CN107276816A - A kind of long-range monitoring and fault diagnosis system and method for diagnosing faults based on cloud service - Google Patents
A kind of long-range monitoring and fault diagnosis system and method for diagnosing faults based on cloud service Download PDFInfo
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- CN107276816A CN107276816A CN201710533956.1A CN201710533956A CN107276816A CN 107276816 A CN107276816 A CN 107276816A CN 201710533956 A CN201710533956 A CN 201710533956A CN 107276816 A CN107276816 A CN 107276816A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
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Abstract
The invention discloses a kind of long-range monitoring and fault diagnosis system based on cloud service and method for diagnosing faults, it is related to intelligence manufacture and cloud diagnostic field, including data acquisition unit, telecommunication gateway unit, cloud storage administrative unit, cloud service center unit.Data acquisition unit, is deployed on data collection station；Telecommunication gateway unit, deployment is beyond the clouds on preposition gateway server；Cloud storage administrative unit, deployment is beyond the clouds on data server；Cloud service center unit, is deployed in cloud application server.Applying the BP neural network method for diagnosing faults based on genetic algorithm optimization in long-range monitoring and fault diagnosis system for the invention, can easily provide the long-range monitoring and fault diagnosis service under suitable cross-region environment；Plant equipment remote online monitoring of working condition can be provided for the technical staff of equipment manufacturers, quick, accurate, the efficient diagnosis of complex fault is provided for the plant equipment that manufacturing enterprise uses.
Description
Technical field
The present invention relates to intelligence manufacture and cloud diagnostic field, a kind of long-range monitoring based on cloud service and failure are particularly related to
Diagnostic system and method for diagnosing faults.
Background technology
With the fast development and extensive use of computer, data acquisition, sensor and network technology, manufacturing technology also to
Networking and intelligent direction development.Plant equipment constantly uses modern advanced industrial technology so that its complexity and dimension
Shield difficulty is continued to increase, while real-time, reliability, integrality to Data acquisition and issuance etc. requires also more and more higher, is passed
That unites can not meet the maintenance requirement of modernization based on unit and the monitoring of equipment and fault diagnosis system of live mode.How
Going on business for technical staff, particularly high-level R＆D personnel is reduced, the Site Service workload for the product that dispatches from the factory is reduced；How
The remote maintenance response speed of product is improved, potential problems how are found in time, and then provide on the other hand for the transformation of product, design
Data foundation, be the realistic problem that equipment manufacturers face.
The content of the invention
It is an object of the invention to provide a kind of maintenance for improving product, response speed is safeguarded, timely discovering device is dived
In long-range monitoring and fault diagnosis system of the problem based on cloud service and method for diagnosing faults.
In order to reach above-mentioned purpose, solution of the invention is：
A kind of long-range monitoring and fault diagnosis system based on cloud service, including：Data acquisition unit, telecommunication gateway
Unit, cloud storage administrative unit and cloud service center unit；The data acquisition unit is deployed in the scene of plant equipment, data
Telecommunication gateway unit between collecting unit and described telecommunication gateway unit, described is managed with described cloud storage
Cloud storage management between telecommunication gateway unit between unit, described and described cloud service center unit, described is single
It is first to be attached between described cloud service center unit using network.
Between described data acquisition unit and described telecommunication gateway unit, it is attached using 4G networks；
Between described telecommunication gateway unit and described cloud storage administrative unit, described telecommunication gateway list
Between first cloud storage administrative unit between described cloud service center unit, described and described cloud service center unit,
It is attached using Internet/Intranet networks；
Described cloud service center unit provides a user WEB/APP/WAP interfaces using Internet or 4G networks
Cloud service.
Described data acquisition unit, including sensor assembly, controller module, remote communication module, are deployed in data
On acquisition terminal；
Described sensor assembly, according to default frequency acquisition, gathers industrial site various kinds of sensors institute in real time
The working condition signal data of perception, send remote communication module to；
Described controller module, the control data that real-time reception remote communication module is transmitted, according to control data pair
The frequency acquisition of sensor assembly, remote communication module report the parameters such as frequency to be configured and change；
Described remote communication module, on the one hand, the downlink command that real-time reception telecommunication gateway unit is issued, it then follows
Telecommunication agreement, analysis instruction data, and the control data that will be obtained after parsing, send controller module to, on the other hand,
The working condition signal data that real-time reception sensor assembly is transmitted, it then follows telecommunication agreement, encapsulate working condition signal data, and will
The director data obtained after encapsulation, reports frequency according to default, sends up-on command, real-time report gives telecommunication gateway list
Member.
Described telecommunication gateway unit includes Communications service module, data prediction service module and intelligent adaptation clothes
Business module, is disposed on preposition gateway server beyond the clouds respectively；
Described Communications service module, on the one hand, the up-on command that real-time reception data acquisition unit is reported, it then follows long-range
Communications protocol, analysis instruction data, and by the working condition signal data obtained after parsing, send data prediction service module to,
On the other hand, the control data transmitted by real-time reception cloud service center unit, it then follows telecommunication agreement, encapsulation control number
According to, and the director data that will be obtained after encapsulation, downlink command is sent, real time down is to data acquisition unit；
Described data prediction service module, the working condition signal data that real-time reception Communications service module is transmitted, enters
Line number Data preprocess, and cloud storage administrative unit is sent in real time；
Described intelligent adaptation service module is there is provided mode adapter, and the plant equipment for correspondence model specifies corresponding
Telecommunication agreement.
Described cloud storage administrative unit, including data storage service module, data retrieval service module and data encryption
Service module, each module deployment is beyond the clouds on data server；
Described data storage service module, the characteristic vector data transmitted by real-time reception telecommunication gateway unit,
Store in corresponding database；
Described data retrieval service module, the inquiry request transmitted by real-time reception cloud service center unit, using number
According to retrieval technique, target data is obtained from database, and target data is returned into cloud service center unit；
Described data encryption services module, is some sensitive or crucial features using the ripe close encryption technology of business
There is provided reliable encryption, decryption service for the storage of vector data.
Described cloud service center unit, including monitoring service module, diagnostic service module, Warning Service module, meeting
Service module, library service module, training service module, are deployed in cloud application server；
Described monitoring service module, can to the running status of plant equipment, live scene etc. there is provided figure, chart,
The real-time visual monitoring service of the ways of presentation such as image, video, and the process backtracking service in the specified period；
Described diagnostic service module, is different difficulty, different levels by built-in knowledge base, model library, state repository etc.
Diagnostic requirements provide expert diagnosis service；
Described expert diagnosis service, using the BP neural network method for diagnosing faults based on genetic algorithm optimization, it is calculated
Method step is as follows：
Step 1：It is determined that input, output vector：
According to the aufbauprinciple of Boolean matrix, it is located in fault diagnosis, characteristic parameter has m, i.e. input feature value P=
(s1,s2,…,sm), fault type to be identified has n, i.e. output characteristic vector Q=(r1,r2,…,rn)；According to fuzzy clustering
Analysis, rj(j ∈ [1, n]) value between (0,1), judges rjThe reason for middle degree of membership the maximum occurs for component failure；
Step 2：Choose the network number of plies：
Using three layers of BP neural network, respectively input layer, hidden layer, output layer.It is inputting according to step 1, defeated
Outgoing vector, it is a to determine input layer number, and wherein a=m, output layer neuron number is b, wherein b=n；
Step 3：Calculate hidden layer neuron number：
Hidden layer neuron number is by formulaIt is determined that, x is a constant, value [1,10] it
Between；
Step 4：Set initial weight：
Initial weight is set as the random number between [- 1,1]；
Step 5：Set learning rate：
Learning rate is set as the random number between [0.01,0.8]；
Step 6：The initial weight and learning rate of BP neural network are optimized using genetic algorithm, it is to avoid follow-up
E-learning is absorbed in local minimum, comprises the following steps：
Step 6.1：Genetic algorithm is determined according to the input layer of BP neural network, hidden layer and output layer neuron number
Code length L
L=a*b+b*h+h*a；
Step 6.2：Determine the fitness function of genetic algorithm；
Step 6.3：New population at individual is produced by the selection of genetic algorithm, intersection and mutation operation；
Step 6.4：According to code length and fitness function, the fitness value of population at individual is calculated, if the fitness value
Adaptive optimal control degree is met, then step 6.3 is obtained to population at individual as optimal individual and is output to BP neural network as initial
Weights and learning rate, into step 6.5, otherwise proceed the operation of step 6.3；
Step 6.5：Judge whether genetic algorithm has reached the maximum evolutionary generation of setting, optimal solution is exported if reaching and is made
For the initial weight and learning rate of BP neural network, into step 7, step 6.3 is otherwise gone to；
Step 7：Characteristic vector is grouped：
Input vector P is divided to for two groups, one group carries out e-learning as learning sample data, uses XPRepresent, another group
As diagnostic analysis data, Y is usedPRepresent；
Step 8：E-learning, comprises the following steps：
Step 8.1：Initial weight, learning rate and the learning sample data X that step 6.4 and step 6.5 are obtainedPInput
The input layer of BP neural network, calculates hidden layer, the output of each neuron of output layer；
Step 8.2：Calculate output layer desired output and the deviation E of real output valueP；
Step 8.3：If EPTraining error condition is met, then e-learning terminates, into step 9, conversely, then adjusting defeated
Go out the weights of layer and hidden layer, return to step 8.1 continues to learn, and the rest may be inferred, until deviation EpIt is eligible；
Step 8.4：The corresponding weights that the final weights that e-learning is drawn are analyzed as follow-up diagnosis, and diagnosed
The algorithm model of analysis；
Step 9：Diagnostic analysis：
By diagnostic analysis data YPThe algorithm model that input step 8.3 is drawn, calculates real output value (degree of membership), if being subordinate to
Category degree thinks that equipment working condition exists for failure more than 0.8, on the contrary, then it is assumed that equipment working condition is normal.
Described Warning Service module, by the technologies such as the detection of early stage small fault, time prediction, qualitative analysis there is provided
Fault pre-alarming service to plant equipment, and warning information can be passed through sound alarm, mail notification, SMS notification, automatic language
The forms such as sound phone, inform related personnel in time；
Described conference service module, provides video conference, voice conferencing, electronics white for equipment manufacturers and manufacturing enterprise
Plate, file-sharing, Desktop Share, collaborative browse, electronic voting etc. are remotely linked up and collaboration services；
Described library service module, the users at different levels for equipment manufacturers and manufacturing enterprise provide conveniently information
Or the service such as document upload, shared, inquiry；
Described training service module, facilitates on-line teaching, and equipment manufacturers can provide remote training for manufacturing enterprise
With interactive teaching service.
It is used as the prioritization scheme of the present invention, described telecommunication gateway unit, using server cluster and load balancing
Technology is disposed, to support high concurrent to access；
It is used as the prioritization scheme of the present invention, described cloud storage administrative unit, using distributed data base, server cluster
Disposed with load-balancing technique, to support the management of magnanimity characteristic vector data and high concurrent to access；
It is used as the prioritization scheme of the present invention, described cloud service center unit, using server cluster and load balancing skill
Art is disposed, to support high concurrent to access.
Advantages of the present invention and good effect are：
(1) the cloud service building mode of the invention based on loose coupling so that the reusable Du Genggao of functional module, it is easy to be
The extension of system, realizes and carries out effective integration to the resource of scattered, isomery, can easily provide under suitable cross-region environment
Long-range monitoring and fault diagnosis service；
(2) present invention creatively applies the BP neural network method for diagnosing faults based on genetic algorithm optimization in machinery
Equipment fault diagnosis, is effectively improved the diagnostic accuracy of diagnostic system.
(3) present invention can provide plant equipment remote online monitoring of working condition for the technical staff of equipment manufacturers, improve
The remote fault diagnosis level of plant equipment, reduces going on business for technical staff, reduces Site Service workload；Can be production enterprise
The plant equipment that industry is used provides quick, accurate, the efficient diagnosis of complex fault, shortens the response time of maintenance request, improves
Maintenance efficiency, reduces the loss caused by maintenance shut-downs；The machinery that equipment manufacturers are used by telecommunication network for manufacturing enterprise
Equipment completes periodic maintenance, data monitoring, system upgrade, failure consulting, coordination diagnosis and maintenance service, reduces product maintenance
Expense, reaches green manufacturing, production and the target safeguarded；
(4) present invention can real-time monitoring equipment state and parameter, pinpoint the problems immediately, realize the transparence of equipment operation
Management, possess complete device history data, can retrospective analysis failure, by monitoring the working order of production equipment in real time, no
Disconnected optimizing process, so as to improve product quality, improves production efficiency；By remotely monitor and fault diagnosis maintenance function
The behaviour in service and development trend of equipment are fed back to and manufacture and design department, constantly improve and can improve equipment, contribute to reality
Existing equipment from design, manufacture, install, operation, superseded lifecycle management.
Brief description of the drawings
Fig. 1 is structural representation of the invention；
Fig. 2 is data acquisition unit structure chart of the invention；
Fig. 3 is intelligent communication gateway unit structure chart of the invention；
Fig. 4 is cloud storage administrative unit structure chart of the invention；
Fig. 5 is cloud service center cellular construction figure of the invention；
Fig. 6 is remote diagnosis data flowchart of the invention；
Fig. 7 is diagnosis algorithm flow chart of the invention.
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
Embodiment
The embodiment party of the present invention is described in detail using the long-range monitoring and fault diagnosis systems of RT-RMDS as prototype in the present embodiment
Formula.The long-range monitoring and fault diagnosis systems of RT-RMDS, based on cloud service framework, for large-scale, complicated, the high skill in manufacturing industry
The characteristic distributions of art equipment, by data acquisition, network communication and fault diagnosis, will be distributed independent equipment contact is mutually association
The organism of work, is timely responded to realizing to status monitoring and failure disposal, and with resource-sharing, remote collaboration, data
The functions such as exchange.
As shown in figure 1, a kind of long-range monitoring and fault diagnosis system based on cloud service, including data acquisition unit 100,
Telecommunication gateway unit 200, cloud storage administrative unit 300, cloud service center unit 400；
Between described data acquisition unit 100 and described telecommunication gateway unit 200, connected using 4G networks
Connect；
Between described telecommunication gateway unit 200 and described cloud storage administrative unit 300, described telecommunication
Cloud storage administrative unit 300 between gateway unit 200 and described cloud service center unit 400, described takes with described cloud
It is engaged between center cell 400, is attached using Internet/Intranet networks；
Described cloud service center unit 400, using Internet/4G networks, provides a user WEB/APP/WAP circle
The cloud service in face；
As shown in Fig. 2 described data acquisition unit 100, including it is sensor assembly 101, controller module 102, long-range
Communication module 103, is deployed on data collection station；
Described sensor assembly 101, according to default frequency acquisition, gathers industrial site various kinds of sensors in real time
The working condition signal data perceived, send remote communication module 103 to；
Described controller module 102, the control data that real-time reception remote communication module 103 is transmitted, according to control
The frequency acquisition of data mutual transmission sensor module 101, remote communication module 103 report the parameters such as frequency to be configured and change；
Coordinate shown in Fig. 6, described remote communication module 103, on the one hand, real-time reception telecommunication gateway unit 200
The downlink command issued, it then follows telecommunication agreement, analysis instruction data, and the control data that will be obtained after parsing, send to
Controller module 102, on the other hand, the working condition signal data that real-time reception sensor assembly 101 is transmitted, it then follows telecommunication
Agreement, encapsulates working condition signal data, and the director data that will be obtained after encapsulation, and frequency is reported according to default, sends up refer to
Order, real-time report is to telecommunication gateway unit 200；
As shown in figure 3, described telecommunication gateway unit 200, including Communications service module 201, data prediction clothes
Business module 202, intelligent adaptation service module 203, deployment is beyond the clouds on preposition gateway server；
Coordinate shown in Fig. 6, described Communications service module 201, on the one hand, real-time reception data acquisition unit 100 is reported
Up-on command, it then follows telecommunication agreement, analysis instruction data, and by the working condition signal data obtained after parsing, send to
Data prediction service module 202, on the other hand, the control data transmitted by real-time reception cloud service center unit 400, it then follows
Telecommunication agreement, encapsulates control data, and the director data that will be obtained after encapsulation, sends downlink command, real time down is to number
According to collecting unit 100；
Described data prediction service module 202, the working condition signal number that real-time reception Communications service module 201 is transmitted
According to, progress data prediction, and cloud storage administrative unit 300 is sent in real time；
Described intelligent adaptation service module 203, there is provided mode adapter, is the plant equipment of different model, is specified not
Same telecommunication agreement；
As shown in figure 4, described cloud storage administrative unit 300, including data storage service module 301, data retrieval clothes
Business module 302, data encryption services module 303, deployment is beyond the clouds on data server；
Described data storage service module 301, feature transmitted by real-time reception telecommunication gateway unit 200 to
Data are measured, are stored into corresponding database；
Described data retrieval service module 302, the inquiry request transmitted by real-time reception cloud service center unit 400,
Using data retrieval technology, target data is obtained from database, and target data is returned into cloud service center unit 400；
Described data encryption services module 303, is some sensitive or crucial spies using the ripe close encryption technology of business
The storage for levying vector data is serviced there is provided reliable encryption, decryption；
As shown in figure 5, described cloud service center unit 400, including monitoring service module 401, diagnostic service module
402nd, Warning Service module 403, conference service module 404, library service module 405, training service module 406, are deployed in cloud
On end application server；
Described monitoring service module 401, can there is provided figure, figure to the running status of plant equipment, live scene etc.
The real-time visual monitoring service of the ways of presentation such as table, image, video, and the process backtracking service in the specified period；
Described diagnostic service module 402, is different difficulty, difference by built-in knowledge base, model library, state repository etc.
The diagnostic requirements of level provide expert diagnosis service；
Coordinate shown in Fig. 7, described expert diagnosis service, using the BP neural network failure based on genetic algorithm optimization
Diagnostic method, its algorithm steps are as follows：
Step 1：It is determined that input, output vector：
According to the aufbauprinciple of Boolean matrix, be located in fault diagnosis, characteristic parameter has a m, i.e., input vector (feature to
Amount) P=(s1,s2,…,sm), fault type to be identified has n, i.e. output vector Q=(r1,r2,…,rn)；According to fuzzy poly-
Alanysis, rj(j ∈ [1, n]) value between (0,1), judges rjMiddle degree of membership the maximum is the original that component failure occurs
Cause；
Step 2：Choose the network number of plies：
Using three layers of BP neural network, respectively input layer, hidden layer, output layer.It is inputting according to step 1, defeated
Outgoing vector, it is a to determine input layer number, and wherein a=m, output layer neuron number is b, wherein b=n；
Step 3：Calculate hidden layer neuron number：
Hidden layer neuron number is by formulaIt is determined that, x is a constant, value [1,10] it
Between；
Step 4：Set initial weight：
Initial weight is set as the random number between [- 1,1]；
Step 5：Set learning rate：
Learning rate is set as the random number between [0.01,0.8]；
Step 6：The initial weight and learning rate of BP neural network are optimized using genetic algorithm, it is to avoid follow-up
E-learning is absorbed in local minimum, comprises the following steps：
Step 6.1：Genetic algorithm is determined according to the input layer of BP neural network, hidden layer and output layer neuron number
Code length L
L=a*b+b*h+h*a；
Step 6.2：Determine the fitness function of genetic algorithm；
Step 6.3：New population at individual is produced by the selection of genetic algorithm, intersection and mutation operation；
Step 6.4：According to code length and fitness function, the fitness value of population at individual is calculated, if the fitness value
Adaptive optimal control degree is met, then step 6.3 is obtained to population at individual as optimal individual and is output to BP neural network as initial
Weights and learning rate, into step 6.5, otherwise proceed the operation of step 6.3；
Step 6.5：Judge whether genetic algorithm has reached the maximum evolutionary generation of setting, optimal solution is exported if reaching and is made
For the initial weight and learning rate of BP neural network, into step 7, step 6.3 is otherwise gone to；
Step 7：Characteristic vector is grouped：
Input vector P is divided to for two groups, one group carries out e-learning as learning sample data, uses XPRepresent, another group
As diagnostic analysis data, Y is usedPRepresent；
Step 8：E-learning, comprises the following steps：
Step 8.1：Initial weight, learning rate and the learning sample data X that step 6.4 and step 6.5 are obtainedPInput
The input layer of BP neural network, calculates hidden layer, the output of each neuron of output layer；
Step 8.2：Calculate output layer desired output and the deviation E of real output valueP；
Step 8.3：If EPTraining error condition is met, then e-learning terminates, into step 9, conversely, then adjusting defeated
Go out the weights of layer and hidden layer, return to step 8.1 continues to learn, and the rest may be inferred, until deviation EpIt is eligible；
Step 8.4：The corresponding weights that the final weights that e-learning is drawn are analyzed as follow-up diagnosis, and diagnosed
The algorithm model of analysis；
Step 9：Diagnostic analysis：
By diagnostic analysis data YPThe algorithm model that input step 8.3 is drawn, it is degree of membership to calculate real output value, if being subordinate to
Category degree thinks that equipment working condition exists for failure more than 0.8, on the contrary, then it is assumed that equipment working condition is normal.
Described Warning Service module 403, by technologies such as the detection of early stage small fault, time prediction, qualitative analyses, is carried
For the fault pre-alarming service to plant equipment, and warning information can be passed through sound alarm, mail notification, SMS notification, automatic
The forms such as voice call, inform related personnel in time；
Described conference service module 404, is that equipment manufacturers and manufacturing enterprise provide video conference, voice conferencing, electricity
Sub- blank, file-sharing, Desktop Share, collaborative browse, electronic voting etc. are remotely linked up and collaboration services；
Described library service module 405, the users at different levels for equipment manufacturers and manufacturing enterprise provide conveniently
The services such as information or document are uploaded, shared, inquiry；
Described training service module 406, facilitates on-line teaching, and equipment manufacturers can provide long-range training for manufacturing enterprise
Instruction and interactive teaching service.
As the prioritization scheme of the present invention, described telecommunication gateway unit 200 is equal using server cluster and load
Weighing apparatus technology is disposed, to support high concurrent to access；
It is used as the prioritization scheme of the present invention, described cloud storage administrative unit 300, using distributed data base, server
Cluster and load-balancing technique deployment, to support the management of magnanimity characteristic vector data and high concurrent to access；
It is used as the prioritization scheme of the present invention, described cloud service center unit 400, using server cluster and load balancing
Technology is disposed, to support high concurrent to access.
The present invention preferably embodiment is these are only, but protection scope of the present invention is not limited thereto, it is any
Those familiar with the art the change that can readily occur in or replaces in the technical scope that the embodiment of the present invention is disclosed
Change, should all be included within the scope of the present invention.
Claims (7)
1. a kind of long-range monitoring and fault diagnosis system based on cloud service, it is characterised in that：Including data acquisition unit, remotely
Communication Gateway unit, cloud storage administrative unit, cloud service center unit；The data acquisition unit is deployed in showing for plant equipment
, telecommunication gateway unit and described cloud between data acquisition unit and described telecommunication gateway unit, described
Between telecommunication gateway unit between MMU memory management unit, described and described cloud service center unit, described cloud deposits
It is attached between storage administrative unit and described cloud service center unit using network；
Described cloud service center unit, including monitoring service module, diagnostic service module and Warning Service module, monitoring service
Module, diagnostic service module, Warning Service module are deployed in cloud application server respectively；
Described monitoring service module, the mistake in real-time visual monitoring service, and specified period is provided to plant equipment
Journey backtracking service；
Described diagnostic service module is different difficulty, the diagnostic requirements of different levels provide expert diagnosis service；
Described Warning Service module provides the fault pre-alarming service to plant equipment, and warning information is informed into relevant people in time
Member.
2. a kind of long-range monitoring and fault diagnosis system based on cloud service according to claim 1, it is characterised in that：
Between described data acquisition unit and described telecommunication gateway unit, it is attached using 4G networks；
Between described telecommunication gateway unit and described cloud storage administrative unit, described telecommunication gateway unit with
Between cloud storage administrative unit between described cloud service center unit, described and described cloud service center unit, use
Internet/Intranet networks are attached；
Described cloud service center unit, using Internet or 4G networks, provides a user the cloud clothes at WEB/APP/WAP interfaces
Business.
3. a kind of long-range monitoring and fault diagnosis system based on cloud service according to claim 1, it is characterised in that institute
The data acquisition unit stated includes：Sensor assembly, controller module and remote communication module, each module of data acquisition unit
It is separately positioned on data collection station；
The working condition signal data that described sensor assembly collection sensor is perceived send remote communication module to；
Described controller module receives the control data that remote communication module is transmitted；
Described remote communication module receives the downlink command of telecommunication gateway unit, sends control data and gives controller mould
Block；Described remote communication module also receives the working condition signal data that sensor assembly is transmitted, and concurrently send up-on command to remote
Journey Communication Gateway unit.
4. a kind of long-range monitoring and fault diagnosis system based on cloud service according to claim 1, it is characterised in that institute
The telecommunication gateway unit stated includes：Communications service module, data prediction service module, intelligent adaptation service module, far
Each module deployment of journey Communication Gateway unit is beyond the clouds on preposition gateway server；
Described Communications service module receives the up-on command of data acquisition unit, sends working condition signal data to data prediction
Service module, the Communications service module also receives the control data transmitted by cloud service center unit, and sends downlink command
To data acquisition unit；
Described data prediction service module receives the working condition signal data that Communications service module is transmitted, and sends after pretreatment
Obtained characteristic vector data gives cloud storage administrative unit；
Described intelligent adaptation service module, for the plant equipment of correspondence model, specifies corresponding long-range there is provided mode adapter
Communications protocol.
5. a kind of long-range monitoring and fault diagnosis system based on cloud service according to claim 1, it is characterised in that institute
The cloud storage administrative unit stated includes：Data storage service module, data retrieval service module and data encryption services module, cloud
Each module deployment of MMU memory management unit is beyond the clouds on data server；
The characteristic vector data storage that described data storage service module receives transmitted by telecommunication gateway unit arrives corresponding
Database in；
Described data retrieval service module receives the inquiry request transmitted by cloud service center unit, and mesh is obtained from database
Mark data return to cloud service center unit.
6. a kind of long-range monitoring and fault diagnosis system based on cloud service according to claim 1, it is characterised in that institute
The cloud service center unit stated also includes conference service module, library service module, training service module, and each module is deployed in cloud
On end application server；
Described conference service module, long-range link up and collaboration services such as provides for equipment manufacturers and manufacturing enterprise；
Described library service module, the users at different levels for equipment manufacturers and manufacturing enterprise provide information or document is uploaded, altogether
Enjoy, inquire about service；
Described training service module, facilitates on-line teaching, and equipment manufacturers provide remote training and interactive religion for manufacturing enterprise
Learn service.
7. according in claim 1 to 6, a kind of long-range monitoring and fault diagnosis system based on cloud service described in any one
Method for diagnosing faults, it is characterised in that comprise the following steps：
Step 1：It is determined that input, output vector：
According to the aufbauprinciple of Boolean matrix, it is defined in fault diagnosis, characteristic parameter has m, i.e. input feature value P=
(s1,s2,…,sm), fault type to be identified has n, i.e. output characteristic vector Q=(r1,r2,…,rn)；According to fuzzy clustering
Analysis, rj(j ∈ [1, n]) value between (0,1), judges rjThe reason for middle degree of membership the maximum occurs for component failure；
Step 2：Choose the network number of plies：
Using three layers of BP neural network, respectively input layer, hidden layer, output layer.Input feature vector according to step 1 to
Amount, output characteristic vector, it is a to determine input layer number, and wherein a=m, output layer neuron number is b, wherein b=
n；
Step 3：Calculate hidden layer neuron number：
Hidden layer neuron number is by formulaIt is determined that, x is a constant, and value is between [1,10]；
Step 4：Set initial weight：
Initial weight is set as the random number between [- 1,1]；
Step 5：Set learning rate：
Learning rate is set as the random number between [0.01,0.8]；
Step 6：The initial weight and learning rate of BP neural network are optimized using genetic algorithm, it is to avoid follow-up network
Study is absorbed in local minimum, comprises the following steps：
Step 6.1：The volume of genetic algorithm is determined according to the input layer of BP neural network, hidden layer and output layer neuron number
Code length L
L=a*b+b*h+h*a；
Step 6.2：Determine the fitness function of genetic algorithm；
Step 6.3：New population at individual is produced by the selection of genetic algorithm, intersection and mutation operation；
Step 6.4：According to code length and fitness function, the fitness value of population at individual is calculated, if the fitness value is met
Adaptive optimal control degree, then obtain population at individual using step 6.3 as optimal individual and be output to BP neural network as initial weight
And learning rate, into step 6.5, otherwise proceed the operation of step 6.3；
Step 6.5：Judge whether genetic algorithm has reached the maximum evolutionary generation of setting, optimal solution is exported if reaching and is used as BP
The initial weight and learning rate of neutral net, into step 7, otherwise go to step 6.3；
Step 7：Characteristic vector is grouped：
Input feature value P is divided to for two groups, one group carries out e-learning as learning sample data, uses XPRepresent, another group of work
For diagnostic analysis data, Y is usedPRepresent；
Step 8：E-learning, comprises the following steps：
Step 8.1：Initial weight, learning rate and the learning sample data X that step 6.4 and step 6.5 are obtainedPInput BP god
Input layer through network, calculates hidden layer, the output of each neuron of output layer；
Step 8.2：Calculate output layer desired output and the deviation E of real output valueP；
Step 8.3：If EPMeet training error condition, then e-learning terminates, into step 9, conversely, then adjustment output layer and
The weights of hidden layer, return to step 8.1 continues to learn, and the rest may be inferred, until deviation EpIt is eligible；
Step 8.4：The corresponding weights that the final weights that e-learning is drawn are analyzed as follow-up diagnosis, and obtain diagnostic analysis
Algorithm model；
Step 9：Diagnostic analysis：
By diagnostic analysis data YPThe algorithm model that input step 8.3 is drawn, it is degree of membership to calculate real output value, if degree of membership
Think that equipment working condition exists for failure more than 0.8, it is on the contrary, then it is assumed that equipment working condition is normal.
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