A method for predicting motor lifespan

Abstract

This invention discloses a method for predicting motor lifespan, comprising: S1. Performing simulation tests on multiple components of a simulated motor to obtain the rate of change of simulated attributes for each component; S2. Performing simulation tests on the entire simulated motor to obtain the rate of change of attributes of the simulated motor; S3. Performing a weighted average of the rate of change of simulated attributes for each component and the rate of change of attributes of the simulated motor to obtain an optimized rate of change; S4. Performing actual tests on a non-destructive motor to obtain the actual rate of change of attributes of the non-destructive motor; S5. Optimizing the actual rate of change of attributes of the non-destructive motor by optimizing the rate of change to obtain a reference rate of change; S6. Performing actual tests on multiple damaged motors to obtain the rate of change of attributes of the damaged motors; S7. Comparing the reference rate of change and the rate of change of attributes of the damaged motors to predict the motor lifespan of the non-destructive motor. This invention's method for predicting motor lifespan, through simulation and statistical analysis of multiple components of a simulated motor, can predict the motor lifespan of a motor under test.

Description

Technical Field

[0001] This invention belongs to the field of motor life prediction, and specifically relates to a method for predicting motor life. Background Technology

[0002] There are generally three methods for testing the lifespan of existing motors:

[0003] 1. Accelerated Life Testing: Using specific testing equipment and methods, the motor is subjected to accelerated testing to simulate long-term use. By monitoring parameters such as the motor's operating status, noise, and vibration, its lifespan under specific conditions is determined.

[0004] 2. Statistical analysis method: By statistically analyzing a large amount of motor usage data, including running time, number of failures, maintenance records, etc., the average failure time can be obtained, and the lifespan of the motor can be estimated accordingly.

[0005] 3. Reliability Testing: In a real-world application environment, a batch of motors undergoes long-term operation tests to observe their failure occurrence and lifespan performance. Based on the test results, the average lifespan of the motors is calculated.

[0006] All three methods have limitations. First, none of them can inform users of the remaining lifespan, which is fatal for users who use large quantities of unsupervised motors that need to be replaced promptly when damaged. For example, in the case of wind turbines inside energy storage battery boxes, if it's impossible to know whether some turbines are damaged, users often have to install 120 units when only 100 are needed to prevent battery damage caused by the inability to replace damaged turbines in time, or replace them all at once when they are close to the theoretical lifespan provided by the turbine supplier, which not only increases costs but also causes a huge waste of energy. Second, the first and third methods are all laboratory tests. The biggest problem with laboratory tests is that they cannot fully simulate real-world conditions. For example, the high-salt and high-humidity environment of coastal areas, which changes with the seasons, is basically impossible to simulate in the laboratory. Laboratory tests are all under standard operating conditions. Finally, the limitation of statistical analysis is that the lifespan given is based on statistical analysis of data from damaged turbines. However, motors are designed to avoid most failure factors, and the failure rate is often not high in actual use, resulting in insufficient data samples. At the same time, this prediction method lacks theoretical basis.

[0007] Patent CN116542141A discloses a system and method for evaluating the service life of DC motors used in electric ovens. It predicts the service life of DC motors using a gated cycle model and a long short-term memory model. However, this single motor life prediction calculation model is only applicable to DC motors used in electric ovens, thus its application scope is significantly limited. Summary of the Invention

[0008] The purpose of this invention is to provide a method for predicting motor lifespan. By simulating and statistically analyzing different components of the motor, the reference rate of change required to predict the motor lifespan is obtained, thereby predicting the motor lifespan in real time.

[0009] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows:

[0010] A method for predicting motor life includes the following steps:

[0011] Step S1. Perform simulation tests on multiple components of the simulated motor to obtain the rate of change of the simulation attributes for each component;

[0012] Step S2. Perform simulation tests on the entire simulated motor to obtain the rate of change of the simulated motor's properties;

[0013] Step S3. Take the weighted average of the change rate of the simulation attribute corresponding to each component and the change rate of the simulation motor attribute to obtain the optimized change rate;

[0014] Step S4. Conduct actual tests on the lossless motor to obtain the actual property change rate of the lossless motor;

[0015] Step S5. Optimize the actual attribute change rate of the lossless motor by optimizing the change rate, and obtain the reference change rate;

[0016] Step S6. Conduct actual tests on multiple damaged motors and statistically obtain the attribute change rate of the damaged motors;

[0017] Step S7. Compare the reference rate of change with the rate of change of the damaged motor's attributes to predict the lifespan of the undamaged motor;

[0018] The simulated motor, the undamaged motor, and the damaged motor are all the same type of motor.

[0019] The present invention provides a method for predicting the lifespan of a motor. By simulating and statistically analyzing multiple components of a simulated motor, and then summarizing and calculating, a reference rate of change required to predict the lifespan of the motor is obtained. Based on the statistically obtained rate of change of attributes of the damaged motor, the lifespan of the undamaged motor is predicted.

[0020] Preferably, the multiple components of the simulated motor include a motor shaft, a rotor, a stator, and bearings; step S1 includes obtaining the resistance change rate of the motor shaft with temperature, the temperature change rate of the motor shaft with voltage, the resistance change rate of the rotor with temperature, the temperature change rate of the rotor with voltage, the resistance change rate of the stator with temperature, the temperature change rate of the stator with voltage, the resistance change rate of the bearing with temperature, and the temperature change rate of the bearing with voltage under simulation testing.

[0021] Step S2 includes obtaining the rate of change of resistance with temperature and the rate of change of temperature with voltage of the simulated motor under simulation test.

[0022] Preferably, step S1 includes the following steps:

[0023] Step Sa1.1. During the simulation test, a constant voltage with a duration of t1 is applied to both ends of the motor shaft, rotor, stator, and bearings of the simulated motor, respectively;

[0024] Step Sa1.2. In the simulation test, the resistance of the motor shaft, rotor, stator and bearing changes with temperature within time t1 are statistically analyzed to obtain the corresponding resistance change rate curves with temperature within time t1.

[0025] Preferably, the motor life prediction method connects power lines to both ends of the motor shaft and installs temperature sensors, applies voltage to the motor through the power lines, and acquires the motor's temperature data through the temperature sensors; step S2 includes the following steps:

[0026] Step Sa2.1. During the simulation test, a constant voltage for a duration of t1 is applied to the power lines connected to both ends of the motor shaft of the simulated motor;

[0027] Step Sa2.2. During the simulation test, the data of the change in resistance of the simulated motor with temperature within time t1 are statistically analyzed to obtain the corresponding curve of the rate of change of resistance of the simulated motor with temperature within time t1.

[0028] Preferably, step S3 includes the following steps:

[0029] Step Sa3.1. Obtain the corresponding first weighting coefficient based on the volume ratio and material thermal conductivity ratio of the motor shaft, rotor, stator, and bearings;

[0030] Step Sa3.2. Based on the first weighting coefficients of the motor shaft, rotor, stator, and bearing, the resistance change rate curves of the motor shaft, rotor, stator, and bearing in the simulation test and the resistance change rate curve of the simulated motor are weighted and averaged to obtain the optimized resistance change rate curve.

[0031] Preferably, step S4 includes:

[0032] Step Sa4.1. In actual testing, apply a constant voltage for a duration of t1 to the power lines connected to both ends of the motor shaft of the non-destructive motor;

[0033] Step Sa4.2. During the actual test, the data on the change of resistance of the non-destructive motor with temperature within time t1 are statistically analyzed to obtain the corresponding curve of the rate of change of resistance of the non-destructive motor with temperature within time t1.

[0034] Step Sa4.3. Perform an arithmetic average of the resistance change rate curve of the lossless motor with temperature and the optimized resistance change rate curve with temperature to obtain a reference curve for the resistance change rate with temperature.

[0035] Preferably, steps S1 to S4 further include the following steps:

[0036] Step Sb1.1. During the simulation test, set the voltage limit value, apply voltages from zero to the limit value to both ends of the simulated motor shaft, rotor, stator, and bearings respectively, and record the time t2 taken.

[0037] Step Sb1.2. In the simulation test, the temperature change data of the motor shaft, rotor, stator and bearing with voltage within time t2 are statistically analyzed to obtain the corresponding temperature change rate curve with voltage within time t2;

[0038] Step Sb2.1. During the simulation test, apply a voltage that increases from zero to the limit value for a duration of t2 to the power lines connected to both ends of the simulated motor shaft;

[0039] Step Sb2.2. During the simulation test, the temperature change data of the simulated motor with voltage within time t2 is statistically analyzed to obtain the corresponding rate of change curve of the temperature change of the simulated motor with voltage within time t2;

[0040] Step Sb3.1. Obtain the corresponding second weighting coefficients based on the volume ratio and material resistivity ratio of the motor shaft, rotor, stator, and bearings;

[0041] Step Sb3.2. Based on the second weighting coefficients of the motor shaft, rotor, stator, and bearings, the temperature change rate curves of the motor shaft, rotor, stator, and bearings in the simulation test and the temperature change rate curve of the simulated motor are weighted and averaged to obtain the optimized temperature change rate curve.

[0042] Step Sb4.1. In actual testing, apply a voltage that increases from zero to the limit value for a duration of t2 to the power lines connected to both ends of the motor shaft of the non-destructive motor.

[0043] Step Sb4.2. During the actual test, the temperature change data of the non-destructive motor with voltage within time t2 is statistically analyzed to obtain the corresponding temperature change rate curve of the non-destructive motor with voltage within time t2.

[0044] Step Sb4.3. Perform an arithmetic average of the temperature change rate curve of the lossless motor with voltage and the optimized temperature change rate curve with voltage to obtain a reference curve for the temperature change rate with voltage.

[0045] Preferably, step S6 includes: conducting actual tests on multiple damaged motors, and statistically obtaining the resistance change rate curve and the temperature change rate curve of the damaged motors.

[0046] Preferably, step S7 includes: comparing the reference curve of resistance change rate with temperature and the curve of resistance change rate with temperature of the damaged motor, and simultaneously comparing the reference curve of temperature change rate with voltage and the curve of temperature change rate with voltage of the damaged motor to predict the motor life of the undamaged motor.

[0047] Preferably, the motor life prediction method further includes: obtaining warning thresholds for resistance changing with temperature and temperature changing with voltage based on the resistance change rate curve and temperature change rate curve of the damaged motor; acquiring resistance data, temperature data, and voltage data of the undamaged motor in real time and simultaneously calculating the corresponding resistance change rate and temperature change rate; and issuing an alarm to remind the motor to be replaced when the real-time calculated resistance change rate with temperature is within the warning threshold for resistance changing with temperature and the real-time calculated temperature change rate with voltage is also within the warning threshold for temperature changing with voltage.

[0048] The beneficial effects of this invention are as follows:

[0049] 1. A method for predicting motor lifespan, which utilizes simulation and statistical analysis combined with resistance testing and temperature rise testing, can provide theoretical support for motor lifespan, minimize the problem of insufficient sample size caused by statistical analysis alone, and make lifespan prediction more accurate.

[0050] 2. A method for predicting motor lifespan, which can acquire the operating parameters of the motor in real time and calculate the rate of change of the operating parameters. When the rate of change of the operating parameters is within the range of the warning threshold, an alarm is issued to remind the motor to be replaced, which can greatly reduce energy waste and reduce the user's operating costs.

[0051] 3. A method for predicting motor lifespan, which can maximize the control of motor lifespan and provide designers with a reliable basis for design. Attached Figure Description

[0052] Figure 1 The above is a flowchart of a motor life prediction method according to Embodiment 1;

[0053] Figure 2 The diagram shown is a first sub-flowchart of a motor life prediction method according to Embodiment 1.

[0054] Figure 3 The following is a second sub-flowchart of a motor life prediction method according to Embodiment 1;

[0055] Figure 4 The figure shows the rate of change of resistance of the motor shaft, rotor, stator, and bearings with temperature during the simulation test of Example 2.

[0056] Figure 5 The figure shows the curve of the rate of change of the optimized resistance with temperature in Example 2;

[0057] Figure 6 The figure shows the rate of change of resistance of the non-destructive motor with temperature during actual testing in Example 2.

[0058] Figure 7 The figure shows a reference curve of the resistance change rate with temperature in Example 2;

[0059] Figure 8 The figure shows the temperature change rate curves of the motor shaft, rotor, stator, and bearings as a function of voltage during the simulation test of Example 2.

[0060] Figure 9 The figure shows the optimized temperature versus voltage rate curve for Example 2;

[0061] Figure 10 The figure shows the rate of change of temperature of the non-destructive motor with voltage during actual testing in Example 2;

[0062] Figure 11 The figure shows a reference curve of the rate of temperature change with voltage in Example 2.

[0063] Figure Labels

[0064] 101. Curve showing the rate of change of motor shaft resistance with temperature; 102. Curve showing the rate of change of motor shaft temperature with voltage; 201. Curve showing the rate of change of rotor resistance with temperature; 202. Curve showing the rate of change of rotor temperature with voltage; 301. Curve showing the rate of change of stator resistance with temperature; 302. Curve showing the rate of change of stator temperature with voltage; 401. Curve showing the rate of change of bearing resistance with temperature; 402. Curve showing the rate of change of bearing temperature with voltage; 501. Curve showing the optimized rate of change of resistance with temperature; 502. Curve showing the optimized rate of change of temperature with voltage; 601. Curve showing the rate of change of resistance with temperature in a non-destructive motor; 602. Curve showing the rate of change of temperature with voltage in a non-destructive motor; 701. Reference curve showing the change of resistance with temperature; 702. Reference curve showing the change of temperature with voltage. Detailed Implementation

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without creative effort.

[0066] The technical solution of the present invention will be described in detail below with specific embodiments.

[0067] Example 1

[0068] like Figures 1-3 As shown, a method for predicting motor lifespan in this embodiment includes the following steps:

[0069] Step S1. Perform simulation tests on multiple components of the simulated motor to obtain the rate of change of the simulation attributes for each component;

[0070] Step S2. Perform simulation tests on the entire simulated motor to obtain the rate of change of the simulated motor's properties;

[0071] Step S3. Take the weighted average of the change rate of the simulation attribute corresponding to each component and the change rate of the simulation motor attribute to obtain the optimized change rate;

[0072] Step S4. Conduct actual tests on the lossless motor to obtain the actual property change rate of the lossless motor;

[0073] Step S5. Optimize the actual attribute change rate of the lossless motor by optimizing the change rate, and obtain the reference change rate;

[0074] Step S6. Conduct actual tests on multiple damaged motors and statistically obtain the attribute change rate of the damaged motors;

[0075] Step S7. Compare the reference rate of change with the rate of change of the damaged motor's attributes to predict the lifespan of the undamaged motor;

[0076] Simulated motors, undamaged motors, and damaged motors are all the same type of motor.

[0077] This embodiment of a motor life prediction method involves simulating and statistically analyzing multiple components of a simulated motor, then summarizing and calculating the results to obtain the reference change rate required to predict the motor life. Based on the statistically obtained attribute change rate of the damaged motor, the life of the undamaged motor is predicted.

[0078] Preferably, the multiple components of the simulated motor include a motor shaft, a rotor, a stator, and bearings; step S1 includes obtaining the resistance change rate of the motor shaft with temperature, the temperature change rate of the motor shaft with voltage, the resistance change rate of the rotor with temperature, the temperature change rate of the rotor with voltage, the resistance change rate of the stator with temperature, the temperature change rate of the stator with voltage, the resistance change rate of the bearing with temperature, and the temperature change rate of the bearing with voltage under the simulation test.

[0079] Step S2 includes obtaining the rate of change of resistance with temperature and the rate of change of temperature with voltage of the simulated motor under simulation test.

[0080] Preferably, step S1 includes the following steps:

[0081] Step Sa1.1. During the simulation test, a constant voltage with a duration of t1 is applied to both ends of the motor shaft, rotor, stator, and bearings of the simulated motor, respectively;

[0082] Step Sa1.2. In the simulation test, the resistance of the motor shaft, rotor, stator and bearing changes with temperature within time t1 are statistically analyzed to obtain the corresponding resistance change rate curves with temperature within time t1.

[0083] Preferably, a method for predicting motor lifespan involves connecting power lines to both ends of the motor shaft and installing temperature sensors, applying voltage to the motor via the power lines, and acquiring the motor's temperature data via the temperature sensors; step S2 includes the following steps:

[0084] Step Sa2.1. During the simulation test, a constant voltage for a duration of t1 is applied to the power lines connected to both ends of the motor shaft of the simulated motor;

[0085] Step Sa2.2. During the simulation test, the data of the change in resistance of the simulated motor with temperature within time t1 are statistically analyzed to obtain the corresponding curve of the rate of change of resistance of the simulated motor with temperature within time t1.

[0086] Preferably, step S3 includes the following steps:

[0087] Step Sa3.1. Obtain the corresponding first weighting coefficient based on the volume ratio and material thermal conductivity ratio of the motor shaft, rotor, stator, and bearings;

[0088] Step Sa3.2. Based on the first weighting coefficients of the motor shaft, rotor, stator, and bearing, the resistance change rate curves of the motor shaft, rotor, stator, and bearing in the simulation test and the resistance change rate curve of the simulated motor are weighted and averaged to obtain the optimized resistance change rate curve.

[0089] Preferably, step S4 includes:

[0090] Step Sa4.1. In actual testing, apply a constant voltage for a duration of t1 to the power lines connected to both ends of the motor shaft of the non-destructive motor;

[0091] Step Sa4.2. During the actual test, the data on the change of resistance of the non-destructive motor with temperature within time t1 are statistically analyzed to obtain the corresponding curve of the rate of change of resistance of the non-destructive motor with temperature within time t1.

[0092] Step Sa4.3. Perform an arithmetic average of the resistance change rate curve of the lossless motor with temperature and the optimized resistance change rate curve with temperature to obtain a reference curve for the resistance change rate with temperature.

[0093] Preferably, steps S1 to S4 further include the following steps:

[0094] Step Sb1.1. During the simulation test, set the voltage limit value, apply voltages from zero to the limit value to both ends of the simulated motor shaft, rotor, stator, and bearings respectively, and record the time t2 taken.

[0095] Step Sb1.2. In the simulation test, the temperature change data of the motor shaft, rotor, stator and bearing with voltage within time t2 are statistically analyzed to obtain the corresponding temperature change rate curve with voltage within time t2;

[0096] Step Sb2.1. During the simulation test, apply a voltage that increases from zero to the limit value for a duration of t2 to the power lines connected to both ends of the simulated motor shaft;

[0097] Step Sb2.2. During the simulation test, the temperature change data of the simulated motor with voltage within time t2 is statistically analyzed to obtain the corresponding rate of change curve of the temperature change of the simulated motor with voltage within time t2;

[0098] Step Sb3.1. Obtain the corresponding second weighting coefficients based on the volume ratio and material resistivity ratio of the motor shaft, rotor, stator, and bearings;

[0099] Step Sb3.2. Based on the second weighting coefficients of the motor shaft, rotor, stator, and bearings, the temperature change rate curves of the motor shaft, rotor, stator, and bearings in the simulation test and the temperature change rate curve of the simulated motor are weighted and averaged to obtain the optimized temperature change rate curve.

[0100] Step Sb4.1. In actual testing, apply a voltage that increases from zero to the limit value for a duration of t2 to the power lines connected to both ends of the motor shaft of the non-destructive motor.

[0101] Step Sb4.2. During the actual test, the temperature change data of the non-destructive motor with voltage within time t2 is statistically analyzed to obtain the corresponding temperature change rate curve of the non-destructive motor with voltage within time t2.

[0102] Step Sb4.3. Perform an arithmetic average of the temperature change rate curve of the lossless motor with voltage and the optimized temperature change rate curve with voltage to obtain a reference curve for the temperature change rate with voltage.

[0103] Preferably, step S6 includes: conducting actual tests on multiple damaged motors, and statistically obtaining the resistance change rate curve and the temperature change rate curve of the damaged motors.

[0104] Preferably, step S7 includes: comparing the reference curve of the resistance change rate with temperature and the curve of the resistance change rate with temperature of the damaged motor, and simultaneously comparing the reference curve of the temperature change rate with voltage and the curve of the temperature change rate with voltage of the damaged motor to predict the motor life of the undamaged motor.

[0105] Preferably, a motor life prediction method further includes: obtaining warning thresholds for resistance changing with temperature and temperature changing with voltage based on the resistance change rate curve and temperature change rate curve of the damaged motor; acquiring resistance data, temperature data, and voltage data of the undamaged motor in real time and simultaneously calculating the corresponding resistance change rate and temperature change rate; and issuing an alarm to remind the motor to be replaced when the real-time calculated resistance change rate with temperature is within the warning threshold for resistance changing with temperature and the real-time calculated temperature change rate with voltage is also within the warning threshold for temperature changing with voltage.

[0106] Specifically, in one embodiment of the motor life prediction method, power lines are connected to both ends of the motor shafts of the simulated motor, the undamaged motor, and the damaged motor, and temperature sensors are installed. Voltage is applied to them through the power lines, and temperature data is obtained through the temperature sensors.

[0107] Specifically, the main causes of motor damage are changes in the resistance of components and changes in thermal stress due to temperature changes, which in turn cause deformation, disruption of dynamic balance, bearing wear, evaporation of lubricating oil, and reduced efficiency of electronic components.

[0108] This embodiment of a motor life prediction method utilizes simulation and statistical analysis, combined with resistance testing and temperature rise testing. First, in the simulation test, the motor shaft, rotor, stator, and bearings of the simulated motor are simulated and analyzed to obtain the rate of change of resistance and the rate of change of temperature rise under the dual effects of the magnetic field and heat generated under the working voltage. Then, by passing current through the power lines at both ends of the simulated motor, the overall resistance change rate and temperature rise rate of the simulated motor are simulated (since all components on the motor need to be connected to the motor shaft, current only needs to be passed through the power lines on both sides of the motor shaft). The resistance change rate and temperature rise rate of each individual component are weighted and averaged with the overall resistance change rate and temperature rise rate of the simulated motor to obtain the final optimized resistance change rate and optimized temperature rise rate.

[0109] Next, the resistance change rate and temperature rise rate of the newly manufactured undamaged motor are detected. The optimized resistance change rate and optimized temperature rise rate are then arithmetically averaged with the resistance change rate and temperature rise rate of the undamaged motor to obtain the reference resistance change rate and reference temperature rise rate of the undamaged motor. Then, using the three existing methods, multiple damaged motors are selected, and working voltage is applied to both ends of the motor shaft. The resistance change rate and temperature rise rate of the damaged motor are statistically obtained. When the resistance change rate and temperature rise rate of the undamaged motor approach those of the damaged motor, it indicates that the undamaged motor is about to fail, and an alarm is issued to remind the motor to be replaced.

[0110] Example 2

[0111] like Figures 4-11 As shown, the motor life prediction method in this embodiment is a specific implementation process of the motor life prediction method in Embodiment 1, and the process is as follows:

[0112] 1. By applying a constant voltage to both ends of the simulated motor shaft, rotor, stator, and bearings through simulation, the following results are obtained: Figure 4 The resistance change rate curve shown is used to simulate the overall resistance change rate of the simulated motor by passing current through the power lines at both ends of the simulated motor. The data for each change rate over a fixed number of hours (every 10,000 hours in this example) are then weighted and averaged to obtain a new resistance change rate, as shown below. Figure 5 As shown; then, by testing the resistance change rate of the lossless motor, the following results were obtained: Figure 6 The graph shown. Finally, through Figure 5 and Figure 6 The arithmetic mean of the key time points yields the final rate of change of resistance, as follows: Figure 7 As shown.

[0113] 2. Through simulation, the motor shaft, rotor, stator, and bearings of the simulated motor are subjected to voltages that increase incrementally from zero to the maximum allowable voltage according to national standards at both ends, and the results are as follows: Figure 8 The temperature rise rate curve shown is used to simulate the overall temperature rise rate of the simulated motor by applying a voltage that increases from zero to the maximum allowable voltage according to national standards to the power lines at both ends of the simulated motor. The data for each rate of change over a fixed number of hours (every 10,000 hours in this example) are then weighted and averaged to obtain a new temperature rise rate, as shown below. Figure 9 As shown. Next, the temperature rise rate of the non-destructive motor was tested to obtain the following results: Figure 10 The graph shown. Finally, through Figure 9 and Figure 10 The arithmetic mean of the key time points yields the final rate of temperature change, as follows: Figure 11 As shown.

[0114] 3. Subsequently, resistivity and temperature rise rate tests were conducted on a large number of damaged motors, yielding a wealth of data, including the rate of change after different periods of use. The process for predicting the lifespan of the undamaged motor is as follows: If the temperature change rate of the undamaged motor is 3.8℃ / A at 20,000 hours, and the highest rate of change among the damaged motors that failed at 20,000 hours is 4℃ / A, then the theoretical maximum lifespan of the undamaged motor is 20,000 hours.

[0115] 4. If both rates of change of the motor are within the warning threshold during the operation of the motor, an alarm will be issued to remind the motor to be replaced.

[0116] The embodiments of the motor life prediction method provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention, and the descriptions of the embodiments above are only for the purpose of helping to understand the core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A method for predicting motor lifespan, characterized in that, Includes the following steps: Step S1. Perform simulation tests on multiple components of the simulated motor to obtain the rate of change of the simulation attributes for each component; Step S2. Perform simulation tests on the entire simulated motor to obtain the rate of change of the simulated motor's properties; Step S3. Take a weighted average of the change rate of the simulation attribute corresponding to each component and the change rate of the attribute of the simulation motor to obtain the optimized change rate curve; the optimized change rate curve includes the optimized resistance change rate curve and the optimized temperature rise change rate curve. Step S4. When the lossless motor is first manufactured, it is actually tested to obtain the actual property change rate curve of the lossless motor; the actual property change rate curve of the lossless motor includes the resistance change rate curve and the temperature rise change rate curve. Step S5. Optimize the actual attribute change rate of the lossless motor by optimizing the change rate to obtain a reference change rate curve; the reference change rate curve includes a reference resistance change rate curve and a reference temperature rise change rate curve; Step S6. Perform actual tests on multiple damaged motors and statistically obtain the attribute change rate of the damaged motors. The attribute change rate of the damaged motors includes the resistance change rate curve with temperature and the temperature change rate curve with voltage. Step S7. Real-time acquisition of resistance, temperature, and voltage data of the non-destructive motor under test during operation, and simultaneous calculation of the corresponding resistance versus temperature and temperature versus voltage rate curves. The real-time calculated resistance versus temperature rate curve of the non-destructive motor under test is compared with the reference resistance versus temperature rate curve of the non-destructive motor and the resistance versus temperature rate curve of the damaged motor, respectively. At the same time, the real-time calculated temperature versus voltage rate curve of the non-destructive motor under test is compared with the reference temperature rise versus voltage rate curve of the non-destructive motor and the temperature versus voltage rate curve of the damaged motor, respectively, to predict the motor life of the non-destructive motor. The simulated motor, the undamaged motor, and the damaged motor are all the same type of motor.

2. The method for predicting motor lifespan according to claim 1, characterized in that, The simulated motor comprises multiple components including a motor shaft, a rotor, a stator, and bearings; step S1 includes obtaining the resistance change rate of the motor shaft with temperature, the temperature change rate of the motor shaft with voltage, the resistance change rate of the rotor with temperature, the temperature change rate of the rotor with voltage, the resistance change rate of the stator with temperature, the temperature change rate of the stator with voltage, the resistance change rate of the bearing with temperature, and the temperature change rate of the bearing with voltage under simulation testing. Step S2 includes obtaining the rate of change of resistance with temperature and the rate of change of temperature with voltage of the simulated motor under simulation test.

3. The method for predicting motor lifespan according to claim 2, characterized in that, Step S1 includes the following steps: Step Sa1.

1. During the simulation test, a constant voltage with a duration of t1 is applied to both ends of the motor shaft, rotor, stator, and bearings of the simulated motor, respectively; Step Sa1.

2. In the simulation test, the resistance of the motor shaft, rotor, stator and bearing changes with temperature within time t1 are statistically analyzed to obtain the corresponding resistance change rate curves with temperature within time t1.

4. The method for predicting motor life according to claim 3, characterized in that, The motor life prediction method involves connecting power lines to both ends of the motor shaft and installing temperature sensors, applying voltage to the motor via the power lines, and acquiring the motor's temperature data via the temperature sensors; step S2 includes the following steps: Step Sa2.

1. During the simulation test, a constant voltage for a duration of t1 is applied to the power lines connected to both ends of the motor shaft of the simulated motor; Step Sa2.

2. During the simulation test, the data of the change in resistance of the simulated motor with temperature within time t1 are statistically analyzed to obtain the corresponding curve of the rate of change of resistance of the simulated motor with temperature within time t1.

5. The method for predicting motor life according to claim 4, characterized in that, Step S3 includes the following steps: Step Sa3.

1. Obtain the corresponding first weighting coefficient based on the volume ratio and material thermal conductivity ratio of the motor shaft, rotor, stator, and bearings; Step Sa3.

2. Based on the first weighting coefficients of the motor shaft, rotor, stator, and bearing, the resistance change rate curves of the motor shaft, rotor, stator, and bearing in the simulation test and the resistance change rate curve of the simulated motor are weighted and averaged to obtain the optimized resistance change rate curve.

6. The method for predicting motor life according to claim 5, characterized in that, Step S4 includes: Step Sa4.

1. In actual testing, apply a constant voltage for a duration of t1 to the power lines connected to both ends of the motor shaft of the factory-made, undamaged motor. Step Sa4.

2. During actual testing, statistically analyze the resistance change data of the undamaged motor when it was just manufactured within time t1 as a function of temperature, and obtain the corresponding resistance change rate curve of the undamaged motor as a function of temperature within time t1. Step Sa4.

3. Take the arithmetic average of the resistance change rate curve of the undamaged motor when it is first manufactured and the optimized resistance change rate curve with temperature to obtain the reference curve of resistance change rate with temperature.

7. The method for predicting motor life according to claim 6, characterized in that, Step S1 further includes the following steps: Step Sb1.

1. During the simulation test, set the voltage limit value, apply voltages from zero to the limit value to both ends of the simulated motor shaft, rotor, stator, and bearings respectively, and record the time t2 taken. Step Sb1.

2. In the simulation test, the temperature change data of the motor shaft, rotor, stator and bearing with voltage within time t2 are statistically analyzed to obtain the corresponding temperature change rate curve with voltage within time t2; Furthermore, step S2 further includes the following steps: Step Sb2.

1. During the simulation test, apply a voltage that increases from zero to the limit value for a duration of t2 to the power lines connected to both ends of the simulated motor shaft; Step Sb2.

2. During the simulation test, the temperature change data of the simulated motor with voltage within time t2 is statistically analyzed to obtain the corresponding rate of change curve of the temperature of the simulated motor with voltage within time t2.

8. The method for predicting motor life according to claim 6, characterized in that, Step S3 further includes the following steps: Step Sb3.

1. Obtain the corresponding second weighting coefficients based on the volume ratio and material resistivity ratio of the motor shaft, rotor, stator, and bearings; Step Sb3.

2. Based on the second weighting coefficients of the motor shaft, rotor, stator, and bearings, the temperature change rate curves of the motor shaft, rotor, stator, and bearings in the simulation test and the temperature change rate curve of the simulated motor are weighted and averaged to obtain the optimized temperature change rate curve.

9. The method for predicting motor life according to claim 6, characterized in that, Step S4 further includes the following steps: Step Sb4.

1. In actual testing, apply a voltage that increases from zero to the limit value for a duration of t2 to the power lines connected to both ends of the motor shaft of the factory-made, undamaged motor. Step Sb4.

2. During actual testing, statistically analyze the temperature change data of the undamaged motor as a function of voltage within time t2 when it is fresh out of the factory, and obtain the corresponding temperature change rate curve of the undamaged motor as a function of voltage within time t2. Step Sb4.

3. Take the arithmetic average of the temperature change rate curve of the undamaged motor when it is first manufactured and the optimized temperature change rate curve with voltage to obtain the reference curve of temperature change rate with voltage.

10. The method for predicting motor life according to claim 1, characterized in that, The motor life prediction method further includes: obtaining the early warning thresholds for resistance changing with temperature and temperature changing with voltage based on the resistance change rate curve and temperature change rate curve of the damaged motor; acquiring the resistance data, temperature data, and voltage data of the undamaged motor in real time and simultaneously calculating the corresponding resistance change rate and temperature change rate; and issuing an alarm to remind the motor to be replaced when the real-time calculated resistance change rate with temperature is within the early warning threshold and the real-time calculated temperature change rate with voltage is also within the early warning threshold.

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