CN109726505A - A kind of forging machine tool main drive gear fault diagnosis system based on intelligent trouble tree - Google Patents
A kind of forging machine tool main drive gear fault diagnosis system based on intelligent trouble tree Download PDFInfo
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Abstract
The invention belongs to intelligent industrial manufacturing fields, more particularly to a kind of forging machine tool main drive gear fault diagnosis system based on intelligent trouble tree, it is made of the data acquisition module of transmission mechanism, data mart modeling processing with characteristics analysis module, intelligent trouble tree diagnostic module, intelligence database access module, interactive interface module.The present invention solves the complex of the time delay of traditional low-frequency signal, high-frequency signal, improve the precision of fault diagnosis, the present invention is then to incorporate intelligent algorithm during Construction of Fault Tree, the intelligent trouble tree with failure branch weight proportion is directly obtained, the precision of fault tree diagnosis is greatly improved.
Description
Technical field
The invention belongs to intelligent industrial manufacturing fields, and in particular to a kind of forging machine tool main transmission based on intelligent trouble tree
Mechanism-trouble diagnostic system.
Background technique
With the rapid development in intelligence manufacture field, the forging machine tool infrastructure device important as manufacturing industry, country is taken
Many effective measures promote the development in intelligence manufacture field, and main drive gear is the core part of forging machine tool, wherein multistage
The linkage mechanism of driving wheel and Primary and secondary bearing composition controls and determines forging quality.Since main drive gear contains a large amount of differences
The gear and connecting shaft of specification, and run when be wrapped in lathe top shell in, to its accident analysis be often in by
Dynamic stage, i.e. technical staff are replaced after carrying out failure to it, and this mode will be greatly reduced making for main drive gear sometimes
With the service life, to improve unnecessary maintenance cost.And mentioning with industrial 4.0 intelligent plants and " made in China 2025 "
Out, a set of intelligent Fault Diagnose Systems are proposed, the operating status of forging machine tool complexity main drive gear can be carried out online
Fault diagnosis, establishing perfect fault diagnosis system is intelligence manufacture field, the problem to be solved under the industrial new era.
Existing forging machine tool fault diagnosis is in the fault locating analysis of single position, i.e., only to a bearing or one
A gear carries out fault diagnosis, is not bound with entire main drive gear, establishes a complete forging machine tool main drive gear event
Hinder diagnosis system.The present invention is then that the intelligent Fault Diagnose Systems of forging machine tool main drive gear have been built based on LabVIEW,
Middle intelligence is embodied in the method application of intelligent trouble tree, and whole system has visual man-machine interface, can be with figure and text
The mode of word shows the diagnosis of intelligent trouble tree to the fault diagnosis result of forging machine tool main transmission structure, significant increase China intelligence
The level of the big machinery fault diagnosis of manufacturing field.
Summary of the invention
The purpose of the present invention is design a kind of intelligent trouble tree diagnosis system of online, intelligent forging machine tool main drive gear
System.
Whole system is implemented in combination with by hardware and software, the intelligent trouble tree of this forging machine tool main drive gear
Diagnostic system is made of 5 modules, is respectively: data acquisition module, data mart modeling processing and the signature analysis mould of transmission mechanism
Block, intelligent trouble tree diagnostic module, intelligence database access module, interactive interface module separately below carry out modules
It explains one by one:
A kind of forging machine tool main drive gear fault diagnosis system based on intelligent trouble tree, is adopted by the data of transmission mechanism
Collect module, data mart modeling processing and characteristics analysis module, intelligent trouble tree diagnostic module, intelligence database access module, interaction
Interface module composition.
The data acquisition module of the transmission mechanism is made of DSP core processor, vibrating sensor, temperature sensor,
It is each responsible for three low-and high-frequency fusions of lubrication number, bear vibration, bearing temperature of acquisition each bearing of equipment main drive gear
Information.
The data mart modeling processing and the data acquisition module of characteristics analysis module and drive mechanism are directly connected to, by small
Wave packet algorithm eliminates the noise section of vibration signal；It is handled by energy normalized, and temperature, lubrication number and vibration is melted
It closes, becomes the feature space of low-and high-frequency combination.
The intelligent trouble tree diagnostic module links together with data mart modeling processing with characteristics analysis module；The intelligence
Fault tree diagnostic module establishes traditional fault tree, in this, as intelligence by researching and analysing to forging machine tool main drive gear
The improvement basis of energy fault tree diagnosis, establishes the neural network model of main drive gear intelligent trouble tree diagnosis, by data mart modeling
With the processed low-and high-frequency signal of characteristics analysis module as inputting, corresponding failure branch weight is used as output for processing, into
The training of row improved Elman network is managed using the intelligent diagnostics result of improved Elman network as local evidence using D-S evidence
By expertise is merged therewith, completes entire intelligent trouble tree diagnostic module.
The intelligence database access module is designed based on SQL database, is made of 3 subdata bases, i.e.,
Data acquisition module, the data mart modeling of transmission mechanism, which are handled, respectively distributes 1 with characteristics analysis module, intelligent trouble tree diagnostic module
Subdata base can be transmitted mutually between 3 subdata bases.
The beneficial effects of the present invention are:
(1) present invention solves the time delay of traditional low-frequency signal, the complex of high-frequency signal, improves the essence of fault diagnosis
Degree.
(2) combination of traditional fault tree and intelligent algorithm tends to belong to soft combination, i.e., has only used fault tree as net
The input or output of network training as a result, but failure branch weight be defaulted as 1, the present invention is then during Construction of Fault Tree
Intelligent algorithm is incorporated, the intelligent trouble tree with failure branch weight proportion is directly obtained, greatly improves fault tree diagnosis
Precision.
Detailed description of the invention
The data acquisition flow figure of Fig. 1 transmission mechanism；
The processing of Fig. 2 data mart modeling and signature analysis flow chart；
Fig. 3 intelligent trouble tree diagnostic flow chart；
Fig. 4 improves Elman intelligent diagnostics network structure；
Fig. 5 intelligence database access module design structure diagram；
Fig. 6 overall system architecture figure.
Specific embodiment
The present invention is described further with reference to the accompanying drawing.
With the rapid development in intelligence manufacture field, the forging machine tool infrastructure device important as manufacturing industry, country is taken
Many effective measures promote the development in intelligence manufacture field, and main drive gear is the core part of forging machine tool, wherein multistage
The linkage mechanism of driving wheel and Primary and secondary bearing composition controls and determines forging quality.Since main drive gear contains a large amount of differences
The gear and connecting shaft of specification, and run when be wrapped in lathe top shell in, to its accident analysis be often in by
Dynamic stage, i.e. technical staff are replaced after carrying out failure to it, and this mode will be greatly reduced making for main drive gear sometimes
With the service life, to improve unnecessary maintenance cost.And mentioning with industrial 4.0 intelligent plants and " made in China 2025 "
Out, a set of intelligent Fault Diagnose Systems are proposed, the operating status of forging machine tool complexity main drive gear can be carried out online
Fault diagnosis, establishing perfect fault diagnosis system is intelligence manufacture field, the problem to be solved under the industrial new era.
Existing forging machine tool fault diagnosis is in the fault locating analysis of single position, i.e., only to a bearing or one
A gear carries out fault diagnosis, is not bound with entire main drive gear, establishes a complete forging machine tool main drive gear event
Hinder diagnosis system.The present invention is then that the intelligent Fault Diagnose Systems of forging machine tool main drive gear have been built based on LabVIEW,
Middle intelligence is embodied in the method application of intelligent trouble tree, and whole system has visual man-machine interface, can be with figure and text
The mode of word shows the diagnosis of intelligent trouble tree to the fault diagnosis result of forging machine tool main transmission structure, significant increase China intelligence
The level of the big machinery fault diagnosis of manufacturing field.
The purpose of the present invention is design a kind of intelligent trouble tree diagnosis system of online, intelligent forging machine tool main drive gear
System.
Whole system is implemented in combination with by hardware and software, the intelligent trouble tree of this forging machine tool main drive gear
Diagnostic system is made of 5 modules, is respectively: data acquisition module, data mart modeling processing and the signature analysis mould of transmission mechanism
Block, intelligent trouble tree diagnostic module, intelligence database access module, interactive interface module separately below carry out modules
It explains one by one:
1. the data acquisition module of transmission mechanism is made of DSP core processor, vibrating sensor, temperature sensor
, it is each responsible for three low-and high-frequency fusions of lubrication number, bear vibration, bearing temperature of acquisition each bearing of equipment main drive gear
Information, the data of this low-and high-frequency combine, solve the time delay of traditional low-frequency signal, the complex of high-frequency signal, improve
The precision of fault diagnosis.
2. data mart modeling processing and characteristics analysis module: the data acquisition module of the module and drive mechanism is directly connected to.
So-called working process is that the noise section of vibration signal is eliminated by Wavelet Packet Algorithm；So-called signature analysis then passes through
Energy normalized processing, and temperature, lubrication number are merged with vibration, become the feature space of low-and high-frequency combination.
3. intelligent trouble tree diagnostic module: the module is directly connected to data mart modeling processing in characteristics analysis module, failure
Diagnostic module be this system nucleus module, the intelligent trouble tree of main drive gear is established by this module, completes mechanism
Intelligent trouble diagnosis task.The innovation of this module is the foundation of intelligent trouble tree, is realized by this module with a kind of online
The mode of habit improves the defect of the not no weight of conventional failure tree, and is the failure branch learnt by intelligent algorithm,
Therefore has very strong intelligent innovation level.Specific operating procedure is as follows:
(1) by researching and analysing to forging machine tool main drive gear, traditional fault tree is established, in this, as intelligent event
The improvement basis of barrier tree diagnosis；
(2) neural network model of main drive gear intelligent trouble tree diagnosis is established；
(3) the low-and high-frequency signal for crossing a upper resume module is used as output as input, corresponding failure branch weight,
Improve Elman network training.This is also that the core content of intelligent trouble tree proposes on the basis of traditional fault tree
A kind of diagnostic system of online failure branch weight distribution study；
(4) expert is passed through using D-S evidence theory using the intelligent diagnostics result of improved Elman network as local evidence
It tests and merges therewith, complete entire intelligent trouble tree diagnostic module.
4. intelligence database access module: this module is designed based on SQL database, and main task is:
(1) initial data in the data acquisition module of transmission mechanism is stored；
(2) storing data working process and characteristics analysis module treated low-and high-frequency amalgamation data；
(3) important information for storing intelligent trouble diagnosis module, including: fault tree and smallest partition collection data, intelligence
The branch weight distribution result that the parameter setting data of energy algorithm, expert experience base, intelligent trouble tree diagnose.
5. interactive interface: this module is responsible for showing the intelligence of forging machine tool main drive gear with visualization, online mode
The diagnostic result of fault tree shows initial data, treated characteristic, intelligent trouble branch by LabVIEW software
Weight map, literal interpretation such as illustrate at the information.
This system combines exploitation by DSP, LabVIEW, SQL database, is made of 5 modules, is respectively: transmission mechanism
Data acquisition module, data mart modeling processing with characteristics analysis module, intelligent trouble diagnosis module, intelligence database access module,
Interactive interface module.
1. the data acquisition module of transmission mechanism
The realization of acquisition module is the signal for having dsp chip to handle, and sensor selects two kinds of vibration and temperature and this is
Low-and high-frequency data of uniting combine, and carry out one of the innovative point of fault diagnosis.Wherein temperature acquisition must select infrared temperature sensor,
To ensure not influence original bearing rotary state.DSP has recorded main drive gear while acquisition vibration and temperature signal
The lubrication number information of bearing, this is also important diagnosis basis.It is finally completed main drive gear temperature, lubrication number, vibration
The acquisition of three kinds of signals establishes huge and perfect data supporting for the study of intelligent trouble branch weight, as shown in Figure 1.
2. data mart modeling processing and characteristics analysis module
Working process and characteristics analysis module then use LabVIEW to be designed.
(1) firstly, determining correct wavelet basis, and the core function of wavelet packet processing are as follows:
In formula；j≥0；P=0,2j-1；H, g are filter group.ψ is then wavelet function；
(2) then by the main drive gear vibration information after denoising, Energy extraction is carried out by following equation:
Wherein, xjk(k=1,2, N) and it is to forge the frequency band discrete point after bearing vibration signal is finally decomposed, so far
The feature space T that energy indicates may be expressed as:
T=[Ei1,Ei2,Ei3,Ei4,···EiN-1,EiN]；
(3) conventional vibration signal needs normalized, it is noted here that not carry out the operation centainly, this is also this mould
The innovation of block, because vibration signal also needs in conjunction with temperature and lubrication number information, normalized, can be made herein
At subsequent data values range differences away from consequence that is excessive, and leading to network training failure.Way of the invention is: vibration signal
It does not normalize first, directly carries out (4)；
(3) bearing temperature of main drive gear, lubrication number are combined with treated vibration signal, completes to build just
The data source of frequency intelligent diagnostics.This method can reduce the processing degree of difficulty of single high-frequency signal, solve single low frequency signal
Diagnosis time delay；
(5) especially set out without normalized in (3), the data in (4) are combined in (5), vibrated,
Temperature, the normalized for lubricating three kinds of low-and high-frequency combined datas of number, improve the diagnosticability of data, as shown in Figure 2.
3. intelligent trouble tree diagnostic module
Intelligence in intelligent trouble tree is embodied in: intelligent algorithm being applied in the establishment process of fault tree, is solved original
Failure branch weight is defaulted as 1 shortcoming, this module passes through the matlab control complete design of LabVIEW.
Traditional failure branch is all to be manually set, intelligent algorithm using fault tree as input and output vector, and
The intelligent trouble tree of invention is incorporated intelligent algorithm in the establishment process of fault tree in a manner of on-line study, and intelligence is completed
The distribution of energy failure branch weight, the specific steps are as follows:
(1) be first forging machine tool main transmission three-level transmission mechanism to be established conventional failure tree analysis chart, and ask minimum
Segmentation collection, this part are then main foundation by expertise and the failure logging sheet at scene；
(2) correction Elman intelligent network model is established, mode input signal has: bear vibration 1~6, one, two, three biography
Dynamic temperature and one, two, three transmission lubrication number, input dimension 12 are tieed up.Using the result of a upper module as this module
Input, the output of this module are then the fault tree bottom events that (1) is established, it may be assumed that one, two, three gear lubrication shortage of oil, with master
Motor connection loosens, and stage transmission wheel engagement is unstable, and the engagement of two three-level driving wheels is unstable, and loosening is connect with mechanical arm,
Totally 7 dimension output, as shown in Figure 4；
(3) training network, and obtain local intelligence failure branch weights；
(4) it combines expert for the failure branch weight experience of forging machine tool main transmission structure, completes base in output layer
It is merged in the diagnostic result of D-S evidence theory, this final module completes intelligent trouble tree diagnostic function.
1. intelligence database access module
This set system needs to establish complicated powerful database, so needing to establish perfect, comprehensive data back
System, this module is by SQL software realization.Need to establish following three subdata bases: the data acquisition module of transmission mechanism
Database, data mart modeling processing and characteristics analysis module database, intelligent trouble diagnosis module database, as shown in Figure 5.
(1) the data acquisition module database of transmission mechanism
It is mainly responsible for the initial data of storage sensor, dsp chip acquisition, i.e. temperature, lubrication number, three kinds of vibration, together
When correct frequency acquisition parameter is set；
(2) data mart modeling processing and characteristics analysis module database
It is mainly responsible for storage and improves intelligence through intelligent algorithm treated low-and high-frequency amalgamation signal and main drive gear
The input feature vector space of network, while the parameter setting of intelligent algorithm also adjusts in database；
(3) intelligent trouble diagnosis module database
This subdata base is firstly the need of traditional forging machine tool main drive gear fault tree information is stored, and secondly storage improves
The learning parameter of network, D-S evidence theory, what is finally stored is by expert to the artificial experience of failure branch and linked office
The branch weight distribution result for the forging machine tool main drive gear intelligent trouble tree diagnosis that portion's result blends.
Three subdata bases are connected with each other, and have collectively constituted intelligence database access module of the invention, and on backstage
The content of training data can be changed, this mode allows the present invention to have by way of changing original intelligent network learning data
Better extended capability and generalization, can be applied to other equipment position or other systems.
5. interactive interface module
Interactive interface is a kind of display effect of data, is equally designed using LabVIEW, needs to establish
The communication interface of LabVIEW and DSP connects, and establishes the data transmission connection of LabVIEW and SQL.Complete this on-line manner
The graphical representation result of the forging machine tool main drive gear intelligent trouble tree diagnosis of invention.Vibration information can pass through figure
Mode shows time-domain and frequency-domain figure, and includes denoising both front and back waveform, is conveniently used for comparison and subsequent study；Intelligent trouble tree
Display be then graphically, while equipped with explain explanation, to determine producing cause and the maintenance of a certain branch failure
Scheme, this set of system have strong intelligence, integrality；The training data in the library SQL can be operated in interface homepage simultaneously,
This mode can not need professional technician in front end windowing, can also be with even newly taking the learner of this system
It completes the increase of SQL database training data and deletes work, improve systematic difference performance.
6. the research of intelligent trouble tree diagnostic method corresponding to above-mentioned system process is as follows, as shown in Figure 3:
Step 1: the temperature and lubrication number of the three-level driving wheel of acquisition main transmission control, constitute the low-frequency information of 6 dimensions
Source；
Step 2: acquisition main motor bearing vibration signal, and Wavelet Denoising Method is carried out, equally constitute the high-frequency information of 6 dimensions
Source；
Step 3: 6+6 constitutes 12 dimension multi-source informations, and it is normalized, constitutes 12 dimension input vectors；
Step 4: establishing forging machine tool fault tree models, smallest partition collection is sought in qualitative analysis, constitutes 7 dimension output vectors；
Step 5: Elman network model is established, using input and the output of third step and the 4th step as a result, training network
Weight；
Step 6:, in conjunction with expert to the experience of forging machine tool main drive gear, being finally completed intelligent event using D-S evidence
Hinder the foundation of tree.
Claims (5)
1. a kind of forging machine tool main drive gear fault diagnosis system based on intelligent trouble tree, it is characterised in that: by driver
The data acquisition module of structure, data mart modeling processing are accessed with characteristics analysis module, intelligent trouble tree diagnostic module, intelligence database
Module, interactive interface module composition.
2. a kind of forging machine tool main drive gear fault diagnosis system based on intelligent trouble tree according to claim 1,
It is characterized in that, the data acquisition module of the transmission mechanism, by DSP core processor, vibrating sensor, temperature sensor group
At three low-and high-frequencies of lubrication number, bear vibration, bearing temperature for being each responsible for acquisition each bearing of equipment main drive gear merge
Information.
3. a kind of forging machine tool main drive gear fault diagnosis system based on intelligent trouble tree according to claim 1,
It is characterized in that, the data mart modeling processing and the data acquisition module of characteristics analysis module and drive mechanism are directly connected to, lead to
Wavelet Packet Algorithm is crossed to eliminate the noise section of vibration signal；It is handled by energy normalized, and by temperature, lubrication number and shaken
Dynamic fusion, becomes the feature space of low-and high-frequency combination.
4. a kind of forging machine tool main drive gear fault diagnosis system based on intelligent trouble tree according to claim 1,
It is characterized in that, the intelligent trouble tree diagnostic module links together with data mart modeling processing with characteristics analysis module；It is described
Intelligent trouble tree diagnostic module is established traditional fault tree, is made with this by researching and analysing to forging machine tool main drive gear
For the improvement basis of intelligent trouble tree diagnosis, the neural network model of main drive gear intelligent trouble tree diagnosis is established, by data
Working process and the processed low-and high-frequency signal of characteristics analysis module are as inputting, and corresponding failure branch weight is as defeated
Out, Elman network training is improved, using the intelligent diagnostics result of improved Elman network as local evidence, is demonstrate,proved using D-S
According to theory, expertise is merged therewith, completes entire intelligent trouble tree diagnostic module.
5. a kind of forging machine tool main drive gear fault diagnosis system based on intelligent trouble tree according to claim 1,
It is characterized in that, the intelligence database access module is designed based on SQL database, it is made of 3 subdata bases,
I.e. the data acquisition module of transmission mechanism, data mart modeling processing respectively distribute 1 with characteristics analysis module, intelligent trouble tree diagnostic module
A subdata base can be transmitted mutually between 3 subdata bases.
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