CN110794301A - Motor life value judgment method - Google Patents
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- CN110794301A CN110794301A CN201910969099.9A CN201910969099A CN110794301A CN 110794301 A CN110794301 A CN 110794301A CN 201910969099 A CN201910969099 A CN 201910969099A CN 110794301 A CN110794301 A CN 110794301A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
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Abstract
The invention discloses a motor life value judgment method, which comprises the following steps: collecting self factors and external factors which influence the life value of the motor; sampling and collecting factors influencing the life value of the motor by using a control variable method, namely collecting motor current data, and determining the light load/heavy load time of the motor based on the motor current data; collecting motor temperature data and vibration data, and determining the temperature rise change between the coil temperature and the environment temperature of the motor in heavy load and light load states and the vibration amplitude of the bearing; acquiring data of flow and pressure generated by motor dragging equipment, and determining the influence of a load on a motor; monitoring the insulation value of the motor coil to the ground and the aging degree of an insulation medium; and (3) carrying out grading evaluation on the motor by taking time as an axis through an association formula, an association map and a deep learning mode. The method and the system predict the service life value of the motor, help a user to overhaul the motor in advance and find out a targeted overhaul direction, and reduce loss.
Description
Technical Field
The invention relates to the field of motors, in particular to a method for judging a life value of a motor.
Background
At present, the motor is widely applied as an independent product or a core part, especially in the industrial manufacturing industry, the motor is widely used in industrial products, and the motor has irreplaceable functions as the core part of the industrial products. In order to ensure the reliability and performance index of the motor operation, the life value of the motor needs to be tested, in the prior art, the endurance test is carried out on the motor, and the endurance test mode is mainly used for testing the actual service life value of the motor. The motor health condition is predicted and analyzed, potential fault hazards which may exist are found in time, and the motor safety monitoring system has very important practical significance and economic value for safe operation of the motor.
Disclosure of Invention
The present invention is directed to a method for determining a life value of a motor, so as to solve the problems mentioned in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a motor life value judging method comprises the following steps:
s1: collecting self factors and external factors which influence the life value of the motor, wherein the self factors comprise motor current data, motor temperature data and vibration data, flow and pressure data generated by motor dragging equipment, the insulation value of a motor coil to the ground and the aging degree of an insulation medium, and the external factors comprise the temperature and the humidity of the environment where the motor is located;
s2: sampling and collecting factors influencing the life value of the motor by using a control variable method, namely collecting motor current data, and determining the light load/heavy load time of the motor based on the motor current data;
s3: collecting motor temperature data and vibration data based on S2, and determining temperature rise change between coil temperature and environment temperature and bearing vibration amplitude of the motor under heavy load and light load conditions, namely determining the vibration amplitude of the bearing under heavy load and the vibration amplitude under light load;
s4: collecting data of flow and pressure generated by motor dragging equipment based on S2, and determining the influence of load on the motor;
s5: based on the insulation value of the motor coil to the ground and the aging degree of the insulation medium under the states of S2, S3 and S4, respectively, the physical condition of the motor under the states is determined;
s6: and (3) grading judgment and evaluation are carried out on the motor by taking time as an axis through an association formula, an association map and a deep learning mode.
Preferably, the collection of the insulation value of the motor coil to the ground and the aging degree of the insulation medium in S5 includes the insulation value, the absorption ratio and the polarization index.
Preferably, the deep learning includes a convolutional neural network, a deep belief network, a recurrent neural network, a stacked self-encoder, a deep boltzmann machine, a long-short term memory model, a gated cyclic unit network, and a neuropilin machine.
Preferably, the specific operation steps in S6 include:
the method comprises the following steps: acquiring test data, wherein the test data comprises an insulation value, an absorption ratio and a polarization index, analyzing and filtering the test data, eliminating outliers, and determining parameters influencing the life value of the motor;
step two: establishing a motor life value prediction model, wherein the model formula is as follows: gamma is A exp (-mt), wherein gamma is the service life, t is the temperature, and A, m are intrinsic material characteristic constants;
step three: performing precision check judgment, wherein the precision check comprises single parameter threshold judgment on the current insulation value, absorption ratio and polarization index test results, comparing the single parameter detection result with respective thresholds, and judging that the motor is seriously degraded if any one parameter exceeds the threshold;
step four: and determining a comment set based on the motor life value prediction model and precision check judgment, wherein the comment set comprises good, warning and danger, obtaining a final multi-parameter fuzzy comprehensive evaluation result according to a maximum membership principle, and performing grading evaluation of three grades of good, warning and danger on the motor.
Preferably, the insulation value of the motor coil to the ground and the aging degree of the insulation medium are judged based on insulation resistance, absorption ratio and polarization index tests and insulation resistance tests of the rotating electrical machine, wherein the insulation resistance is determined according to national standard:
GB/T20160-2006 (insulation resistance test for rotating electrical machines)
JB/T10098-2000 (AC motor stator forming coil impulse voltage resistance level)
GB/T16927.1-1997 (first part of high Voltage test technology: general test requirements)
DL/T474.1 ~ 5-2006 (insulation resistance, absorption ratio and polarization index test)
DL/T596 (preventive test protocol for electric power equipment)
IEEE Std 62-1995 (first part of field diagnosis test of IEEE power equipment: oil type transformer, voltage regulator and reactor)
IEEE Std 95-1977 (recommendation method for IEEE large AC rotating machine insulation high voltage direct current test)
IEEE Std 432-1992 (IEEE rotating electrical machine insulation maintenance guide 5 hp-10000 hp)
IEEE Std 433-1974 (recommended method for IEEE insulation test)
IEEE Std 434-1973 (IEEE large-scale high-voltage motor insulation system function type evaluation guide rule)
IEEE Std 510-1983 (IEEE high-voltage high-power test safety recommendation method).
Preferably, the determination of the physical condition of the motor further includes comparing the physical condition with an initial value of the insulation and voltage resistance of the motor at the time of shipment.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, relevant factors such as current, frequency, coil temperature, vibration amplitude, complete machine insulation value, absorption ratio, polarization index, pressure, flow and liquid level are collected to realize relevant calculation and relevant analysis, and a relevant formula, a relevant map and a deep learning mode are adopted to carry out life value and fault analysis, namely the length of the life value used by the motor is predicted, so that a user is helped to overhaul the motor in advance and find out a targeted overhaul direction, and the loss is reduced.
Drawings
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "vertical", "upper", "lower", "horizontal", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, the present invention provides a technical solution: a motor life value judging method comprises the following steps:
s1: collecting self factors and external factors which influence the life value of the motor, wherein the self factors comprise motor current data, motor temperature data and vibration data, flow and pressure data generated by motor dragging equipment, the insulation value of a motor coil to the ground and the aging degree of an insulation medium, and the external factors comprise the temperature and the humidity of the environment where the motor is located;
s2: sampling and collecting factors influencing the life value of the motor by using a control variable method, namely collecting motor current data, and determining the light load/heavy load time of the motor based on the motor current data;
s3: collecting motor temperature data and vibration data based on S2, and determining temperature rise change between coil temperature and environment temperature and bearing vibration amplitude of the motor under heavy load and light load conditions, namely determining the vibration amplitude of the bearing under heavy load and the vibration amplitude under light load;
s4: collecting data of flow and pressure generated by motor dragging equipment based on S2, and determining the influence of load on the motor;
s5: based on the insulation value of the motor coil to the ground and the aging degree of the insulation medium under the states of S2, S3 and S4, respectively, the physical condition of the motor under the states is determined;
s6: and (3) grading judgment and evaluation are carried out on the motor by taking time as an axis through an association formula, an association map and a deep learning mode.
In the step S5, the collection of the insulation value of the motor coil to the ground and the aging degree of the insulation medium comprises the insulation value, the absorption ratio and the polarization index.
The specific operation steps in the S6 include:
the method comprises the following steps: acquiring test data, wherein the test data comprises an insulation value, an absorption ratio and a polarization index, analyzing and filtering the test data, eliminating outliers, and determining parameters influencing the life value of the motor;
step two: establishing a motor life value prediction model, wherein the model formula is as follows: gamma is A exp (-mt), wherein gamma is the service life, t is the temperature, and A, m are intrinsic material characteristic constants;
step three: performing precision check judgment, wherein the precision check comprises single parameter threshold judgment on the current insulation value, absorption ratio and polarization index test results, comparing the single parameter detection result with respective thresholds, and judging that the motor is seriously degraded if any one parameter exceeds the threshold;
step four: and determining a comment set based on the motor life value prediction model and precision check judgment, wherein the comment set comprises good, warning and danger, obtaining a final multi-parameter fuzzy comprehensive evaluation result according to a maximum membership principle, and performing grading evaluation of three grades of good, warning and danger on the motor.
The deep learning comprises a convolutional neural network, a deep belief network, a recurrent neural network, a stacked self-encoder, a deep boltzmann machine, a long-short term memory model, a gated cyclic unit network and a neural turing machine.
The insulation value of the motor coil to the ground and the aging degree of the insulation medium are judged based on insulation resistance, absorption ratio and polarization index tests and rotary motor insulation resistance tests, wherein the insulation value is determined according to national standards:
GB/T20160-2006 (insulation resistance test for rotating electrical machines)
JB/T10098-2000 (AC motor stator forming coil impulse voltage resistance level)
GB/T16927.1-1997 (first part of high Voltage test technology: general test requirements)
DL/T474.1 ~ 5-2006 (insulation resistance, absorption ratio and polarization index test)
DL/T596 (preventive test protocol for electric power equipment)
IEEE Std 62-1995 (first part of field diagnosis test of IEEE power equipment: oil type transformer, voltage regulator and reactor)
IEEE Std 95-1977 (recommendation method for IEEE large AC rotating machine insulation high voltage direct current test)
IEEE Std 432-1992 (IEEE rotating electrical machine insulation maintenance guide 5 hp-10000 hp)
IEEE Std 433-1974 (recommended method for IEEE insulation test)
IEEE Std 434-1973 (IEEE large-scale high-voltage motor insulation system function type evaluation guide rule)
IEEE Std 510-1983 (IEEE high-voltage high-power test safety recommendation method).
The judgment of the physical condition of the motor also comprises comparison with the initial values of insulation and voltage resistance when the motor leaves the factory.
Because the winding insulation is under the effect of direct voltage, four kinds of current are produced: 1. a transient charging current; 2. polarizing or absorbing current; 3. the conduction current of the main insulation; 4. surface leakage current; transient charging current, polarization or sink current components decay with time; the conduction current, surface leakage current of the main insulation is generated due to the presence of moisture, carbide, etc. or defects, and does not change with time.
The judgment of the single parameter threshold in the third step comprises the following steps: without considering the influence of the surface current, if the proportion of the conductance current in the total current increases, the change of the total current with time will decrease. This indicates that the lower the polarization index, the more defects in the insulation and the more severe the aging.
The first step also comprises measuring partial discharge in the stator winding of the motor, namely, the insulation state is known by measuring the partial discharge, and when the insulation is aged or the impregnation process is poor, the partial discharge quantity is obviously increased along with the increase of the applied voltage.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A motor life value judgment method is characterized by comprising the following steps:
s1: collecting self factors and external factors which influence the life value of the motor, wherein the self factors comprise motor current data, motor temperature data and vibration data, flow and pressure data generated by motor dragging equipment, the insulation value of a motor coil to the ground and the aging degree of an insulation medium, and the external factors comprise the temperature and the humidity of the environment where the motor is located;
s2: sampling and collecting factors influencing the life value of the motor by using a control variable method, namely collecting motor current data, and determining the light load/heavy load time of the motor based on the motor current data;
s3: collecting motor temperature data and vibration data based on S2, and determining temperature rise change between coil temperature and environment temperature and bearing vibration amplitude of the motor under heavy load and light load conditions, namely determining the vibration amplitude of the bearing under heavy load and the vibration amplitude under light load;
s4: collecting data of flow and pressure generated by motor dragging equipment based on S2, and determining the influence of load on the motor;
s5: based on the insulation value of the motor coil to the ground and the aging degree of the insulation medium under the states of S2, S3 and S4, respectively, the physical condition of the motor under the states is determined;
s6: and (3) grading judgment and evaluation are carried out on the motor by taking time as an axis through an association formula, an association map and a deep learning mode.
2. The motor life value judgment method according to claim 1, wherein: and in the step S5, the collection of the insulation value of the motor coil to the ground and the aging degree of the insulation medium comprises the insulation value, the absorption ratio and the polarization index.
3. The motor life value judgment method according to claim 1, wherein: the deep learning comprises a convolutional neural network, a deep belief network, a recurrent neural network, a stacked self-encoder, a deep boltzmann machine, a long-short term memory model, a gated cyclic unit network and a neural turing machine.
4. The motor life value judgment method according to claim 2, characterized in that: the insulation value of the motor coil to the ground and the aging degree of the insulation medium are judged based on insulation resistance, absorption ratio and polarization index tests and a rotating electrical machine insulation resistance test.
5. The motor life value judgment method according to claim 1, wherein: the judgment of the physical condition of the motor further comprises comparison with an initial value of insulation and voltage resistance of the motor when the motor leaves a factory.
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