CN116609652B - Motor health prediction assessment method based on crane motor vibration temperature signal - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 59
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- 238000012544 monitoring process Methods 0.000 claims abstract description 31
- 238000004458 analytical method Methods 0.000 claims abstract description 26
- 230000005540 biological transmission Effects 0.000 claims abstract description 7
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- 238000004804 winding Methods 0.000 claims description 15
- 230000002159 abnormal effect Effects 0.000 claims description 14
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 230000003111 delayed effect Effects 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 abstract description 6
- 230000008901 benefit Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
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- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
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- 238000010438 heat treatment Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
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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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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K3/00—Thermometers giving results other than momentary value of temperature
- G01K3/08—Thermometers giving results other than momentary value of temperature giving differences of values; giving differentiated values
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/12—Measuring rate of change
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
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Abstract
The invention discloses a motor health prediction evaluation method based on a crane motor vibration temperature signal, which comprises the following steps: collecting vibration signals on a transmission bearing of a crane motor by using a vibration sensor, constructing a motor vibration analysis model through the vibration signals, and predicting the health state of the crane motor under the motor vibration analysis model; collecting the temperature of the crane motor in the running process by using a temperature sensor, calculating the average value of the temperature rise of the crane motor, obtaining the standard deviation of the temperature rise of the crane motor in the loading and unloading process, and predicting the health state of the crane motor under the monitoring of the temperature according to the standard deviation of the temperature rise of the crane motor; collecting current and voltage real-time variation in the running process of the crane motor by adopting a current sensor and a voltage sensor, and predicting the health state of the crane motor under current and voltage monitoring; and comprehensively evaluating the health states of the crane motor under the three states, predicting the health states of the crane motor, and providing scientific basis for replacing the motor for maintenance personnel.
Description
Technical Field
The invention belongs to the technical field of port safety, and particularly relates to a motor health prediction and assessment method based on a crane motor vibration temperature signal.
Background
The motor is a core element in a crane driving mechanism, is an indispensable component for the operation of the crane mechanism, and the real-time monitoring of parameters such as impact, vibration, temperature rise, current, voltage, service time, winding insulation and the like of the motor in the use process is fundamental to the safe, stable and reliable operation of the motor, and is particularly important for the health assessment and the service life of the motor. Meanwhile, scientific monitoring of the motor is also the biggest guarantee of cost control and safety benefit. The technical research and development of big data statistics, vibration detection and health evaluation of the motor are also development strategies of green development of wharfs, digital economy and ecological benefits.
On a crane of a port and a dock at the present stage, the faults of a motor are completely random, untimely, uncertain and other factors, which cause great difficulty to maintenance of the motor, and the crane has a large number of motor models and specifications and a complex driving mechanism, and the spare parts bring great capital pressure and cash circulation to the production cost; if spare parts are not advanced, the model specification motor is purchased again after the motor is damaged, and as most of crane driving motors are nonstandard, the purchasing period is long, so that the equipment downtime is long, and the whole production benefit of the wharf is influenced.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a motor health prediction evaluation method based on a crane motor vibration temperature signal, which evaluates the residual life through health detection of parameters such as crane motor vibration, current, voltage, temperature rise and the like, provides a scientific basis for replacing a motor for maintenance personnel, and simultaneously collects data information of a statistical motor to provide reference verification for a motor big data theory.
In order to achieve the technical purpose, the invention adopts the following technical scheme: a motor health prediction evaluation method based on a crane motor vibration temperature signal specifically comprises the following steps:
step 1, collecting vibration signals on a transmission bearing of a crane motor by using a vibration sensor, constructing a motor vibration analysis model through the vibration signals, and predicting the health state of the crane motor under the motor vibration analysis model;
step 2, collecting the temperature of the crane motor in the running process by using a temperature sensor, calculating a temperature rise average value of the crane motor, obtaining a standard deviation of the temperature rise of the crane motor in the loading and unloading process, and predicting the health state of the crane motor under the monitoring of the temperature according to the standard deviation of the temperature rise of the crane motor;
step 3, collecting current and voltage real-time variation in the running process of the crane motor by adopting a current sensor and a voltage sensor, and predicting the health state of the crane motor under current and voltage monitoring;
and 4, comprehensively evaluating the health state of the predicted crane motor under the motor vibration analysis model, predicting the health state of the crane motor under the temperature monitoring according to the standard deviation of the temperature rise of the crane motor, and predicting the health state of the crane motor under the current and voltage monitoring, so as to predict the health state of the crane motor.
Further, a vibration sensor is arranged on a transmission bearing of the crane motor.
Further, the construction process of the motor vibration analysis model in the step 1 is as follows:
v(t)＝f(i 1 ，ω 1 )
wherein v (t) is a collected time domain signal of crane motor vibration, i 1 Is electron current, omega 1 For the number of turns of the stator winding, f () is represented by v (t), i 1 、ω 1 A function of the composition.
Further, in the step 1, if the crane vibration trend curve corresponding to the motor vibration analysis model is normal, predicting that the state of the crane motor under the motor vibration analysis model is healthy; otherwise, predicting that the state of the crane motor under the motor vibration analysis model is abnormal.
Further, a temperature sensor is arranged in the three-phase winding coil of the crane motor, and a temperature sensor is arranged at the front, middle and rear positions of each phase winding coil of the crane motor.
Further, step 2 comprises the following sub-steps:
step S21, when the power circuit of the crane is powered on, acquiring temperature values T collected by each temperature sensor α When the power circuit of the crane is powered off and delayed for 10 minutes, the temperature value T collected by each temperature sensor is read β Obtaining the temperature rise value T of the crane motor in the whole loading and unloading process 1 ＝T β -T α ；
Step S22, under the normal working state of the crane motor, repeating the step S21 for different seasons and different loading and unloading processes to obtain the temperature rise value of the crane motor in each loading and unloading process, and calculating the average value of the temperature rise of the crane motor in each loading and unloading process;
step S23, collecting the temperatures of the power-on process and the power-off process for 10 minutes through temperature sensors in a certain loading and unloading process, solving the temperature rise value of the crane motor under each sensor, and solving the standard deviation of the temperature rise value of the crane motor and the average value of the temperature rise of the crane motor in each loading and unloading process;
s24, if the standard deviation exceeds a set threshold, predicting that the state of the crane motor is abnormal under the monitoring of the temperature in the loading and unloading process; otherwise, predicting the state health of the crane motor under the temperature monitoring.
Further, a group of current sensors and voltage sensors are arranged on cables in a power supply control cabinet of the crane motor.
Further, if the monitored current or voltage is abnormal in the step 3, predicting that the state of the crane motor is abnormal under the current and voltage monitoring; otherwise, predicting the state health of the crane motor under current and voltage monitoring.
Compared with the prior art, the invention has the following beneficial effects: according to the motor health prediction assessment method based on the crane motor vibration temperature signals, health state assessment is carried out through health detection of parameters such as crane motor vibration, current, voltage and temperature rise, comprehensive prediction assessment is carried out on the state of the crane motor in vibration, current, voltage and temperature rise, the assessment result is more accurate, scientific basis is provided for future motor estimated service life and state monitoring, accurate and effective scientific maintenance data are provided for dock maintainers, and safety and reliability of motor service performance are greatly improved.
Drawings
FIG. 1 is a flow chart of a motor health prediction assessment method based on a crane motor vibration temperature signal;
fig. 2 is a schematic view illustrating the installation of a vibration sensor according to the present invention.
Detailed Description
The technical scheme of the invention is further explained below with reference to the accompanying drawings.
Fig. 1 is a diagram showing a motor health prediction and assessment method based on a crane motor vibration temperature signal, which specifically comprises the following steps:
step 1, arranging a vibration sensor on a transmission bearing of a crane motor, as shown in fig. 2, so that the vibration measuring direction is consistent with the vibration direction of the crane motor. Collecting vibration signals on a transmission bearing of a crane motor by using a vibration sensor, constructing a motor vibration analysis model through the vibration signals, and predicting the health state of the crane motor under the motor vibration analysis model, specifically, if a crane vibration trend curve corresponding to the motor vibration analysis model is normal, predicting the health state of the crane motor under the motor vibration analysis model; otherwise, predicting that the state of the crane motor under the motor vibration analysis model is abnormal.
The motor vibration analysis model is based on a motor dragging theory, analyzes the relation between electromagnetic waves and rotor current and stator current, deduces the relation between different running states and vibration frequencies of a motor, combines vibration data collected by a crane, and determines the vibration frequencies of the motor based on wavelet analysis and other methods, thereby evaluating the running states of the motor on line and realizing safe running monitoring and early warning of the motor, and the construction process comprises the following steps:
firstly, after a stator of a three-phase asynchronous motor is connected with a power supply grid, a stator current comprehensive vectorGenerating stator magnetomotive force fundamental wave +.>
Wherein omega 1 For the number of turns, k, of stator windings ω1 And p is the number of pole pairs, which is the harmonic distribution coefficient of the stator.
When the three-phase stator is arranged along the air gap in the anticlockwise direction, because the current phase sequence is positive,will rotate in the counter-clockwise direction at synchronous speed, when the rotor winding is switched on by the starting resistor Rst, the generated rotor current is a three-phase symmetrical current with the same frequency as the stator current +.>Combined generation of rotor magnetomotive force fundamental wave>
Wherein omega 2 For the number of turns, k, of rotor windings ω2 For rotor harmonic distribution coefficient, θ 12 Is the included angle of the corresponding axes of the stator and the rotor.
Due toAnd->The two magnetomotive force have the same pole number, the same rotating speed and the same direction, namely, the two magnetomotive force have relative static space, so that the vector addition can be carried out to calculate the composite excitation magnetomotive force fundamental wave in the air gap, and finally, an air gap magnetic field is generated. The stator current is applied according to the analysis method used by the transformer>Split into excitation current->And torque current component>Two components, i.e.)>Which flows through the stator three-phase winding to generate exciting magnetomotive force +.>
And, satisfy between magnetomotive force:
the following are obtained by simplifying the formulas (1) to (4):
since the rotor current can be deduced from the stator current i, the corresponding function model of the current in the time domain is:
wherein sigma 1 Sum sigma 2 Respectively representing the functional relationship between the currents.
The magnetomotive fundamental wave generates a rotating magnetic field with the same rotating speed and the same steering directionAnd the magnetic field strength of the rotating magnetic field generated by the magnetomotive force of the stator and the rotor +.>The method comprises the following steps of:
wherein, lambda 0 Is an invariable part of air gap magnetic conduction, B m 、B 1 、B 2 Is amplitude and is respectively proportional to current i m 、i、i 2 ，α m 、α 1 、α 2 Respectively are phase and alpha m ＝ω 1 t-α 0 ，α 1 ＝ω 1 t-uα 0 ，α 2 ＝ω 1 t-vα 0 ，α 0 For fundamental wave electric angle, u and v are stator and rotor harmonic frequency, sigma 3 Represents ω 1 、ω 2 The functional relationship between them, therefore, the time domain model is expressed as:
ω 2 (t)＝σ 3 (ω 1 ) (8)
electromagnetic vibration of motor is caused by electromagnetic wave generated by interaction of stator and rotor magnetic fields, and radial electromagnetic force of rotor is utilizedThe alternating force wave generated by the fundamental magnetic field is:
wherein mu 0 The magnetic permeability is a function of the formula,the amplitude of the generated vibrations is proportional to +.>I.e. proportional to +.>2α m In phase, i.e. the frequency of vibration is 2ω 1 The method comprises the steps of carrying out a first treatment on the surface of the The alternating force wave generated by the stator and rotor harmonic magnetic field is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,the amplitude of the generated vibration is proportional to B 1 2 、B 2 2 、2B 1 B 2 I.e. proportional to +.>2i 1 i 2 The method comprises the steps of carrying out a first treatment on the surface of the Phase is 2 alpha 1 、2α 2 、α 1 ±α 2 I.e. vibration frequency 2 omega 1 、2ω 2 、ω 1 +ω 2 . The vector expression is expressed as a function model of a time domain:
wherein h is 1 、h 2 Respectively represent fundamental electromagnetic wave p m1 (t) and stator-rotor alternating force wave p 12 (t) and electricityFunctional relationship between flow, rotational speed. Thus, from formulas (6), (8) and (11):
the analytical model of motor vibration can be expressed as:
v(t)＝f 3 (p m1 (t)，p 12 (t))＝f 3 (f 1 (i 1 ，ω 1 )，f 2 (i 1 ，ω 1 ))＝f(i 1 ，ω 1 ) (13)
wherein f 3 Is a vibration time domain signal v (t) and a fundamental electromagnetic wave p m1 (t) stator-rotor alternating force wave p 12 (t) a functional relationship between; f represents v (t) and i 1 、ω 1 A functional relationship between them.
The formula (13) establishes a time domain analysis model of the vibration signal of the hoisting motor, and the function relation can show that the parameters influencing the vibration are normalized and are mainly the stator current i 1 And the number of turns omega of the stator winding 1 。
Step 2, the temperature rise of the motor plays an important role in the service life of the motor, and is an important parameter performance caused by heating during the operation of the motor, because the temperature rise of the motor is abnormally changed when iron loss or copper loss occurs in a stator core and a rotor winding of the motor. When the temperature rise is increased rapidly, the motor is marked to have faults for some reasons or damage. In order to more accurately detect the temperature and the temperature rise of the motor, a temperature sensor is arranged in a three-phase winding coil of the crane motor, meanwhile, the damage and the accuracy of the temperature sensor are prevented, and the front, the middle and the rear positions of each phase winding coil of the crane motor are respectively provided with the temperature sensor. Collecting the temperature of the crane motor in the running process by using a temperature sensor, calculating the average value of the temperature rise of the crane motor, obtaining the standard deviation of the temperature rise of the crane motor in the loading and unloading process, and predicting the health state of the crane motor under the monitoring of the temperature according to the standard deviation of the temperature rise of the crane motor so as to facilitate a motor maintainer to track and deduce the next motor operation condition and check the reason of the abnormal state of the temperature rise of the motor; the method specifically comprises the following substeps:
step S21, when the power circuit of the crane is powered on, acquiring a temperature value T collected by each temperature sensor α When the power circuit of the crane is powered off and delayed for 10 minutes, the temperature value T collected by each temperature sensor is read β Obtaining a temperature rise value T of a crane motor in the whole loading and unloading process 1 ＝Τ β -Τ α ；
Step S22, under the normal working state of the crane motor, repeating the step S21 for different seasons and different loading and unloading processes to obtain the temperature rise value of the crane motor in each loading and unloading process, and calculating the average value of the temperature rise of the crane motor in each loading and unloading process;
step S23, collecting the temperatures of the power-on process and the power-off process for 10 minutes through temperature sensors in a certain loading and unloading process, solving the temperature rise value of the crane motor under each sensor, and solving the standard deviation of the temperature rise value of the crane motor and the average value of the temperature rise of the crane motor in each loading and unloading process;
s24, if the standard deviation exceeds a set threshold, predicting that the state of the crane motor is abnormal under the monitoring of the temperature in the loading and unloading process; otherwise, predicting the state health of the crane motor under the temperature monitoring.
Step 3, installing a group of current sensors and voltage sensors on cables in a power supply control cabinet of the crane motor, collecting current and voltage real-time variation in the running process of the crane motor by adopting the current sensors and the voltage sensors, and predicting the health state of the crane motor under current and voltage monitoring; if the monitored current or voltage is abnormal, predicting the state abnormality of the crane motor under the current and voltage monitoring; otherwise, predicting the state health of the crane motor under current and voltage monitoring.
And 4, comprehensively evaluating the health state of the predicted crane motor under the motor vibration analysis model, predicting the health state of the crane motor under the temperature monitoring according to the standard deviation of the temperature rise of the crane motor, and predicting the health state of the crane motor under the current and voltage monitoring, wherein the predicted health state of the crane motor is particularly the unrecoverable fault of the motor if the motor is severely abnormal under vibration, temperature, current and voltage conditions, and the motor is likely to be replaced for repair, otherwise, maintenance personnel are required to judge the health state of the crane motor according to the frequency and frequency of the abnormal occurrence.
The health state of the crane motor is comprehensively predicted through the current trend chart, the voltage trend chart, the temperature rise trend histogram and the vibration trend histogram of the crane motor in the loading and unloading process, the evaluation result is more accurate, scientific basis is provided for the predicted service life and state monitoring of the future motor, accurate and effective scientific maintenance data are provided for dock maintainers, and the safety and reliability of the service performance of the motor are greatly improved.
The above is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, and all technical solutions belonging to the concept of the present invention are within the scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (7)
1. A motor health prediction evaluation method based on a crane motor vibration temperature signal is characterized by comprising the following steps:
step 1, collecting vibration signals on a transmission bearing of a crane motor by using a vibration sensor, constructing a motor vibration analysis model through the vibration signals, and predicting the health state of the crane motor under the motor vibration analysis model;
step 2, collecting the temperature of the crane motor in the running process by using a temperature sensor, calculating a temperature rise average value of the crane motor, obtaining a standard deviation of the temperature rise of the crane motor in the loading and unloading process, and predicting the health state of the crane motor under the monitoring of the temperature according to the standard deviation of the temperature rise of the crane motor; the method specifically comprises the following substeps:
the method comprises the following substeps:
step S21, when the power circuit of the crane is powered on, acquiring a temperature value T collected by each temperature sensor α When the power circuit of the crane is powered off and delayed for 10 minutes, the temperature value T collected by each temperature sensor is read β Obtaining a temperature rise value T of a crane motor in the whole loading and unloading process 1 ＝Τ β -Τ α ；
Step S22, under the normal working state of the crane motor, repeating the step S21 for different seasons and different loading and unloading processes to obtain the temperature rise value of the crane motor in each loading and unloading process, and calculating the average value of the temperature rise of the crane motor in each loading and unloading process;
step S23, collecting the temperatures of the power-on process and the power-off process for 10 minutes through temperature sensors in a certain loading and unloading process, solving the temperature rise value of the crane motor under each sensor, and solving the standard deviation of the temperature rise value of the crane motor and the average value of the temperature rise of the crane motor in each loading and unloading process;
s24, if the standard deviation exceeds a set threshold, predicting that the state of the crane motor is abnormal under the monitoring of the temperature in the loading and unloading process; otherwise, predicting the state health of the crane motor under the monitoring of the temperature;
step 3, collecting current and voltage real-time variation in the running process of the crane motor by adopting a current sensor and a voltage sensor, and predicting the health state of the crane motor under current and voltage monitoring;
and 4, comprehensively evaluating the health state of the predicted crane motor under the motor vibration analysis model, predicting the health state of the crane motor under the temperature monitoring according to the standard deviation of the temperature rise of the crane motor, and predicting the health state of the crane motor under the current and voltage monitoring, so as to predict the health state of the crane motor.
2. The motor health prediction assessment method based on the crane motor vibration temperature signal according to claim 1, wherein a vibration sensor is arranged on a transmission bearing of the crane motor.
3. The motor health prediction evaluation method based on the crane motor vibration temperature signal according to claim 2, wherein the construction process of the motor vibration analysis model in step 1 is as follows:
v(t)＝f(i 1 ，ω 1 )
wherein v (t) is a collected time domain signal of crane motor vibration, i 1 For stator current, ω 1 For the number of turns of the stator winding, f () is represented by v (t), i 1 、ω 1 A function of the composition.
4. The motor health prediction assessment method based on the crane motor vibration temperature signal according to claim 3, wherein in the step 1, if a crane vibration trend curve corresponding to the motor vibration analysis model is normal, the state of the crane motor under the motor vibration analysis model is predicted to be healthy; otherwise, predicting that the state of the crane motor under the motor vibration analysis model is abnormal.
5. The motor health prediction assessment method based on the crane motor vibration temperature signal according to claim 1, wherein a temperature sensor is arranged in a three-phase winding coil of the crane motor, and a temperature sensor is arranged at the front, middle and rear positions of each phase winding coil of the crane motor.
6. The motor health prediction assessment method based on the crane motor vibration temperature signal according to claim 1, wherein a group of current sensors and voltage sensors are installed on cables in a power control cabinet of the crane motor.
7. The motor health prediction assessment method based on the crane motor vibration temperature signal according to claim 6, wherein if the monitored current or voltage is abnormal in the step 3, predicting that the state of the crane motor is abnormal under the current or voltage monitoring; otherwise, predicting the state health of the crane motor under current and voltage monitoring.
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