of running data, and the model update cycle is 24 hours.

[0127] It also includes fault diagnosis linkage strategy:

[0128] When the cumulative protection threshold is triggered 3 times / 24 hours, a motor insulation aging warning report is automatically generated. The report includes the thermal accumulation peak value, event time, and environmental conditions.

[0129] Overload events are stored in conjunction with motor operating conditions to form a thermal stress life map. The map uses load-time as the coordinate axis to visualize the heat accumulation history.

[0130] Implementation 1: Multi-condition adaptive protection of CNC machine tool spindle motor

[0131] This method was applied to the spindle servo motor of a machining center under heavy cutting conditions. During the heat accumulation model construction phase, the heat dissipation parameters were calibrated through a no-load temperature increase experiment. At an ambient temperature of 40°C, it took 210 seconds for the motor to cool from 120°C to 80°C, and the heat dissipation rate constant a was fitted to be 0.033 / s.

[0132] The dynamic detection module sets a two-level sliding window based on the intermittent cutting load characteristics: the short window processes the transient impact of the cutter teeth cutting in, and the long window evaluates the continuous load effect. When milling titanium alloy, the load peak reaches the rated value, and the system automatically activates the square law acceleration accumulation to increase the heat accumulation rate to the baseline value.

[0133] The multi-level protection mechanism links the cooling system: when the heat accumulation reaches the warning threshold, the PLC controls the oil cooler flow to increase; when the protection threshold is reached, the tool is immediately retracted and shut down. The environmental compensation module uses the built-in temperature and humidity sensors in the control cabinet to automatically increase the heat accumulation coefficient in the workshop in summer.

[0134] The load fluctuation frequency of the finishing process is several times that of the normal working conditions. The adaptive learning module shortens the sliding window time accordingly. After more than a few protection shutdowns occur in a single day, the fault diagnosis system generates a spindle bearing lubrication deterioration warning. After disassembly and inspection, it is confirmed that the grease is carbonized and replaced in advance to avoid motor burning accidents. This implementation reduces the temperature rise of the motor under continuous heavy cutting conditions and extends the tool life.

[0135] Implementation 2: Optimized heat dissipation control for multi-motor coordination in logistics sorting lines

[0136] In the express sorting system, eight conveyor belt servo motors are centrally controlled. The system synchronously collects the load of each motor through the bus. The heat accumulation model sets different parameters based on the parallel operation characteristics: the total heat capacity threshold of the inlet motor is set to 4500J due to frequent start and stop; the continuously running motor at the outlet is set to 5500J.

[0137] During the dynamic detection phase, when a package gets stuck, causing a sudden increase in the load on motor No. 3, the nonlinear acceleration model causes the accumulated heat to reach the protection threshold, triggering emergency reversal of the motor to release the jam. The heat dissipation recovery strategy innovatively introduces collaboration with neighboring machines: when motor No. 5 enters the attenuation stage due to poor heat dissipation, the scheduling system automatically transfers its load to the adjacent motors No. 4 and No. 6, increasing its heat dissipation rate. The environmental compensation module detects the peak temperature period in the workshop during the summer afternoon and automatically lowers the warning threshold for the entire system.

[0138] Analysis of the historical learning module found that the load peak every Monday is higher than the average. Based on this, the LSTM model is trained to activate the enhanced cooling mode in advance. In the scenario of deteriorating heat dissipation, the system automatically lowers the safety threshold and prohibits load distribution operations. After three consecutive months of operation, it has been verified that the motor group's fault downtime under peak operating conditions has been reduced, the overall energy consumption has been reduced, and the thermal stress life map shows that the aging rate of the motor insulation has slowed down.

[0139] Implementation 3: Overload protection and energy efficiency optimization of the injection molding machine's mold clamping servo motor

[0140] In a large injection molding production line, the mold closing mechanism servo motor adopts this method to realize dynamic protection and energy efficiency collaborative control. In the system initialization stage, a discrete load-time correspondence table is constructed based on the motor thermal characteristic curve: the maximum allowable duration of the high-pressure locking stage in the mold closing cycle is set, and there is no limit value in the low-pressure mold moving stage; the dynamic detection module monitors the crank angle in real time through a high-precision encoder, and automatically identifies the load state combined with the pressure sensor feedback. When the mold foreign matter causes the locking resistance to abnormally rise, the load value jumps, and the nonlinear acceleration model immediately increases the heat accumulation rate to several times the reference value, so that the system triggers the emergency mold opening protection when the heat accumulation reaches the total capacity threshold, avoiding the deformation of the crank mechanism.

[0141] The heat dissipation recovery strategy is specially designed for the high temperature and high humidity environment of the injection molding workshop: during the mold cooling period, the system automatically starts the forced air cooling device to increase the heat dissipation rate constant, and the closed-loop correction module automatically adjusts the heat accumulation calculation reference when detecting local temperature rise abnormalities through the temperature sensor embedded in the mold plate, eliminating model deviations caused by uneven heat conduction of the mold. The historical learning module analyzes three months of production data and finds that the locking load peak when producing polycarbonate materials is higher than that when producing ABS materials, and accordingly establishes a material-load correlation database to pre-adjust the protection threshold parameters when switching materials.

[0142] The adaptive compensation system is linked to the workshop environment monitoring network: when the summer ambient temperature continuously exceeds 35°C, the warning threshold is automatically lowered from the total capacity; when the cooling water tower efficiency is detected to be reduced, the standby chiller unit is immediately activated and the mold opening heat dissipation time is extended. In terms of energy efficiency optimization, the system analyzes the load curve in the mold closing cycle and identifies that the high-pressure locking stage can be shortened without affecting product quality. The fault prediction mechanism automatically generates a guide rail lubrication inspection work order after multiple abnormal overloads occur in succession, and the maintenance personnel check and confirm the rail wear and replace it in time to avoid unplanned downtime losses. After implementing this scheme, the motor winding temperature peak of the injection molding machine is reduced, the quarterly maintenance cost is reduced, and the unit energy consumption is reduced.

[0143] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, and the scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A servo motor dynamic overload detection and protection method based on a thermal accumulation model, characterized in that: The following steps are involved: S1. Build a heat accumulation equivalent model: Obtain motor overload curve data, discretize it into a load-time correspondence table, establish a mapping model between load value and equivalent heating coefficient, set the total heat capacity threshold, accumulate heat value in real time, and trigger protection when the ratio is greater than 1; S2. Dynamic overload detection: The load value is acquired in real time through current sampling and encoder conversion. The allowed duration is obtained by querying the corresponding table and starting the timer. When the load continuously exceeds the rated value, a nonlinear acceleration model is used to accumulate the heat accumulation. To address load fluctuations, the sliding window method is used to calculate the root mean square of the load value within the window as the equivalent load value. S3. Execute the heat dissipation recovery strategy: calibrate the natural heat dissipation rate of the motor and establish a temperature-time decay curve. When the load drops below the rated value, the thermal accumulation decay amount changes according to the preset decay rate. A safe thermal accumulation threshold is set. Overload operation is allowed again only when the real-time value is lower than the threshold. S4. Implement a multi-level protection mechanism: set warning thresholds and protection thresholds, and calculate the thermal accumulation through closed-loop correction of thermistor measured temperature; S5, Adaptive environmental compensation: The integrated temperature sensor dynamically adjusts the heat accumulation coefficient, monitors the heat dissipation conditions, and increases the heat dissipation correction coefficient when deterioration occurs; S6. Historical data learning function: record overload event parameters, analyze patterns using time series algorithms, and optimize warning and protection thresholds based on machine learning.

2. The method for dynamic overload detection and protection of a servo motor based on a heat accumulation model according to claim 1, characterized in that: The discretization process in step S1 specifically includes: The motor overload curve data is obtained through experimental calibration and manufacturer's technical documents. The curve uses torque percentage as the load unit and the allowed duration as the time unit; The continuous curve is divided into preset load intervals, and the load interval is set to 5% rated torque increment to generate a discretized load-time correspondence table; Each set of data corresponds to the safety duration threshold under the load value. When the load value is 110% of the rated torque, the allowed duration is 10 seconds. When the load value is 150% of the rated torque, the allowed duration is 3 seconds. The discretization process uses a linear interpolation algorithm to compensate for the nonlinear region of the curve, ensuring that the accuracy of the data points is within 1%.

3. The method for dynamic overload detection and protection of a servo motor based on a heat accumulation model according to claim 1, characterized in that: The mapping model between the load value and the equivalent heating coefficient in step S1 is specifically: There is a linear positive correlation between the load value and the equivalent heating coefficient, that is, when the load value increases by 10%, the equivalent heating coefficient increases by 0.13 times; The corresponding coefficient for rated load is 1.0, and the coefficient for 120% overload is 1.2; In the calculation of heat accumulation per unit time, the time increment is set to a millisecond sampling period, and the heat accumulation increment is proportional to the load value; The mapping model is obtained by fitting the experimental data using the least squares method, and the fitting error is controlled within 2% to ensure the adaptability of the model under light-load and heavy-load conditions.

4. The method for dynamic overload detection and protection of a servo motor based on a heat accumulation model according to claim 1, characterized in that: In the acceleration model accumulation of step S2: The nonlinear acceleration model is used to calculate the heat accumulation increment. The specific formula is: ； in is the cumulative heat increment per unit time, is the real-time motor load value, is the rated load value of the motor, is the time increment; This formula is only activated when the load exceeds the rated load by more than 10% continuously. When the load is 110%, the heat accumulation rate is 1.21 times the baseline value, and when the load is 150%, it is 2.25 times, to reflect the square law nonlinear effect of the load increase on heat accumulation. During the accumulation process, the ratio of the heat accumulation amount to the total heat capacity threshold is compared in real time, and the sampling frequency of the ratio calculation is 100 Hz to ensure timeliness.

5. The method for dynamic overload detection and protection of a servo motor based on a heat accumulation model according to claim 1, characterized in that: The sliding window method in step S2 specifically includes: The window duration is set to be adjustable up to 5 seconds, and the window sliding step is synchronized with the control system sampling period; The root mean square of the load sampling values ​​within the calculation window is taken as the equivalent load value. The calculation formula is: ； in is the equivalent load value, is the number of sampling points in the window, is the load value of the i-th sampling point; RMS value calculation is applicable to load fluctuation scenarios to reduce false triggering; When the equivalent load value is used in the heat accumulation calculation, it is linked to the load-time correspondence table to ensure the stability of the protection action when the load fluctuates.

6. The method for dynamic overload detection and protection of a servo motor based on a heat accumulation model according to claim 1, characterized in that: The thermal cumulative attenuation in step S3 is specifically: ； in is the cumulative thermal attenuation, is the heat dissipation rate constant, is the time increment; The heat dissipation rate constant is calibrated by no-load test, and the basic value is set to 0.05 / s at an ambient temperature of 25°C; The attenuation model uses a piecewise function: linear attenuation is used in the high temperature zone, and exponential attenuation is used in the low temperature zone to match the thermal inertia of the motor. The high temperature zone is >40°C, and the low temperature zone is ≤40°C. The safety heat accumulation threshold is set at 40% of the total heat capacity threshold and is dynamically adjusted through temperature feedback.

7. The method for dynamic overload detection and protection of a servo motor based on a heat accumulation model according to claim 1, characterized in that: In the multi-level protection mechanism of step S4: The warning threshold is set at 75% of the total thermal capacity threshold. When triggered, the motor output power is limited to 80% of the rated value and the warning signal is activated; The protection threshold is set at 97% of the total thermal capacity. When triggered, the machine is forced to shut down and an audible and visual alarm is generated. The closed-loop correction uses a thermistor to collect the actual temperature value of the stator winding. When the deviation between the measured temperature and the calculated heat accumulation exceeds 10%, the model correction module is activated and the correction coefficient is calculated based on the deviation ratio. The sampling frequency of the correction process is 50Hz to ensure real-time performance.

8. The method for dynamic overload detection and protection of a servo motor based on a heat accumulation model according to claim 1, characterized in that: In the environmental compensation of step S5: For every 10°C increase in ambient temperature, the heat accumulation calculation coefficient increases by 0.15 times; Heat dissipation condition assessment is achieved through fan speed monitoring. When the fan speed drops by 20%, it is determined that the heat dissipation has deteriorated. At this time, the heat dissipation rate constant is reduced to 60% of the normal value. The compensation coefficients are stored in a lookup table. The table data is generated based on thermal simulation experiments and covers temperatures up to 60°C. The compensation mechanism is linked to the historical data learning function, and the correction coefficient is automatically adjusted when heat dissipation deterioration events occur frequently.

9. The method for dynamic overload detection and protection of a servo motor based on a heat accumulation model according to claim 1, characterized in that: In the historical data learning function of step S6: Overload event parameters include load value, duration, and ambient temperature three-dimensional data, which are stored in the operation log; The time series analysis algorithm uses an autoregressive integrated moving average model to explore the periodicity of loads; The machine learning algorithm uses a support vector machine. Input parameters include historical load peaks, daily average overload frequency, and average heat dissipation conditions. It outputs dynamically adjusted warning and protection thresholds, with the adjustment range being less than 15% of the initial value. The training dataset covers 1000 hours of running data, and the model update cycle is 24 hours.

10. The method for dynamic overload detection and protection of a servo motor based on a heat accumulation model according to claim 1, characterized in that: It also includes fault diagnosis linkage strategy: When the cumulative protection threshold is triggered 3 times / 24 hours, a motor insulation aging warning report is automatically generated. The report includes the thermal accumulation peak value, event time, and environmental conditions. Overload events are stored in conjunction with motor operating conditions to form a thermal stress life map. The map uses load-time as the coordinate axis to visualize the heat accumulation history.

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