CN107966942A - A kind of equipment fault forecasting system in knowledge based storehouse - Google Patents
A kind of equipment fault forecasting system in knowledge based storehouse Download PDFInfo
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- CN107966942A CN107966942A CN201710460956.3A CN201710460956A CN107966942A CN 107966942 A CN107966942 A CN 107966942A CN 201710460956 A CN201710460956 A CN 201710460956A CN 107966942 A CN107966942 A CN 107966942A
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- 238000012806 monitoring device Methods 0.000 claims abstract description 20
- 230000005540 biological transmission Effects 0.000 claims abstract description 6
- 238000012544 monitoring process Methods 0.000 claims description 28
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 8
- 238000000034 method Methods 0.000 claims description 8
- 230000002123 temporal effect Effects 0.000 claims description 8
- 238000005065 mining Methods 0.000 claims description 5
- 238000012706 support-vector machine Methods 0.000 claims description 5
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- 230000004888 barrier function Effects 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000003860 storage Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 2
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- 238000004458 analytical method Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000007850 degeneration Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
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- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
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- 230000035772 mutation Effects 0.000 description 1
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- 239000007787 solid Substances 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/048—Monitoring; Safety
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
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- 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
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Abstract
The invention discloses a kind of equipment fault forecasting system in knowledge based storehouse,Device data acquisition module is equipped with RFD identities block and sensor gathered data block,Device data acquisition module is directly connected with monitoring device,Equipment life prediction module is connected in the knowledge acquisition module,Equipment life prediction module is connected with failure modes and identification module,Device data acquisition module is connected by Zigbee transmission data wires with cloud server,It is connected again by cloud server with knowledge acquisition module,The knowledge acquisition module,Device data acquisition module,Can touch-control terminal display module,Failure modes and identification module are connected with operation management module respectively,Utilize each equipment of RFID identification,Easy to the binding of position determination of fault and database,It is described can touch control terminal real-time display equipment state,There is warning function at the same time,Beneficial to high-speed decision and response.
Description
Technical field
The present invention relates to monitoring of tools technical field, is specially a kind of equipment fault forecasting system in knowledge based storehouse.
Background technology
Although machinery equipment has the theoretical service life, but in actual use can be multiple because of vibration, burn into stress, temperature etc.
The comprehensive function of factor and produce mutation failure or soft fault.In the flow of traditional maintaining, using work
People inspects periodically maintenance, but such flow can neither find the trend of soft fault, can not reduce the damage of mutation failure
Lose, and once break down and will result in sizable drain on manpower and material resources, bring massive losses.Therefore it is special by analyzing
The historical variations trend of parameter is levied, using nonlinear mixed model theoretical prediction equipment concrete surplus working life, can greatly be solved
The above problem.But further refinement monitoring and failure modes identification cannot get effective early warning and supervision.
In device data acquisition mode, current most of device parameters for being collected sensor using ZigBee technology
It is transmitted.Although Zigbee has the characteristics that low in energy consumption, cost is low, response is fast, its speed transmitted is relatively also very slow,
It can only carry out the transmission of digital category information.Simultaneously because its transmission range is limited, so needing the data for carrying out a small range to converge
Always, then reach Cloud Server for equipment life prediction and failure modes identification provides data support, be research and development application a side
To.
Equipment fault can be classified and be identified using support vector machines theory.Primal problem may be limited at one
In dimension space, often occur to differentiate whether it can divide in the spatial linear.If the original finite dimensional space is mapped to one
The solid space of a higher-dimension, can be such that it separates in space and be more prone to.Keep calculated load reasonable, use support vector machines
The mapping of plan is designed to ensure that in dot product, can be readily calculated for the variable in former space, by defining in them
The calculating of the kernel function k (x, y) of selection solves.But the theory is suitable for the classification of limited a small amount of parameter, it is therefore desirable to
Suitable characteristic parameter screening is carried out in data knowledge acquisition module, and emphasis is used applied to monitoring.
The content of the invention
It is an object of the invention to provide a kind of equipment fault forecasting system in knowledge based storehouse, possess while can pass through
Operation management theory saves manpower financial capacity and finally can be with the excellent of the equipment fault forecasting system of high-speed decision response
Point, solves the problems, such as that equipment fault is inscrutable.
To achieve the above object, the present invention provides following technical solution：A kind of equipment fault prediction system in knowledge based storehouse
System, including monitoring device, device data acquisition module, knowledge acquisition module, equipment life prediction module, failure modes and identification
Module, can touch-control terminal display module and operation monitoring management module, it is characterised in that：Device data acquisition module is equipped with
RFD identities block and sensor gathered data block, device data acquisition module are directly connected with monitoring device, the knowledge
Equipment life prediction module is connected in acquisition module, equipment life prediction module is connected with failure modes and identification module, equipment
Data acquisition module is connected by Zigbee transmission data wires with cloud server, then passes through cloud server and knowledge acquisition mould
Block connects, the data knowledge acquisition module, device data acquisition module, can the terminal display module of touch-control, failure modes and
Identification module is connected with operation management module respectively.
The RFD identities block of the data acquisition module includes RFID reader and RFID device label and is marked with RFID
Know identity, the RFID identities, RFID device label are located in monitoring device, and the RFID reader passes through read-write
RFID device label is connected with the sensing of RFID identities；Sensor gathered data block includes pressure sensing and temperature sensor,
The pressure sensing and temperature sensor are separately mounted to be located on monitoring device suitable position；
It is described can the terminal display module of touch-control be equipped with alarm；
The operation management module is equipped with routine observation and emphasis monitors, the routine observation and data acquisition transport module
Connection, the emphasis monitoring are connected with alarm.
The equipment life prediction module is with artificial neural network algorithm and temporal sequence association rule life prediction and company
The prediction module connect, the failure modes and identification module are a kind of classification blocks with support vector machines theory, and described sets
Standby life prediction module and failure modes and identification module are connected to form the knowledge base of prediction and classification.
The knowledge acquisition module be it is a kind of have to refine equipment fault case using dynamic property degraded data storehouse be
Model and store be sent to cloud server.
The equipment life prediction module is that a kind of employment artificial neural networks are combined with temporal sequence association rule mining algorithm
The soft fault and catastrophic failure of equipment are predicted.
The failure modes and identification module are classified and are identified using the model in knowledge base.
It is described can touch control terminal display module be a kind of to simplify decision process using touch screen technology and multi-windowing.
The operation monitoring management module is connected using routine observation and emphasis monitoring.
The device data acquisition module is registered using RFID technique for the identity of each monitoring device.
The operation monitoring management module is identical using intelligent algorithm real-time estimate according to history case data knowledge base
The concrete surplus working life of the monitoring device of classification, and equipment operation condition is divided into three types：1. parameter is normal at this stage, hair
It is normal to open up Trend Stationary；2. parameter is normal at this stage, but shows bad trend；3. parameter burst is abnormal.For every species
Type takes different monitoring management means.
The touch control terminal display module is a kind of portable terminal computer or touch screen all-in-one machine
Compared with prior art, beneficial effects of the present invention are as follows：
1. the technical program combines the RFID technique of forefront in logistic industry at present, fully identity has been used uniquely to change
Theory, allow equipment to be respectively provided with oneself independent database in failure predication.
2. in the operation management theory of specific implementation the system, routine observation and emphasis monitoring are combined, and are based on knowing
Storehouse is known on the basis of equipment concrete surplus working life, is carried out equipment fault classification and identification, has effectively been saved the manpower of factory
With financial resources.
3. storing data to cloud server, long-range execution is operated in more convenient for arrangement and the analysis of data.
4. alarm module can be both being with the addition of, the response for failure is more obvious, allows whole in touch control display terminal module
Body failure predication and diagnostic process more lean；It can be connected again with portable terminal, make the response of whole system with determining
Plan more high-speeding.
Brief description of the drawings
Fig. 1 is the equipment fault forecasting system module map in knowledge based storehouse of the present invention.
Fig. 2 is the equipment fault forecasting system block diagram in knowledge based storehouse of the present invention.
Fig. 3 be knowledge based storehouse of the present invention equipment fault forecasting system in equipment predicting residual useful life and failure modes and
The reference flow sheet of identification.
In figure：1 device data acquisition module, 2 knowledge acquisition modules, 3 equipment life prediction modules, 4 failure modes and knowledge
Other module, 5 can touch-control terminal display module, 6 operation monitoring management modules.
Embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other without making creative work
Embodiment, belongs to the scope of protection of the invention.
~3 are please referred to Fig.1, a kind of equipment fault forecasting system in knowledge based storehouse, including monitoring device, device data are adopted
Collection module 1, knowledge acquisition module 2, equipment life prediction module 3, failure modes and identification module 4, can the terminal of touch-control show
Module 5 and operation monitoring management module 6, it is characterised in that：Device data acquisition module 1 is equipped with RFD identities block and biography
Sensor gathered data block, device data acquisition module 1 are directly connected with monitoring device, are connected in the knowledge acquisition module 2
Equipment life prediction module 3, equipment life prediction module 3 are connected with failure modes and identification module 4, device data acquisition module
1 is connected by Zigbee transmission data wires with cloud server, then is connected by cloud server with knowledge acquisition module 2, institute
State knowledge acquisition module 2, device data acquisition module 1, can terminal display module 5, failure modes and the identification module 4 of touch-control divide
It is not connected with operation management module 6.
The RFD identities block of the data acquisition module 1 includes RFID reader and RFID device label and RFID
Identity, the RFID identities, RFID device label are located in monitoring device, and the RFID reader passes through read-write
RFID device label is connected with the sensing of RFID identities；Sensor gathered data block includes pressure sensing and temperature sensor,
The pressure sensing and temperature sensor are separately mounted to be located on monitoring device suitable position；
It is described can the terminal display module 5 of touch-control be equipped with alarm；
The operation management module is equipped with routine observation and emphasis monitors, the routine observation and data acquisition transport module
Connection, the emphasis monitoring are connected with alarm.
The equipment life prediction module 3 is with artificial neural network algorithm and temporal sequence association rule life prediction and company
The prediction module connect, the failure modes and identification module are a kind of classification blocks with support vector machines theory, and described sets
Standby life prediction module and failure modes and identification module are connected to form the knowledge base of prediction and classification.
The knowledge acquisition module 2 be it is a kind of have to refine equipment fault case using dynamic property degraded data storehouse be
Model and store be sent to cloud server.
The equipment life prediction module 3 is that a kind of employment artificial neural networks are combined with temporal sequence association rule mining algorithm
The soft fault and catastrophic failure of equipment are predicted.
The failure modes and identification module 4 are classified and are identified using the model in knowledge base.
Wherein knowledge acquisition module 2, equipment life prediction module 3, failure modes and identification module 4 can be a kind of existing
Band storage and computing function active computer.
It is described can touch control terminal display module 5 be a kind of to simplify decision process using touch screen technology and multi-windowing.
The operation monitoring management module profit 6 is connected with routine observation and emphasis monitoring.Runing monitoring management module profit 6 can
Be it is a kind of equipped with monitoring, management system software and with storage and computing function industrial personal computer or computer.
The device data acquisition module 1 is registered using RFID technique for the identity of each monitoring device.
The operation monitoring management module 6 utilizes intelligent algorithm real-time estimate phase according to history case data knowledge base
The concrete surplus working life of generic monitoring device, and equipment operation condition is divided into three types：1. parameter is normal at this stage,
Development trend is steadily normal；2. parameter is normal at this stage, but shows bad trend；3. parameter burst is abnormal.For every kind of
Type takes different monitoring management means.
The touch control terminal display module 5 is a kind of portable terminal computer or touch screen all-in-one machine.
First with ZigBee technology by the operation data transfer of monitoring device to knowledge acquisition in the data acquisition module 1
In module 2, and utilize each equipment of RFID identification, easy to the binding of position determination of fault and database, can touch control terminal it is real-time
Display device state, easy to operate, more convenient inquiry and acquisition of information, while there is warning function, beneficial to high-speed decision with
Response.It can be assemblied at general headquarters expert and one line production unit of factory, and in a line field progressively development of small-scale portable terminal
End, fault identification utilize the theory of data mining with sort module.
It is described can touch control terminal operation module be transferred to decision-making and show,
The equipment life prediction module 3 is that a kind of employment artificial neural networks are combined with temporal sequence association rule mining algorithm
The soft fault and catastrophic failure of equipment are predicted.
The failure modes and identification module 4 are classified and are identified using the model in knowledge base, pass through setting for acquisition
For parameter information by faulty progress classified storage to case library, and therefrom extraction model feature, for the failure of kainogenesis
Model classifications are carried out, further sort out regulation so that decision-making uses, knowledge acquisition module utilizes dynamic property degeneration number
Equipment fault case is refined according to storehouse and is sent to cloud server for model and storage, so as to meet other clients for this number
According to calling demand, be combined pair with temporal sequence association rule mining algorithm using artificial neural network in equipment life prediction module
The soft fault and catastrophic failure of equipment are predicted, and support, failure modes and identification are provided for Fault Tree Diagnosis Decision module
Module is classified and is identified using the model in knowledge base, and equipment identities can utilize touch screen technology in touch control terminal display module
And multi-windowing simplifies decision process, the mode section that operation monitoring management module is combined using routine observation with emphasis monitoring
The about manpower and financial resources of factory, and alarm is sent when being likely to occur significant problem, device data acquisition module utilize RFID
Technology is registered for the identity of each equipment, by data acquisition and is transmitted to cloud server, and from substantial amounts of data
Sort out required knowledge parameter, so as to form each specific exclusive parameter database of equipment, operation monitoring management mould
Root tuber is according to history case data knowledge base, using the concrete surplus working life of the equipment of intelligent algorithm real-time estimate identical category, and
Equipment operation condition is divided into three types：1. parameter is normal at this stage, development trend is steadily normal；2. parameter is at this stage just
Often, but bad trend is showed；3. parameter burst is abnormal.Different monitoring management means are taken for each type.
Although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
Understanding without departing from the principles and spirit of the present invention can carry out these embodiments a variety of changes, modification, replace
And modification, the scope of the present invention is defined by the appended.
Claims (10)
1. a kind of equipment fault forecasting system in knowledge based storehouse, including monitoring device, device data acquisition module (1), knowledge
Acquisition module (2), equipment life prediction module (3), failure modes and identification module (4), can touch-control terminal display module (5)
And operation monitoring management module (6), it is characterised in that：Device data acquisition module (1) is equipped with RFD identities block and sensing
Device gathered data block, device data acquisition module (1) are directly connected with monitoring device, and the knowledge acquisition module connects in (2)
Equipment life prediction module (3) is connect, equipment life prediction module (3) is connected with failure modes and identification module (4), device data
Acquisition module (1) is connected by Zigbee transmission data wires with cloud server, then passes through cloud server and knowledge acquisition mould
Block (2) connect, the knowledge acquisition module (2), device data acquisition module (1), can touch-control terminal display module (5), therefore
Barrier classification and identification module (4) are connected with operation management module (6) respectively.
A kind of 2. equipment fault forecasting system in knowledge based storehouse according to claim 1, it is characterised in that：The number
Include RFID reader and RFID device label and RFID identities according to the RFD identities block of acquisition module (1), it is described
RFID identities, RFID device label are located in monitoring device, and the RFID reader is by reading and writing RFID device label
Sense with RFID identities and connect；Sensor gathered data block includes pressure sensing and temperature sensor, the pressure sensing
It is separately mounted to be located on monitoring device suitable position with temperature sensor；
It is described can the terminal display module (5) of touch-control be equipped with alarm；
The operation management module (6) is equipped with routine observation and emphasis monitors, the routine observation and data acquisition transport module
Connection, the emphasis monitoring are connected with alarm；
The equipment life prediction module (3) is with artificial neural network algorithm and temporal sequence association rule life prediction and connects
Prediction module, the failure modes and identification module are a kind of classification blocks with support vector machines theory, the equipment
Life prediction module and failure modes and identification module are connected to form the knowledge base of prediction and classification.
A kind of 3. equipment fault forecasting system in knowledge based storehouse as claimed in claim 1, it is characterised in that：The knowledge obtains
Modulus block (2) be it is a kind of have to refine equipment fault case using dynamic property degraded data storehouse be sent to for model and storing
Cloud server.
A kind of 4. equipment fault forecasting system in knowledge based storehouse as claimed in claim 1, it is characterised in that：The equipment longevity
It is that a kind of employment artificial neural networks are combined the soft fault to equipment with temporal sequence association rule mining algorithm to order prediction module (3)
And catastrophic failure is predicted.
A kind of 5. equipment fault forecasting system in knowledge based storehouse as claimed in claim 1, it is characterised in that：The failure point
Class and identification module (4) are classified and are identified using the model in knowledge base.
A kind of 6. equipment fault forecasting system in knowledge based storehouse as claimed in claim 1, it is characterised in that：It is described can touch-control
Terminal display module (5) is that one kind simplifies decision process using touch screen technology and multi-windowing.
A kind of 7. equipment fault forecasting system in knowledge based storehouse as claimed in claim 1, it is characterised in that：The operation prison
Management module (6) is surveyed to connect using routine observation and emphasis monitoring.
A kind of 8. equipment fault forecasting system in knowledge based storehouse as claimed in claim 1, it is characterised in that：The equipment
Data acquisition module (1) is registered using RFID technique for the identity of each monitoring device.
A kind of 9. equipment fault forecasting system in knowledge based storehouse as claimed in claim 1, it is characterised in that：The operation
Monitoring management module (6) utilizes the monitoring device of intelligent algorithm real-time estimate identical category according to history case data knowledge base
Concrete surplus working life, and equipment operation condition is divided into three types：1. parameter is normal at this stage, development trend is steadily just
Often；2. parameter is normal at this stage, but shows bad trend；3. parameter burst is abnormal, taken for each type different
Monitoring management means.
A kind of 10. equipment fault forecasting system in knowledge based storehouse as claimed in claim 1, it is characterised in that：Described touches
Control terminal display module (5) is a kind of portable terminal computer or touch screen all-in-one machine.
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| CN201710460956.3A CN107966942A (en) | 2017-06-18 | 2017-06-18 | A kind of equipment fault forecasting system in knowledge based storehouse |
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| CN201710460956.3A CN107966942A (en) | 2017-06-18 | 2017-06-18 | A kind of equipment fault forecasting system in knowledge based storehouse |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109391515A (en) * | 2018-11-07 | 2019-02-26 | 武汉烽火技术服务有限公司 | Network failure prediction technique and system based on dove group's algorithm optimization support vector machines |
| CN112632127A (en) * | 2020-12-29 | 2021-04-09 | 国华卫星数据科技有限公司 | Data processing method for real-time data acquisition and time sequence of equipment operation |
| CN115497267A (en) * | 2022-09-06 | 2022-12-20 | 江西小手软件技术有限公司 | Equipment early warning platform based on time sequence association rule |
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| CN102765643A (en) * | 2012-05-31 | 2012-11-07 | 天津大学 | Elevator fault diagnosis and early-warning method based on data drive |
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