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
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- G05B19/00—Programme-control systems
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- 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
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- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31356—Automatic fault detection and isolation
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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.
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