CN107797537A - A kind of prognostic and health management method applied to automatic production line - Google Patents
A kind of prognostic and health management method applied to automatic production line Download PDFInfo
- Publication number
- CN107797537A CN107797537A CN201711104095.1A CN201711104095A CN107797537A CN 107797537 A CN107797537 A CN 107797537A CN 201711104095 A CN201711104095 A CN 201711104095A CN 107797537 A CN107797537 A CN 107797537A
- Authority
- CN
- China
- Prior art keywords
- automatic production
- production line
- data
- bearing
- prognostic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 59
- 230000036541 health Effects 0.000 title claims abstract description 31
- 238000007726 management method Methods 0.000 title claims abstract description 17
- 238000012549 training Methods 0.000 claims abstract description 20
- 230000007935 neutral effect Effects 0.000 claims abstract description 19
- 238000012360 testing method Methods 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 13
- 230000032683 aging Effects 0.000 claims abstract description 6
- 239000002245 particle Substances 0.000 claims abstract description 6
- 239000000314 lubricant Substances 0.000 claims description 15
- 238000013136 deep learning model Methods 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 6
- 241001269238 Data Species 0.000 claims description 5
- 238000005299 abrasion Methods 0.000 claims description 5
- 238000013135 deep learning Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 4
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000002474 experimental method Methods 0.000 claims description 3
- 239000004519 grease Substances 0.000 claims description 3
- 235000003642 hunger Nutrition 0.000 claims description 3
- 230000001050 lubricating effect Effects 0.000 claims description 3
- 238000003754 machining Methods 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 230000037351 starvation Effects 0.000 claims description 3
- 206010054949 Metaplasia Diseases 0.000 claims description 2
- 230000004888 barrier function Effects 0.000 claims description 2
- 230000008878 coupling Effects 0.000 claims description 2
- 238000010168 coupling process Methods 0.000 claims description 2
- 238000005859 coupling reaction Methods 0.000 claims description 2
- 230000015689 metaplastic ossification Effects 0.000 claims description 2
- 230000001537 neural effect Effects 0.000 claims description 2
- 230000003595 spectral effect Effects 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims 1
- 238000004140 cleaning Methods 0.000 claims 1
- 210000005036 nerve Anatomy 0.000 claims 1
- 230000009467 reduction Effects 0.000 claims 1
- 238000000034 method Methods 0.000 abstract description 8
- 238000012423 maintenance Methods 0.000 abstract description 7
- 238000003745 diagnosis Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 230000003862 health status Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- 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] or computer integrated manufacturing [CIM]
- G05B19/41875—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] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31356—Automatic fault detection and isolation
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Automation & Control Theory (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention belongs to automatic production line technical field, specially a kind of prognostic and health management method applied to automatic production line;This method comprises the following steps:Prepare some automatic production lines to be measured first, heat ageing, vibration-testing are carried out to bearing apparatus therein respectively, training data is obtained and is stored in database;FMECA analyses are carried out again, obtain training sample;Then neutral net is trained using particle filter algorithm and be put into test chip;Finally the bearing apparatus in running order automatic production line is monitored in real time using test chip, while calculates its residual life and carries out health control.The present invention can monitor the operating condition of the bearing apparatus in automatic production line, and predictive-failure time in real time, reduce the probability of happening of catastrophic failure, and many potential safety hazards are avoided when catastrophic failure occurs, and so as to protect property, reduce maintenance costs.
Description
Technical field
The invention belongs to automatic production line technical field, and in particular to a kind of failure applied to automatic production line is pre-
Survey and health control method.
Background technology
With the development of science and technology, integrated level and the complexity increase of modern automation apparatus for production line, bring therewith
Maintenance cost and difficulty be also ramping up;The method of the low and costly traditional artificial repair and maintenance of efficiency in face of
No longer it is applicable during the present automatic production line equipment of large amount of complex.Prognostic and health management system (PHM) is intended to subtract
The cost of few manual maintenance, its health status is determined with the fault model on analysis automated production line, so as in unmanned value
Self-assessment and fault pre-alarming are carried out in the case of keeping, while health control can be carried out, to the failure on automatic production line
Carry out repair process.PHM technologies typically are provided with fault detect, Fault Isolation, the diagnosis of enhancing, performance detection, failure predication, strong
Ability, the typical PHM flows such as Kang Guanli, component life tracking contain data acquisition, data prediction, data transfer, spy
Levy extraction, data fusion, status monitoring, fault diagnosis, failure predication and ensure the links such as decision-making.
PHM based on data analysis is the status data by gathering equipment under test, excludes physical knowledge and enters line number to it
According to analysis, fault model is obtained to judge the health status of equipment using mathematical modeling, it need not obtain equipment under test
Physical model, and model that the time is short, and this can further reduce cost overhead, and have extensive versatility.
Primarily rested on currently for the fault diagnosis technology of the bearing apparatus in automatic production line on physical model,
The bearing apparatus in automatic production line is analyzed by phenomena such as abrasion that observes, aging, finds out failure therein
Point, the reason for occurring phenomena such as recycling physical model analysis abrasion, aging.This method is currently used primarily in research automation
The failure mechanism of bearing apparatus in production line, and also rarely have in terms of the fault diagnosis and prediction for the purpose of reducing manual maintenance
Occur.
The content of the invention
Automated production is applied to based on deep learning and particle filter algorithm it is an object of the invention to provide a kind of
The prognostic and health management method of line.This method is used to carry out in fact the bearing apparatus in operating automatic production line
When monitor, and Monitoring Data is handled in real time, realize the status checkout to the bearing apparatus in automatic production line therefore
Barrier prediction, life prediction and health control, the bearing apparatus in automatic production line is avoided to operate in the case of a fault.
To achieve the above object, the technical solution adopted by the present invention is introduced as follows.
A kind of prognostic and health management method applied to automatic production line, comprise the following steps:
Step 1, prepare some automatic production lines to be measured, the bearing apparatus in automatic production line to be measured is added respectively
Heat ageing, vibration-testing, obtain training data and be stored in database;
Step 2, FMECA analyses are carried out to training data, obtains training sample;
Step 3, CBM experiments are carried out, the neutral net number of plies is set and initializes neutral net;
Step 4, data acquisition is carried out to the bearing apparatus in automatic production line using sensor;
Step 5, data analysis is carried out, feature extraction is carried out to the data of collection;
Step 6, using deep learning model, with particle filter algorithm:VNN, DBN, CNN and SAE are instructed respectively to training sample
Practice neutral net;If the training error of all models is respectively less than predetermined threshold value, return to step 3, neural net layer is reset
Count and initialize neutral net;Otherwise, the minimum deep learning model of training error, and the neutral net trained are selected
It is put into test chip.
Step 7, the bearing apparatus of in running order automatic production line to be measured is counted in real time using sensor
According to collection, according to real time data, the bearing apparatus current health state X progress that test chip calculates automatic production line to be measured is defeated
Go out, and by history data store in RAM;
Step 8, using section bispectrum detection, when bearing breaks down, the feature of sampled signalx(t) impact to be disturbed
Modulated signal, i.e.,;
In formula, ωiFor modulation source, including bearing fault characteristics frequency and its harmonic frequency;ω0For carrier frequency;B is any normal
Number;When bearing breaks down, the signal after the demodulation of its vibration signal contains the first harmonic of fault characteristic frequency, and phase is
It is inter-related, that is, square phase-couple phenomenon be present;If set ωFFor the fault characteristic frequency of bearing, then bispectrum(ωF,
ωF )There is phase coupling estimation phenomenon in place,So as to which bispectrum is in (ωF ,ωF)Place has obvious spectral peak;
Step 9, the average value of all historical datas in RAM is calculated, with latest dataContrast, if fault rate change be present
Main trend, then calculate its rate of change v:, wherein time is time offset;And calculate it and reach event
Hinder threshold valueTime:,Axle in automatic production line as to be measured
Hold the residual life of equipment;
Step 10, the bearing apparatus in automatic production line is carried out repairing preparation and health control.
Preferably, the sensor in step 4 is temperature sensor and acceleration transducer.
Preferably, in step 5, the feature extracted to the data of collection is temperature and vibration frequency.
Preferably, in step 6, test chip is connected with the bearing apparatus in automatic production line.
Preferably, in step 7, the storage mode of historical data is:
1)Set the maximum of historical data and preserve number N;
2) N number of historical data is sequentially stored into sequentially in time;
3) i=1 is initialized;
4) when collecting next new data, i-th of data is replaced, and update i=mod (i, N)+1.
Preferably, in step 10, the mode of health control is:
1) temperature of automatic production line middle (center) bearing equipment is obtained by temperature sensor, if mid-winter:Lubricant is excessive, subtracts
Few lubricant;Lubricant starvation is improper, increases lubricant or selection proper lubrication agent;
2)The vibration frequency of automatic production line middle (center) bearing equipment is obtained by acceleration transducer, the rotary vibration of axle causes greatly
Crack, abrasion:Fatigue flake, change bearing;Assembly failure, improve the machining accuracy of axle;Foreign matter invades, and cleans correlated parts,
Use clean lubricating grease.
Further illustrate, the purpose of FMECA analyses is to find the root of failure event, modern FMECA analyses
Advanced algorithm can be provided, to extract optimal fault signature or conditional indicator, detect and isolate the failure of early stage, and predict
The remaining life of critical component, FMECA analyses can collect previous machine failure data to be carried out to failure evolution
Prediction, and good existing human resources are planned to perform attended operation.The present invention uses deep learning model, according to real-time number
According to, deep learning neutral net calculate output valve X is automatic production line current health state to be measured;In deep learning
In, its output valve X represents its class probability, and classification 1 is marked as more than 0.5, and classification 0 is marked as less than 0.5, the present invention
Middle setting classification 0 is failure, and above-mentioned " fault rate becomes big trend " is exactly the trend that X values reduce, and it is then that X subtracts to reach fault threshold
At the time of small arrival 0.5.
Compared to the prior art, the beneficial effects of the present invention are:
The present invention can monitor the operating condition of automatic production line middle (center) bearing equipment, and predictive-failure time in real time, reduce prominent
The probability of happening of failure is sent out, and many potential safety hazards are avoided when catastrophic failure occurs, so as to protect property, maintenance is reduced and opens
Pin, while manual maintenance can be reduced.
Brief description of the drawings
Fig. 1 is the CBM/PHM for the prognostic and health management system that the present invention is applied to automatic production line synthesis
Design method.
Fig. 2 is the CBM/PHM cycles for the prognostic and health management system that the present invention is applied to automatic production line.
Fig. 3 is the schematic diagram step by step for the prognostic and health management system that the present invention is applied to automatic production line.
Fig. 4 is the Stored Procedure signal for the prognostic and health management system that the present invention is applied to automatic production line
Figure.
Embodiment
The present invention will be described in detail with reference to the accompanying drawings and examples
As illustrated in fig. 1 and 2, the present invention includes:Design and trade study, FMECA analyses, CBM experiments, data acquisition, data point
Analysis, algorithm development, perform and verify and verify.It is specific as follows:
1. design and trade study main function be find critical component/subsystem fault pattern diagnosis and prediction it is best
Or best balanced scheme, to realize optimal CBM/PHM.
2.FMECA analyses collect previous machine failure data to be predicted to failure evolution, and planning is good existing
Somebody's power resource performs attended operation.
3. carrying out data acquisition, the service data of equipment includes internal data and external parameter:Internal data includes:Equipment
Running frequency, the data that can directly be obtained by equipment of error correction number, live load etc.;External parameter includes:Temperature, electricity
Pressure, electric current etc. need the data gathered by sensor;The capacity and reliability of data directly affects the accurate of test system
Degree and reliability, therefore, in order to improve the reliability of test system, it usually needs to the axle in a plurality of automatic production line to be measured
Hold equipment and carry out aging, vibration test, to collect enough training datas of its whole life cycle.
4. establishing neural network model, by the service data of equipment in database, by deep neural network algorithm, build
The vertical health status model obtained by parameter, it mainly trains the flow to be:
It is determined that the bearing apparatus in tested automatic production line, prepares multiple temperature, vibration frequency sample is used to collect training number
According to;
The data message of bearing apparatus in automatic production line is gathered by temperature sensor, acceleration transducer, and will be adopted
In the information deposit database collected;
FMECA analyses are carried out to the data collected, if finding to contribute minimum feature, noise dimension is regarded as, is entered
Training sample is obtained after row noise processed;
The neutral net number of plies is set, initializes neutral net;Using deep learning model, with particle filter algorithm:VNN、
Neutral net is respectively trained to training sample in DBN, CNN and SAE;If the training error of all models is respectively less than predetermined threshold value,
Reset the neutral net number of plies and initialize neutral net;Otherwise, the minimum deep learning model of training error is selected, and will
Its neutral net trained is put into test chip.
5. as shown in figure 3, using deep learning model and particle filter algorithm, when equipment works to automatic production line
In bearing apparatus monitored in real time, by test program by equipment current health status X, i.e. deep learning model output
Calculate and exported, and by rule by storage of history data P in RAM;If detecting device fails during this period,
Carry out the protection operation such as alarm;After history data store, trajectory track is carried out, exports the bearing apparatus in automatic production line
Life prediction, fault diagnosis and fault prediction and health control ensure decision-making;
6. storage mode flow is as shown in figure 4, history data store rule is as follows:
Set the maximum of historical data and preserve number N;
N number of historical data is sequentially stored into sequentially in time;
Initialize i=1;
When collecting next new data, i-th of data is replaced, and update i=mod (i, N)+1;
The final data characteristicses that so store are:Newer data storage is more intensive, and older data storage is more sparse.
7. calculating the average value of all historical datas in RAM, with newest data comparison, observe its variation tendency and calculate
Rate of change V:, wherein time is time offset, and the flat of all historical datas is subtracted by current time
The equal time obtains;If the faulty rate of data becomes big trend, calculate it and reach fault thresholdTime:,The residual life of the bearing of equipment in automatic production line as to be measured.
Inventor carries out actual measurement and calculating using the above method for certain automatic production line, draws the automatic metaplasia
Bearing apparatus prediction residual life in producing line is 7900h, actual life 8000h, accurate to predict automation
Bearing apparatus residual life in production line;
8. the bearing apparatus in pair automatic production line carries out repairing preparation and health control:
1) temperature of automatic production line middle (center) bearing equipment is obtained by temperature sensor, if mid-winter:Lubricant is excessive, subtracts
Few lubricant;Lubricant starvation is improper, increases lubricant or selection proper lubrication agent;
2)The vibration frequency of automatic production line middle (center) bearing equipment is obtained by acceleration transducer, the rotary vibration of axle causes greatly
Crack, abrasion:Fatigue flake, change bearing;Assembly failure, improve the machining accuracy of axle;Foreign matter invades, and cleans correlated parts,
Use clean lubricating grease.
The foregoing is only a specific embodiment of the invention, any feature disclosed in this specification, except non-specifically
It is described, can alternative features equivalent by other or specific similar purpose replaced.For the technical field of the invention
For those of ordinary skill, without departing from the inventive concept of the premise, it can also do to the embodiment that these have been described
Protection scope of the present invention should be all considered as belonging to by going out some replacements or variant.
Claims (7)
- A kind of 1. prognostic and health management method applied to automatic production line, it is characterised in that comprise the following steps:Step 1, prepare some automatic production lines to be measured, the bearing apparatus in automatic production line to be measured is added respectively Heat ageing, vibration-testing, obtain training data and be stored in database;Step 2, FMECA analyses are carried out to training data, obtains training sample;Step 3, CBM experiments are carried out, the neutral net number of plies is set and initializes neutral net;Step 4, data acquisition is carried out to the bearing apparatus in automatic production line using sensor;Step 5, data analysis is carried out, feature extraction is carried out to the data of collection;Step 6, using deep learning model, with particle filter algorithm:VNN, DBN, CNN and SAE are instructed respectively to training sample Practice neutral net;If the training error of all models is respectively less than predetermined threshold value, return to step 3, neural net layer is reset Count and initialize neutral net;Otherwise, the minimum deep learning model of training error, and the neutral net trained are selected It is put into test chip;Step 7, real time data is carried out to the bearing apparatus of in running order automatic production line to be measured using sensor to adopt Collection, according to real time data, the bearing apparatus current health state X that test chip calculates automatic production line to be measured is exported, And by history data store in RAM;Step 8, using section bispectrum detection, when bearing breaks down, the feature of sampled signalx(t) impact to be disturbed Modulated signal, i.e.,;In formula, ωiFor modulation source, including bearing fault characteristics frequency and its harmonic frequency;ω0For carrier frequency;B is any normal Number;When bearing breaks down, the signal after the demodulation of its vibration signal contains the first harmonic of fault characteristic frequency, and phase is It is inter-related, that is, square phase-couple phenomenon be present;If set ωFFor the fault characteristic frequency of bearing, then bispectrum(ωF, ωF )There is phase coupling estimation phenomenon in place, so as to which bispectrum is in (ωF ,ωF)Place has obvious spectral peak;Step 9, the average value of all historical datas in RAM is calculated, with latest dataContrast, if fault rate be present becomes big Trend, then calculate its rate of change v:, wherein time is time offset;And calculate it and reach event Hinder threshold valueTime:,Bearing in automatic production line as to be measured The residual life of equipment;Step 10, the bearing apparatus in automatic production line is carried out repairing preparation and health control.
- 2. prognostic and health management method as claimed in claim 1, it is characterised in that the sensor in step 4 is temperature Spend sensor and acceleration transducer.
- 3. prognostic and health management method as claimed in claim 1, it is characterised in that in step 5, to the data of collection The feature of extraction is temperature and vibration frequency.
- 4. prognostic and health management method as claimed in claim 1, it is characterised in that in step 6, test chip with from Bearing apparatus in dynamic metaplasia producing line is connected.
- 5. prognostic and health management method as claimed in claim 1, it is characterised in that in step 7, historical data is deposited Storage mode is:1)Set the maximum of historical data and preserve number N;2) N number of historical data is sequentially stored into sequentially in time;3) i=1 is initialized;4) when collecting next new data, i-th of data is replaced, and update i=mod (i, N)+1.
- 6. prognostic and health management method as claimed in claim 1, it is characterised in that step X is deep learning nerve The bearing apparatus current health state for the automatic production line to be measured that network calculations obtain;In deep learning neutral net, its Output valve X represents its class probability, and classification 1 is marked as more than 0.5, and classification 0 is marked as less than 0.5, and classification 0 is event Barrier;The fault rate becomes the trend that big trend is the reduction of X values, it is described reach fault threshold be then X be reduced to up to 0.5 when Carve.
- 7. prognostic and health management method as claimed in claim 1, it is characterised in that in step 10, health control Mode is:1) temperature of automatic production line middle (center) bearing equipment is obtained by temperature sensor, if mid-winter:Lubricant is excessive, Reduce lubricant;Lubricant starvation is improper, increases lubricant or selection proper lubrication agent;2)The vibration frequency of automatic production line middle (center) bearing equipment is obtained by acceleration transducer, if the rotary vibration of axle is led greatly Fracturing seam, abrasion:Fatigue flake, change bearing;Assembly failure, improve the machining accuracy of axle;Foreign matter invades, cleaning correlation zero Part, use clean lubricating grease.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711104095.1A CN107797537A (en) | 2017-11-10 | 2017-11-10 | A kind of prognostic and health management method applied to automatic production line |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711104095.1A CN107797537A (en) | 2017-11-10 | 2017-11-10 | A kind of prognostic and health management method applied to automatic production line |
Publications (1)
Publication Number | Publication Date |
---|---|
CN107797537A true CN107797537A (en) | 2018-03-13 |
Family
ID=61534739
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201711104095.1A Pending CN107797537A (en) | 2017-11-10 | 2017-11-10 | A kind of prognostic and health management method applied to automatic production line |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN107797537A (en) |
Cited By (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108803552A (en) * | 2018-08-31 | 2018-11-13 | 承德建龙特殊钢有限公司 | A kind of the monitoring system and monitoring method of equipment fault |
CN109143851A (en) * | 2018-07-11 | 2019-01-04 | 佛山科学技术学院 | The method of the identification of multiple labeling failure deep learning and its result intelligent expression |
CN109165396A (en) * | 2018-06-27 | 2019-01-08 | 谭晓栋 | A kind of equipment remaining life prediction technique of failure evolution trend |
CN109213034A (en) * | 2018-08-27 | 2019-01-15 | 硕橙(厦门)科技有限公司 | Equipment health degree monitoring method, device, computer equipment and readable storage medium storing program for executing |
CN109540522A (en) * | 2018-11-16 | 2019-03-29 | 北京航空航天大学 | Bearing health quantifies modeling method, device and server |
CN109682953A (en) * | 2019-02-28 | 2019-04-26 | 安徽大学 | A method of motor bearing lubricating grease content is determined using BP neural network |
CN109766930A (en) * | 2018-12-24 | 2019-05-17 | 太原理工大学 | A kind of method for predicting residual useful life of the mine machinery equipment based on DCNN model |
CN110044586A (en) * | 2019-03-13 | 2019-07-23 | 中交广州航道局有限公司 | Ship machine equipment failure judgment method, device, system and storage medium |
CN110047252A (en) * | 2019-04-18 | 2019-07-23 | 北京数字新思科技有限公司 | A kind of time-out reminding method, device, electronic equipment and readable storage medium storing program for executing |
CN110081927A (en) * | 2019-03-13 | 2019-08-02 | 中交广州航道局有限公司 | Ship machine equipment failure prediction method, device, system and storage medium |
CN110645153A (en) * | 2018-06-27 | 2020-01-03 | 北京金风科创风电设备有限公司 | Wind generating set fault diagnosis method and device and electronic equipment |
CN111458143A (en) * | 2020-04-11 | 2020-07-28 | 湘潭大学 | Temperature fault diagnosis method for main bearing of wind turbine generator |
CN111695631A (en) * | 2020-06-12 | 2020-09-22 | 泽恩科技有限公司 | Method, device, equipment and medium for extracting verification fault features based on SAE |
CN111765075A (en) * | 2020-05-20 | 2020-10-13 | 天津市天锻压力机有限公司 | Hydraulic forging press pump source fault prediction method and system |
CN112689600A (en) * | 2018-09-27 | 2021-04-20 | 利乐拉瓦尔集团及财务有限公司 | Method for failure prediction in a packaging machine |
CN113280910A (en) * | 2021-04-27 | 2021-08-20 | 圣名科技(广州)有限责任公司 | Real-time monitoring method and system for long product production line equipment |
CN114047733A (en) * | 2021-11-15 | 2022-02-15 | 界首市粮食机械有限责任公司 | Safety control system based on flexible production line of bearing roller |
WO2022069258A1 (en) | 2020-09-30 | 2022-04-07 | Siemens Aktiengesellschaft | Device and method for identifying anomalies in an industrial system for carrying out a production process |
CN114594737A (en) * | 2020-12-07 | 2022-06-07 | 北京福田康明斯发动机有限公司 | Optimization method and device for monitoring engine assembly process |
CN116681272A (en) * | 2023-06-02 | 2023-09-01 | 苏州索力伊智能科技有限公司 | Automatic assembly production line monitoring system and method thereof |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6594589B1 (en) * | 2001-05-23 | 2003-07-15 | Advanced Micro Devices, Inc. | Method and apparatus for monitoring tool health |
CN102831325A (en) * | 2012-09-04 | 2012-12-19 | 北京航空航天大学 | Method for predicting bearing fault based on Gaussian process regression |
CN104598736A (en) * | 2015-01-22 | 2015-05-06 | 西安交通大学 | Roller bearing service life predicting model of self-adaptive multi-kernel combination relevance vector machine |
CN106528975A (en) * | 2016-11-01 | 2017-03-22 | 电子科技大学 | Fault prognostics and health management method applied to circuits and systems |
-
2017
- 2017-11-10 CN CN201711104095.1A patent/CN107797537A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6594589B1 (en) * | 2001-05-23 | 2003-07-15 | Advanced Micro Devices, Inc. | Method and apparatus for monitoring tool health |
CN102831325A (en) * | 2012-09-04 | 2012-12-19 | 北京航空航天大学 | Method for predicting bearing fault based on Gaussian process regression |
CN104598736A (en) * | 2015-01-22 | 2015-05-06 | 西安交通大学 | Roller bearing service life predicting model of self-adaptive multi-kernel combination relevance vector machine |
CN106528975A (en) * | 2016-11-01 | 2017-03-22 | 电子科技大学 | Fault prognostics and health management method applied to circuits and systems |
Non-Patent Citations (2)
Title |
---|
刘本纪: "军用直升机齿轮箱状态与使用监控系统研究", 《中国优秀硕士学位论文全文数据库(电子期刊)工程科技II辑》 * |
张琳等: "基于EMD与切片双谱的轴承故障诊断方法", 《北京航空航天大学学报》 * |
Cited By (31)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109165396B (en) * | 2018-06-27 | 2023-09-29 | 谭晓栋 | Equipment residual service life prediction method of fault evolution trend |
CN109165396A (en) * | 2018-06-27 | 2019-01-08 | 谭晓栋 | A kind of equipment remaining life prediction technique of failure evolution trend |
CN110645153A (en) * | 2018-06-27 | 2020-01-03 | 北京金风科创风电设备有限公司 | Wind generating set fault diagnosis method and device and electronic equipment |
CN109143851A (en) * | 2018-07-11 | 2019-01-04 | 佛山科学技术学院 | The method of the identification of multiple labeling failure deep learning and its result intelligent expression |
CN109143851B (en) * | 2018-07-11 | 2021-06-01 | 佛山科学技术学院 | Method for recognizing multi-mark fault deep learning and intelligently expressing result thereof |
CN109213034A (en) * | 2018-08-27 | 2019-01-15 | 硕橙(厦门)科技有限公司 | Equipment health degree monitoring method, device, computer equipment and readable storage medium storing program for executing |
CN109213034B (en) * | 2018-08-27 | 2020-04-21 | 硕橙(厦门)科技有限公司 | Equipment health degree monitoring method and device, computer equipment and readable storage medium |
CN108803552B (en) * | 2018-08-31 | 2021-08-03 | 承德建龙特殊钢有限公司 | Monitoring system and monitoring method for equipment fault |
CN108803552A (en) * | 2018-08-31 | 2018-11-13 | 承德建龙特殊钢有限公司 | A kind of the monitoring system and monitoring method of equipment fault |
CN112689600B (en) * | 2018-09-27 | 2022-06-21 | 利乐拉瓦尔集团及财务有限公司 | Method for failure prediction in a packaging machine |
CN112689600A (en) * | 2018-09-27 | 2021-04-20 | 利乐拉瓦尔集团及财务有限公司 | Method for failure prediction in a packaging machine |
CN109540522A (en) * | 2018-11-16 | 2019-03-29 | 北京航空航天大学 | Bearing health quantifies modeling method, device and server |
CN109766930A (en) * | 2018-12-24 | 2019-05-17 | 太原理工大学 | A kind of method for predicting residual useful life of the mine machinery equipment based on DCNN model |
CN109766930B (en) * | 2018-12-24 | 2020-02-07 | 太原理工大学 | Method for predicting residual life of mine mechanical equipment based on DCNN model |
CN109682953A (en) * | 2019-02-28 | 2019-04-26 | 安徽大学 | A method of motor bearing lubricating grease content is determined using BP neural network |
CN109682953B (en) * | 2019-02-28 | 2021-08-24 | 安徽大学 | Method for judging lubricating grease content of motor bearing by using BP neural network |
CN110081927A (en) * | 2019-03-13 | 2019-08-02 | 中交广州航道局有限公司 | Ship machine equipment failure prediction method, device, system and storage medium |
CN110044586A (en) * | 2019-03-13 | 2019-07-23 | 中交广州航道局有限公司 | Ship machine equipment failure judgment method, device, system and storage medium |
CN110047252A (en) * | 2019-04-18 | 2019-07-23 | 北京数字新思科技有限公司 | A kind of time-out reminding method, device, electronic equipment and readable storage medium storing program for executing |
CN111458143A (en) * | 2020-04-11 | 2020-07-28 | 湘潭大学 | Temperature fault diagnosis method for main bearing of wind turbine generator |
CN111765075A (en) * | 2020-05-20 | 2020-10-13 | 天津市天锻压力机有限公司 | Hydraulic forging press pump source fault prediction method and system |
CN111695631A (en) * | 2020-06-12 | 2020-09-22 | 泽恩科技有限公司 | Method, device, equipment and medium for extracting verification fault features based on SAE |
CN111695631B (en) * | 2020-06-12 | 2023-06-20 | 泽恩科技有限公司 | SAE-based verification fault feature extraction method, device, equipment and medium |
WO2022069258A1 (en) | 2020-09-30 | 2022-04-07 | Siemens Aktiengesellschaft | Device and method for identifying anomalies in an industrial system for carrying out a production process |
CN114594737A (en) * | 2020-12-07 | 2022-06-07 | 北京福田康明斯发动机有限公司 | Optimization method and device for monitoring engine assembly process |
CN114594737B (en) * | 2020-12-07 | 2024-09-17 | 北京福田康明斯发动机有限公司 | Optimization method and device for monitoring engine assembly process |
CN113280910A (en) * | 2021-04-27 | 2021-08-20 | 圣名科技(广州)有限责任公司 | Real-time monitoring method and system for long product production line equipment |
CN114047733A (en) * | 2021-11-15 | 2022-02-15 | 界首市粮食机械有限责任公司 | Safety control system based on flexible production line of bearing roller |
CN114047733B (en) * | 2021-11-15 | 2023-12-29 | 界首市粮食机械有限责任公司 | Safety control system based on flexible production line of bearing roller |
CN116681272A (en) * | 2023-06-02 | 2023-09-01 | 苏州索力伊智能科技有限公司 | Automatic assembly production line monitoring system and method thereof |
CN116681272B (en) * | 2023-06-02 | 2024-02-02 | 苏州索力伊智能科技有限公司 | Automatic assembly production line monitoring system and method thereof |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN107797537A (en) | A kind of prognostic and health management method applied to automatic production line | |
CN110414155B (en) | Fan component temperature abnormity detection and alarm method with single measuring point | |
CN111597682B (en) | Method for predicting remaining life of bearing of gearbox of wind turbine | |
CN106528975B (en) | A kind of prognostic and health management method applied to Circuits and Systems | |
CN110414154B (en) | Fan component temperature abnormity detection and alarm method with double measuring points | |
CN104390657B (en) | A kind of Generator Unit Operating Parameters measurement sensor fault diagnosis method and system | |
CN109948860A (en) | A kind of mechanical system method for predicting residual useful life and system | |
CN108896299A (en) | A kind of gearbox fault detection method | |
CN107796609B (en) | Water chilling unit fault diagnosis method based on DBN model | |
GB2476246A (en) | Diagnosing an operation mode of a machine | |
CN109492790A (en) | Wind turbines health control method based on neural network and data mining | |
CN111506049B (en) | Multiple fault diagnosis method for aero-engine control system based on AANN network system | |
CN106529832A (en) | Relay protection system risk assessment method based on Markov reliability correction model | |
CN116629627A (en) | Intelligent detection system of power transmission on-line monitoring device | |
CN117471346A (en) | Method and system for determining remaining life and health status of retired battery module | |
CN115037603A (en) | Diagnosis evaluation method, device and system of electricity consumption information acquisition equipment | |
CN117350377A (en) | Knowledge graph driving-based equipment fault diagnosis method and device | |
CN116295948A (en) | Abnormality detection method, system and storage medium of industrial temperature sensor in large temperature difference environment | |
CN114184375A (en) | Intelligent diagnosis method for common faults of gear box | |
CN117993562A (en) | Wind turbine generator system fault prediction method and system based on artificial intelligent big data analysis | |
CN113313365A (en) | Degradation early warning method and device for primary air fan | |
CN105302476B (en) | A kind of reliability data online acquisition for nuclear power plant equipment analyzes storage system and its storage method | |
CN112016193A (en) | Online prediction method and system for lubrication failure of shield tunneling machine system | |
US11339763B2 (en) | Method for windmill farm monitoring | |
CN107727392B (en) | State index evaluation and optimization method based on signal detection and ROC analysis |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180313 |