CN109444575A - Motor driving system runs health state evaluation system and method - Google Patents
Motor driving system runs health state evaluation system and method Download PDFInfo
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- CN109444575A CN109444575A CN201811235033.9A CN201811235033A CN109444575A CN 109444575 A CN109444575 A CN 109444575A CN 201811235033 A CN201811235033 A CN 201811235033A CN 109444575 A CN109444575 A CN 109444575A
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- motor
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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
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
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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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The present invention relates to motor drag fields more particularly to motor driving system to run health state evaluation system and method, the system comprises: the first data acquisition module, for acquiring the operation data of the motor in turn(a)round；Second data acquisition module, for acquiring the operation data of the load in turn(a)round；Fault detection module carries out accident analysis for the operation to controller；Neural network model, for carrying out accident analysis according to the operation data of motor and the operation data of load；System grading module, the rule of built-in faulty scoring, for being scored according to the failure analysis result computing system of controller, motor and controller.By using the present invention, following effect may be implemented: can accurately assess the operation health status of motor driving system.
Description
Technical field
The present invention relates to motor drag field more particularly to motor driving system operation health state evaluation system and sides
Method.
Background technique
Motor drag, i.e., using motor as a kind of driving style of prime mover driving machinery equipment moving.Motor drag
System includes sequentially connected controller, motor and load, and controller is by the control to motor, to realize to load
Driving.
Existing there are two ways to motor driving system operation health status is assessed.The first: is according to electronic
Machine and the cumulative operation time of load, if its cumulative operation time close to its working life, judges that it runs health status
It is bad.Second, staff is judged by sense organ, such as to the amplitude and frequency of the vibration of motor or load and its
It is compared when normal work, to judge its working condition.
Obviously, both appraisal procedures can not accurately assess motor driving system operation health status.
Summary of the invention
To solve the above problems, the present invention proposes that motor driving system runs health state evaluation system and method, with standard
The really health status of assessment motor driving system operation.
Motor driving system runs health state evaluation system, and the motor driving system includes sequentially connected control
Device, motor and load, the operation health state evaluation system include:
First data acquisition module, for acquiring the operation data of the motor in turn(a)round；
Second data acquisition module, for acquiring the operation data of the load in turn(a)round；
Fault detection module carries out accident analysis for the operation to controller；
Neural network model, for carrying out accident analysis according to the operation data of motor and the operation data of load；
System grading module, the rule of built-in faulty scoring, for according to controller, motor and the failure of load
The scoring of Analysis result calculation system.
Preferably, first data acquisition module includes: the first current sensor, for when acquiring motor running
Export electric current.
Preferably, first data acquisition module includes: first voltage sensor, for when acquiring motor running
Output voltage.
Preferably, first data acquisition module includes: the first vibrating sensor, for when acquiring motor running
Vibration parameters.
Preferably, first data acquisition module includes: speed probe, is turned when for acquiring motor running
Speed.
Preferably, second data acquisition module includes: the second vibrating sensor, for acquiring vibration when load running
Dynamic parameter.
Preferably, second data acquisition module includes: oil liquid data acquisition module, for acquiring the oil liquid number of load
According to.
The present invention also provides motor driving systems to run health state evaluation method, the described method comprises the following steps:
The rule that failure scores is set in system grading module；
Acquire the operation data of the motor in turn(a)round and the operation data of load；
Judge controller whether break down within the time between overhauls(TBO) alarm or occur Warning alarm；
The operation data of the operation data of motor and load is input to the neural network model after training and carries out failure
Analysis；
System grading module scores according to the result computing system of judging result and accident analysis to controller.
Preferably, the operation data of the motor includes: output electric current, output voltage, vibration parameters.
Preferably, the operation data of the load includes vibration parameters, oil liquid parameter, revolving speed.
By using the present invention, following effect may be implemented: by the first data acquisition module, the second data acquisition module
Acquire the motor in turn(a)round and the operation data of load；It to the operation data of motor and is born by neural network model
The operation data of load carries out accident analysis, output analysis result；Finally by system grading module according to the result of accident analysis
And whether controller alarms computing system scoring, can accurate evaluation motor drag the health status of machine system.
Detailed description of the invention
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
Fig. 1 is the overall structure diagram of motor driving system in the embodiment of the present invention；
Fig. 2 is the module connection diagram of motor driving system operation health state evaluation system in the embodiment of the present invention；
Fig. 3 is the flow chart of motor driving system operation health state evaluation method in the embodiment of the present invention.
Specific embodiment
Below in conjunction with attached drawing, technical scheme of the present invention will be further described, but the present invention is not limited to these realities
Apply example.
As shown in Figure 1, motor driving system includes sequentially connected controller, motor and load, controller passes through
Control to motor, to realize the driving to load.
Before running health state evaluation, need related by a large amount of historical data and the training of experiment porch data
Neural network model.Neural network has self study, self-organizing and adaptivity.The self study of neural network refers to, when outer
When boundary's environment (input) changes, neural network can be through training after a period of time, by inner parameter and structure
Adjustment enables and generates desired output for given input.After neural network model trains, motor is inputted
Operation data, the operation data of load, it can be determined that go out the operating condition of motor and load.
The embodiment of the present invention provides a kind of motor driving system operation health state evaluation system, as shown in Figure 2, comprising:
First data acquisition module, for acquiring the operation data of the motor in turn(a)round；
Second data acquisition module, for acquiring the operation data of the load in turn(a)round；
Fault detection module carries out accident analysis for the operation to controller,；
Neural network model, for carrying out accident analysis according to the operation data of motor and the operation data of load；
System grading module, the rule of built-in faulty scoring, for according to controller, motor and the failure of load
The scoring of Analysis result calculation system.
In the present embodiment, pass through the first data acquisition module, the motor in the second data collecting module collected turn(a)round
With the operation data of load；Failure is carried out to the operation data of motor and the operation data of load by neural network model
Analysis, output analysis result；Whether alarmed meter finally by system grading module according to the result and controller of accident analysis
The scoring of calculation system.
Specifically, the first data acquisition module includes: the first current sensor, for acquiring output when motor running
Electric current.Important evidence of the output electric current as motor health state evaluation when motor running, when output electric current is greater than defeated
Out maximum current or be less than output minimum current when, illustrate that motor has already appeared failure or will break down.
Further, the first data acquisition module further include: first voltage sensor, for when acquiring motor running
Output voltage.Important evidence of the output voltage as motor health state evaluation when motor running, when output voltage is big
When exporting maximum voltage or being less than output minimum voltage, illustrate that motor has already appeared failure or will break down.
Further, the first data acquisition module further include: the first vibrating sensor, for when acquiring motor running
Vibration parameters.Important evidence of the vibration parameters as motor health state evaluation when motor running, specifically includes that vibration
Amplitude and vibration frequency.Perhaps it is less than maximum amplitude or vibration frequency when the vibration amplitude of motor is greater than maximum amplitude
When greater than maximum frequency or being less than minimum frequency, illustrate that motor has already appeared failure or will break down.
Further, the first data acquisition module further include: speed probe turns when for acquiring motor running
Speed.Important evidence of the revolving speed as motor health state evaluation when motor running, when the revolving speed of motor is greater than maximum
Revolving speed or be less than minimum speed when, equally illustrate that motor has already appeared failure or will break down.
Output electric current, output voltage, vibration parameters and revolving speed when using motor running is foundation, neural network model
It can judge whether motor has already appeared failure or will break down.
Specifically, the second data acquisition module includes: the second vibrating sensor, for acquiring vibration ginseng when load running
Number.Important evidence of the vibration parameters as load health state evaluation when load running, comprising: vibration amplitude and vibration frequency
Rate.When the vibration amplitude of load be greater than maximum amplitude perhaps be less than minimum amplitude when or vibration frequency be greater than maximum frequency or
When person is less than minimum frequency, equally illustrate that load has already appeared failure or will break down.
Further, the second data acquisition module further include: oil liquid data acquisition module, for acquiring the oil liquid number of load
According to.The oil liquid data of load include:
Viscosity: viscosity increase is based on the oxidation of oil product, insolubles content increases, the infiltration of high viscosity oil or moisture
Enter.Viscosity reduces the infiltration for being based on low-viscosity oil, water, cryogen or fuel；Or oil product inner macromolecule polymer by
Shearing force and generate variation.
Flash-point: flash-point reduces display oil product and is diluted or oil product excessive temperature and cracking by combustion things.
Insoluble matter: pentane insolubes show the total content of solid matter in oil product, include organic matter and inorganic matter.Toluene energy
Most organic substance is dissolved, so toluene insolubles only include the dirt grains of sand, abraded metal particle and unburned carbon dust.Penta
The difference of alkane and toluene insolubles represents the content of colloid and oxide.Usual pentane insolubes are just measured when surmounting a certain limit
Toluene insolubles.
Color: oil product, which darkens, within extremely short period shows that oil product is contaminated or starts to be oxidized.
Moisture: there is the hydrogenesis in the leakage of water display system or air in oil product.Moisture can cause to corrode and aoxidize, also
It can make oil emulsion.So every filter method or removing should be vacuum-treated with centrifugal process.
Acid and alkalinity: pH value (pH)-pH increases representative and is permeated with alkaline oil product.PH reduction represents oil product and starts to become
Acid.
Total acid number (TAN): the total acid number of oil product is to measure the index that acidic materials are generated because of oxidation.
Total base number (TBN): total base number increases, it may be possible to caused by the high oil pollution of another alkalinity.Total base number
It reduces, it may be possible to because of the loss of high alkalinity additive, be used for antacid burning and oxidation product, or the moisture being infiltrated
It washes away.Metallic element analysis is for identifying pollution condition, it was demonstrated that the content of additive and the wear condition for showing parts are usually used
Spectroscopic analysis methods are tested.
By viscosity, flash-point, insoluble matter, color, moisture, acidity and the alkalinity, the total acid number that calculate or analyze oil liquid
(TAN), total base number (TBN), and these data are acquired by oil liquid data acquisition module, if the appearance of these data is different
Often, then illustrate that load has already appeared failure or will break down.
As foundation, neural network model can be judged to load the data of vibration parameters and oil liquid when using load running
Whether have already appeared failure or will break down.
Controller itself inline fault detection module, the low and high level that fault detection module passes through the output of detection controller
The duration of signal and low and high level signal detects its failure.It should be noted that fault detection module is
The corollary equipment of controller belongs to the prior art, is no longer described in detail in the present embodiment.
System grading module, the rule of built-in faulty scoring.Within the time between overhauls(TBO), controller have already appeared failure or
It will break down, motor has already appeared failure or will break down, and load and have already appeared failure or will occur
Failure respectively corresponds different deduction of points items, obtains final scoring after cumulative calculation, which can accurately assess motor
The operation health status of dragging system.
This motor driving system operation health state evaluation system can assess motor driving system in local,
Remotely motor driving system can be assessed.That is neural network model and system grading module are mounted on backstage,
First data acquisition module, the second data collecting module collected data remote transmission to backstage.This makes it possible to realize to more
The operation health status of set motor driving system is assessed.
The embodiment of the present invention also provides a kind of motor driving system operation health state evaluation method, as shown in figure 3, described
Method the following steps are included:
The rule that failure scores is arranged in step 1 in system grading module.
Specifically, the rule of failure scoring are as follows:
A, within the time between overhauls(TBO), if controller has occurred and that failure, fault alarm (Error), then score subtracts 50；If
It will break down, then Warning alarm (Warning), then score subtracts 15 points；
B, within the time between overhauls(TBO), the sensing data of motor inputs trained neural network model, judges motor
Operating condition.If judging to have occurred and that failure, fault alarm (Error), then score subtracts 50；If judging to send out
Failure is given birth to, then Warning alarm (Warning), then score subtracts 15 points；
C, within the time between overhauls(TBO), the sensing data of load inputs trained neural network model, judges motor
Operating condition.If judging to have occurred and that failure, fault alarm (Error), then score subtracts 50.If judging to occur
Failure, then Warning alarm (Warning), then score subtracts 15 points；
D, within the time between overhauls(TBO), operator or user feed back the information for any abnormal phenomenon that system is run,
Score subtracts 10 points, such as: there is oil leakage phenomenon；
E, within the time between overhauls(TBO), after the confirmation of system-down caused by different fault alarm (Error) is repaired, score, which returns, to be added
45 points；
F, within the time between overhauls(TBO), after different Warning alarms (Warning) Problem Confirmation is repaired, score, which returns, adds 10 points；
G, within the time between overhauls(TBO), after the operation exception information feedback acknowledgment that operator or user submit is repaired, score
It returns and adds 5 points；
H, after time between overhauls(TBO), if there is no any fault alarm (Error) or Warning alarm (Warning),
Score, which returns, adds 5 points；
I, after time between overhauls(TBO), if there is no the information that operator or user are operating abnormally system is anti-
Feedback, score, which returns, adds 5 points；
G, fault alarm (Error) phylogenetic before the time between overhauls(TBO) is not weighed if not confirmed and being handled
Multiple deduction of points；
K, the scoring initial value of system is 100 points, and with 0 point for lower limit, 100 points are the upper limit, and plus-minus fractional result must not
More than bound.
Step 2 acquires the operation data of the motor in turn(a)round and the operation data of load.
Specifically, the operation data of motor includes: output electric current, output voltage, vibration parameters, revolving speed.The fortune of load
Row data include vibration parameters, oil liquid parameter.Exporting electric current, output voltage, vibration parameters, revolving speed is motor running health shape
The important evidence of state assessment, and vibration parameters, oil liquid parameter are the important evidence of load running health state evaluation.Above-mentioned data
Phase can be carried out by current sensor, voltage sensor, vibrating sensor, speed probe and oil liquid data acquisition module
Close data acquisition.
Step 3, judge controller whether break down within the time between overhauls(TBO) alarm or occur Warning alarm.
Controller itself inline fault detection module, the low and high level that fault detection module passes through the output of detection controller
The duration of signal and low and high level signal detects its failure.It should be noted that fault detection module is
The corollary equipment of controller belongs to the prior art, is no longer described in detail in the present embodiment.
Step 4, neural network model carries out after the operation data of the operation data of motor and load is input to training
Accident analysis.
After neural network model trains, the operation data of motor, the operation data of load are inputted, it can be determined that go out
The operating condition of motor and load, judges whether it breaks down or alert.
Step 5, system grading module are commented according to the result computing system of judging result and accident analysis to controller
Point.
System grading module, the rule of built-in faulty scoring.The judging result of controller and the result of accident analysis
Different deduction of points items is respectively corresponded, obtains final scoring after cumulative calculation, which can accurately assess motor drag
The operation health status of system.
Those skilled in the art can make various modifications to described specific embodiment
Or supplement or be substituted in a similar manner, however, it does not deviate from the spirit of the invention or surmounts the appended claims determines
The range of justice.
Claims (10)
1. motor driving system run health state evaluation system, the motor driving system include sequentially connected controller,
Motor and load, which is characterized in that the operation health state evaluation system includes:
First data acquisition module, for acquiring the operation data of the motor in turn(a)round；
Second data acquisition module, for acquiring the operation data of the load in turn(a)round；
Fault detection module carries out accident analysis for the operation to controller；
Neural network model, for carrying out accident analysis according to the operation data of motor and the operation data of load；
System grading module, the rule of built-in faulty scoring, for according to controller, motor and the accident analysis of load
As a result computing system scores.
2. motor driving system according to claim 1 runs health state evaluation system, which is characterized in that described first
Data acquisition module includes: the first current sensor, for acquiring output electric current when motor running.
3. motor driving system according to claim 1 runs health state evaluation system, which is characterized in that described first
Data acquisition module includes: first voltage sensor, for acquiring output voltage when motor running.
4. motor driving system according to claim 1 runs health state evaluation system, which is characterized in that described first
Data acquisition module includes: the first vibrating sensor, for acquiring vibration parameters when motor running.
5. motor driving system according to claim 1 runs health state evaluation system, which is characterized in that described first
Data acquisition module includes: speed probe, for acquiring revolving speed when motor running.
6. motor driving system according to claim 1 runs health state evaluation system, which is characterized in that described second
Data acquisition module includes: the second vibrating sensor, for acquiring vibration parameters when load running.
7. motor driving system according to claim 1 runs health state evaluation system, which is characterized in that described second
Data acquisition module includes: oil liquid data acquisition module, for acquiring the oil liquid data of load.
8. motor driving system runs health state evaluation method, which is characterized in that the described method comprises the following steps:
The rule that failure scores is set in system grading module；
Acquire the operation data of the motor in turn(a)round and the operation data of load；
Judge controller whether break down within the time between overhauls(TBO) alarm or occur Warning alarm；
The operation data of the operation data of motor and load is input to the neural network model after training and carries out accident analysis；
System grading module scores according to the result computing system of judging result and accident analysis to controller.
9. motor driving system according to claim 8 runs health state evaluation method, which is characterized in that described electronic
The operation data of machine includes: output electric current, output voltage, vibration parameters.
10. motor driving system according to claim 8 runs health state evaluation method, which is characterized in that described negative
The operation data of load includes vibration parameters, oil liquid parameter, revolving speed.
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