CN106408192A - Mineral processing equipment operating state monitoring system and method - Google Patents
Mineral processing equipment operating state monitoring system and method Download PDFInfo
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- CN106408192A CN106408192A CN201610844083.1A CN201610844083A CN106408192A CN 106408192 A CN106408192 A CN 106408192A CN 201610844083 A CN201610844083 A CN 201610844083A CN 106408192 A CN106408192 A CN 106408192A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0633—Workflow analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
Abstract
The invention provides a mineral processing equipment operating state monitoring system and a mineral processing equipment operating state monitoring method. The mineral processing equipment operating state monitoring system comprises a local server, a plurality of data acquisition sensors and a video acquisition module, wherein input ends of the plurality of data acquisition sensors are connected with monitored equipment in a mining plant, output ends of the plurality of data acquisition sensors are connected with the local server, and an output end of the video acquisition module is connected with the local server. Operating state data and equipment index data of the monitored equipment are acquired by means of the data acquisition sensors in real time, operation videos of the equipment are acquired by means of the video acquisition module in real time, the operating state data and the equipment index data of the equipment are monitored by means of the local server, the local server sends out early warning when the equipment index data exceeds a threshold value, calculates an equipment failure rate of the corresponding equipment and OEE analytical value of the equipment, and performs online diagnosis on the operating state data acquired in real time by utilizing a KPCA model. The mineral processing equipment operating state monitoring system and the mineral processing equipment operating state monitoring method can realize the real-time monitoring of the equipment operating state by the mineral processing production-manufacturing execution layer.
Description
Technical field
The invention belongs to monitoring of tools technical field is and in particular to a kind of preparation equipment running state monitoring system and side
Method.
Background technology
Process industry is one of mainstay industry of Chinese national economy, and its state of development directly affects the economy of country
Basis.Dressing Production Process is typical process industry, and with the aggravation of market competition, enterprise wants victory in keen competition
Go out it is necessary to improve production efficiency is cost-effective.Equipment is the fixed assets of an enterprise, also signifies the strength of enterprise, safeguards
Equipment is a huge expense, and improving utilization rate of equipment and installations and the maintenance cost of reduction business equipment is to reduce having of production cost
Effect approach.Equipment is the main matter basis that enterprise produces, ensure the production efficiency of enterprise, product quality, production cost all with
The technical merit of equipment is closely bound up.Development with manufacturing and manufacturing execution system (MES) in the application of manufacturing enterprise,
Enterprise puts forward higher requirement to equipment control.Thus it is guaranteed that the normal operation of equipment, improve the operational efficiency of equipment, and
When discovering device operation troubles, rational Plant maintenance plan just seems particularly significant.
Dressing Production Process is continuous, and main body production equipment mainly passes through belt or pipeline connects, and produces path and fixes.Ore dressing
Main body production equipment every expensive, device type is fixed, and it plays more next in the Mineral Processing Enterprises strategy of sustainable development
More important effect, the comprehensive performance that correctly assessment equipment is run, for improve product quality, reduces production cost, foundation
The competitive advantage of enterprise is most important.Therefore, strengthen the monitoring to equipment running status, on the one hand to the equipment improving ore dressing plant
Operational efficiency and minimizing equipment fault time seem particularly significant, are on the other hand also to ensure that what Mineral Processing Enterprises production safety was carried out
Important means.
The application present situation of Mineral Processing Industry is to manufacture the Equipment Foundations information that in execution level, equipment control laid particular emphasis at present
Management, realize Equipment Foundations information, equipment maintenance record, equipment point-detecting tracking, Plant maintenance plan, equipment operation management etc.
Function, is mainly accomplished that record and statistical function to the operation conditions of equipment it is impossible to use this equipment in time according to equipment
Current operating conditions make control action, do not realize monitoring to equipment running status.Equipment running monitoring is mainly in ore dressing
Process Control System (PCS) layer is realized, and lays particular emphasis on the on-line monitoring to plant machinery fault and key parameter variation tendency, sends
Alarm, the real time data that status of equipment is monitored is less to carry out comprehensive analysis processing it is impossible to realize equipment operating condition is examined
Disconnected.Visible in sum, the monitoring shortage currently for preparation equipment running status diagnoses to the abnormal industrial and mineral of equipment, lacks
The weary data to sign equipment operation condition is analyzed, calculates and processes, equipment operation condition in being thus difficult to produce
Feed back to plan layer in time, lead to be difficult to carry out quick response and adjustment to mineral processing production production plan, thus impact is whole selecting
Ore deposit enterprises production efficiency.
Content of the invention
For the deficiencies in the prior art, the present invention proposes a kind of preparation equipment running state monitoring system and method.
The technical scheme is that：
A kind of preparation equipment running state monitoring system, including home server, multiple data sampling sensor and video
Acquisition module；
The plurality of data sampling sensor input is all connected with each equipment of mining plants monitoring, and the plurality of data is adopted
The output end of collection sensor connects described home server, and the output end of described video acquisition module connects described local service
Device；
Described data sampling sensor, the running state data for the equipment of Real-time Collection monitoring and equipment index number
According to, and send to home server；The equipment of described monitoring includes：Ball mill, shaft furnace, filter, intensity magnetic separator, High-gradient Magnetic
Select machine, high frequency fine screen and plunger displacement pump；The running state data of described monitoring device includes：The run time of each equipment, rest
Time, calendar working time, reality processing cycle and certified products quantity, described idle hours includes：External factor downtime,
Adjustment initialization time and fault idle hours；
Described video acquisition module, for the work video of each equipment of Real-time Collection, and sends to home server；
Described home server has built equipment account unit, equipment running status monitoring unit, equipment operating statistic list
Unit, equipment operating analysis unit, equipment operation condition comparative analysis unit, equipment video monitor unit；
Described equipment account unit, for recording and storing each Equipment Foundations information of mining plants monitoring；
Described equipment running status monitoring unit, running state data and equipment for each equipment of display monitoring refer to
Mark data；By setting upper limit threshold and the lower threshold of each equipment index data, when the equipment index of each equipment of monitoring
When data exceeds its corresponding upper limit threshold or lower threshold, carry out early warning, and show whole known fault of source of early warning
Reason；
Described equipment operating statistic unit, the running state data for each equipment according to monitoring counts each equipment
Run time and each equipment idle hours, count each equipment operation maintenance record, each equipment maintenance record and therefore
Barrier maintenance knowledge base, and show；
Described equipment operating analysis unit, for set the Scheduled Down Time of each equipment, the theoretical process-cycle and plus
The fault idle hours of number amount, the run time according to each equipment and each equipment calculates the equipment fault of corresponding device
Rate；According to calendar working time of equipment, Scheduled Down Time, external factor downtime, adjustment initialization time, processing number
Amount, reality processing cycle, theoretical process-cycle, certified products quantity, the OEE assay value of fault idle hours calculating corresponding device；
The historical failure data of the running state data according to each equipment and history normal data set up statistics KPCA of corresponding device
Model, and the statistics KPCA model of equipment is verified, obtains the diagnostic model of equipment, each to the monitoring of Real-time Collection
The running state data of equipment carries out inline diagnosis using the diagnostic model of its corresponding device, judges the operation shape of this monitoring device
State；
Described equipment operation maintenance unit, for recording equipment operation maintenance record, the equipment maintenance record of user's offer
With trouble hunting knowledge base；
Described equipment operation condition comparative analysis unit, for showing the run time of individual equipment within a certain period of time
Comparison diagram, the run time comparison diagram within a certain period of time of the equipment of whole equipment type, the idle hours of individual equipment exist
Comparison diagram in certain time, the idle hours comparison diagram within a certain period of time of the equipment of whole equipment type；
Described video monitor unit, the real-time working video for the equipment operation of display monitoring and history work video.
When described calendar working time according to equipment, Scheduled Down Time, external factor downtime, adjustment initialization
Between, processing quantity, reality processing cycle, theoretical process-cycle, certified products quantity, fault idle hours calculate corresponding device
The formula of OEE assay value is：
The OEE assay value of the equipment=time rate of starting × Performance Rate × accepted product percentage × 100%；
Wherein, the time rate of starting=running time/duration of load application；
Duration of load application=calendar working time-Scheduled Down Time-external factor downtime；
Running time=duration of load application-fault idle hours-adjustment initialization time；
Performance Rate=only start rate × speed starts rate；
Only the rate of starting=processing quantity × reality processing cycle/running time；
Only the rate of starting=processing quantity × reality processing cycle/running time；
The speed rate of starting=theory process-cycle/reality processing cycle；
Accepted product percentage=certified products quantity/processing quantity.
The method carrying out preparation equipment running state monitoring using preparation equipment running state monitoring system, walks including following
Suddenly：
The running state data of the equipment monitored by data sampling sensor Real-time Collection and equipment index data, concurrently
Deliver to home server；
By the work video of video acquisition module each equipment of Real-time Collection, and send to home server；
By home server record and store mining plants monitoring each Equipment Foundations information；
By running state data and the equipment index data of each equipment of home server display monitoring；
Set upper limit threshold and the lower threshold of each equipment index data by home server, when each equipment of monitoring
Equipment index data exceed its corresponding upper limit threshold or during lower threshold, carry out early warning, and show the complete of source of early warning
Portion's known fault reason；
The equipment operation maintenance record, equipment maintenance record and the trouble hunting that are there is provided by home server record user are known
Know storehouse；
Count the run time of each equipment by the running state data of each equipment according to monitoring for the home server
With the idle hours of each equipment, count each equipment operation maintenance record, each equipment maintenance record and trouble hunting knowledge
Storehouse, and show；
Set Scheduled Down Time, theoretical process-cycle and the processing quantity of each equipment by home server；
Corresponding with the calculating of the fault idle hours of each equipment according to the run time of each equipment by home server
The equipment failure rate of equipment；
By home server according to calendar working time of equipment, Scheduled Down Time, external factor downtime, tune
Whole initialization time, processing quantity, reality processing cycle, theoretical process-cycle, certified products quantity, the calculating of fault idle hours are right
Answer the OEE assay value of equipment；
By home server according to the historical failure data of the running state data of each equipment and history normal data
Set up the statistics KPCA model of corresponding device, and the statistics KPCA model of equipment verified, obtain the diagnostic model of equipment,
Using the diagnostic model of its corresponding device, inline diagnosis is carried out to the running state data of each equipment of the monitoring of Real-time Collection,
Judge the running status of this monitoring device；
Show run time comparison diagram within a certain period of time, the whole equipment type of individual equipment by home server
The run time comparison diagram within a certain period of time of equipment, the idle hours comparison diagram within a certain period of time of individual equipment,
The idle hours of the equipment of whole equipment type comparison diagram within a certain period of time；
The real-time working video being run by the equipment of home server display monitoring and history work video.
Described normal according to the historical failure data of the running state data of each equipment and history by home server
Data sets up the statistics KPCA model of corresponding device, and the statistics KPCA model of equipment is verified, obtains the diagnosis of equipment
Model, is carried out using the diagnostic model of its corresponding device to the running state data of each equipment of the monitoring of Real-time Collection online
Diagnosis, judges that the detailed process of the running status of this monitoring device comprises the following steps：
S1：Obtain the historical data of the running state data of a certain equipment of collection；
S2：The N group historical data of running state data is pre-processed, by the running status of equipment same performance index
The historical data of data is unified；
S3：Choose M group history normal data in pretreated running state data and, as training set data, choose core letter
Number, carries out nuclear mapping to training set data, obtains nuclear matrix, and carry out centralization process to this nuclear matrix, set up this equipment
Statistics KPCA model；
S4：Statistics KPCA model according to equipment calculates the T of training set data2Statistic controls limit and SPE statistic control
System limit；
S5：Using the K group history normal data in the historical data of running state data and historical failure data as test
Collection data, the statistics KPCA model according to equipment calculates the T of test set data2Statistic and SPE statistic；
S6：The statistics KPCA model of equipment is verified：If the T of history normal data2Statistic and SPE statistic surpass
Go out its corresponding quantity controlling limit in the range of the model accuracy rate setting, and the T of historical failure data2Statistic and
SPE statistic in the range of the model accuracy rate setting, then executes S7, otherwise holds in its corresponding quantity controlling in the range of limit
Row S8；
S7：Using the statistics KPCA model of current device as diagnostic model, execute S9；
S8：Reacquire the historical data of the running state data of this equipment of collection or change kernel function, return step
Rapid S2；
S9：The running state data of each equipment of the monitoring of Real-time Collection is entered using the diagnostic model of its corresponding device
Row inline diagnosis：If the T that the running state data of Real-time Collection calculates according to the diagnostic model of equipment2Statistic and SPE statistics
Amount all controls in limit at it, then this equipment is normal condition, and otherwise, this equipment is malfunction.
Beneficial effects of the present invention：
The present invention proposes a kind of preparation equipment running state monitoring system and method, and the present invention can realize mineral processing production system
Make the monitor in real time to equipment running status for the execution level, data is carried out with comprehensive analysis processing, realize to equipment operating condition
Diagnosis；Carry out equipment failure rate analysis, the analysis loss maximum to the contribution amount of equipment downtime loss, to the behaviour of field control room
Make personnel with reference, promote it to take appropriate measures, improve.
Brief description
Fig. 1 is preparation equipment running state monitoring system architecture diagram in the specific embodiment of the invention；
Fig. 2 is the flow chart of the method for preparation equipment running state monitoring in the specific embodiment of the invention；
Fig. 3 is by setting up the monitoring to Real-time Collection for the statistics KPCA model of equipment in the specific embodiment of the invention
The flow chart that the running state data of each equipment carries out inline diagnosis.
Specific embodiment
Below in conjunction with the accompanying drawings the specific embodiment of the invention is described in detail.
A kind of preparation equipment running state monitoring system, as shown in figure 1, include home server, multiple data acquisition passes
Sensor and video acquisition module.
The plurality of data sampling sensor input is all connected with each equipment of mining plants monitoring, and the plurality of data is adopted
The output end of collection sensor connects described home server, and the output end of described video acquisition module connects described local service
Device.
In present embodiment, data sampling sensor from section win micro- WirelessHart module M1100 and
WirelessHart intelligent gateway G1100.
Video acquisition module selects Haikang prestige to regard web camera (model：DS-2CD2725F-1).
Described data sampling sensor, the running state data for the equipment of Real-time Collection monitoring and equipment index number
According to, and send to home server；The running state data of described monitoring device includes：The run time of each equipment, rest
Time, calendar working time, reality processing cycle and certified products quantity, described idle hours includes：External factor downtime,
Adjustment initialization time and fault idle hours.
In present embodiment, the equipment of monitoring includes：Ball mill, shaft furnace, filter, intensity magnetic separator, high gradient magnetic separator,
High frequency fine screen and plunger displacement pump.
Described video acquisition module, for the work video of each equipment of Real-time Collection, and sends to home server.
Described home server has built equipment account unit, equipment running status monitoring unit, equipment operating statistic list
Unit, equipment operating analysis unit, equipment operation maintenance unit, equipment operation condition comparative analysis unit, equipment video monitoring list
Unit.
Described equipment account unit, for recording and storing each Equipment Foundations information of mining plants monitoring.
In present embodiment, record and store each Equipment Foundations information of mining plants monitoring, each sets to realize ore dressing plant
The management of standby Back ground Information, Equipment Foundations information includes locations of structures in the ore dressing plant of equipment place, equipment files, equipment class
Type and equipment state.
Wherein, the locations of structures in the ore dressing plant of equipment place：For equipment according to factory's hierarchical structure, position system, to setting
The standby position carrying out subregion according to the difference of operation and operation area；
Equipment files：For the essential informations such as the specifications and models of equipment, service life, the date of production and auxiliary device, energy
Enough to the device name of some equipment concrete, device coding, equipment another name, the specifications and models of equipment, performance parameter, factory
The record of these Back ground Informations such as business, modification and query function, are corresponded with live physical device；
Device type：The different process sorted out classification, be easy to device statistics analysis for equipment；
Equipment state：Whether marking arrangement normally enables, and can be set to dead status to a certain equipment.
Described equipment running status monitoring unit, running state data and equipment for each equipment of display monitoring refer to
Mark data.By setting upper limit threshold and the lower threshold of each equipment index data, when the equipment index of each equipment of monitoring
When data exceeds its corresponding upper limit threshold or lower threshold, carry out early warning, and show whole known fault of source of early warning
Reason.
Described equipment operating statistic unit, the running state data for each equipment according to monitoring counts each equipment
Run time and each equipment idle hours, count each equipment operation maintenance record, each equipment maintenance record and therefore
Barrier maintenance knowledge base, and show.
In present embodiment, equipment operating statistic unit counts the run time of each equipment, when resting of each equipment
Between, equipment operation maintenance record, equipment maintenance record and trouble hunting knowledge base, and achieve check each in random time section
The log of individual equipment, equipment log includes resting of equipment and records and log；The idle hours of each equipment,
Rest cause type, reason of resting description and confirm mark；Can be according to the initial time of input, termination time and implementor name
Claim, carry out checking the log with each equipment any in random time section；Realize the equipment log to bottom collection
Processed, it is possible to achieve to the interpolation of log, modification, the operation deleted and confirm.
For the equipment log gathering first, need equipment is rested record rests cause type and reason of resting
Description carries out perfect, if the equipment start-stop time of equipment log, start and stop mark, reason of resting all determines correctly, to this
Bar equipment log carries out confirmation flag.If one record having been acknowledged of modification, during this recording equipment start and stop
Between after the confirmation flag of all logs of this equipment be automatically designated " not confirm ", need to reaffirm.
The run time statistics of each equipment：Each main equipment is based on confirmed equipment log with sky as list
Position carries out equipment operating statistic, and during equipment fortune, statistics once can only count during the fortune of one day it is possible to calculate the equipment of this day
Operating rate, supports that User Defined class sets up and puts, can arrange class's system according to the concrete ore dressing plant condition of production.
The idle hours statistics of each equipment：Realize carrying out idle hours to ore dressing plant main equipment according to resting reason
Statistics；Equipment rests year statistics and refers to uniting by resting the difference of reason of every month in each device type one year
Meter, is to be counted in the result that equipment rests moon statistics.
Described equipment operating analysis unit, for set the Scheduled Down Time of each equipment, the theoretical process-cycle and plus
The fault idle hours of number amount, the run time according to each equipment and each equipment calculates the equipment fault of corresponding device
Rate；According to calendar working time of equipment, Scheduled Down Time, external factor downtime, adjustment initialization time, processing number
Amount, reality processing cycle, theoretical process-cycle, certified products quantity, the OEE (Overall of fault idle hours calculating corresponding device
Equipment Effectiveness, overall equipment efficiency.comprehensive efficiency of equipment) assay value；The history of the running state data according to each equipment
Fault data and history normal data set up the statistics KPCA model of corresponding device, and the statistics KPCA model of equipment is tested
Card, obtains the diagnostic model of equipment, adopts its corresponding device to the running state data of each equipment of the monitoring of Real-time Collection
Diagnostic model carry out inline diagnosis, judge the running status of this monitoring device.
In present embodiment, the run time according to each equipment calculates corresponding setting with the fault idle hours of each equipment
Standby equipment failure rate.Equipment failure rate：Refer to according to the fault idle hours of equipment and the run time of equipment, computing device
Fault rate.Equipment failure rate=fault idle hours/(run time+fault idle hours).
In present embodiment, when described calendar working time according to equipment, Scheduled Down Time, external factor are shut down
Between, adjustment initialization time, processing quantity, the reality processing cycle, the theoretical process-cycle, certified products quantity, fault idle hours
The formula of OEE assay value calculating corresponding device is：
The OEE assay value of the equipment=time rate of starting × Performance Rate × accepted product percentage × 100%.
Wherein, the time rate of starting=running time/duration of load application.
Duration of load application=calendar working time-Scheduled Down Time-external factor downtime.
Running time=duration of load application-fault idle hours-adjustment initialization time.
Performance Rate=only start rate × speed starts rate.Performance Rate reflects the time used by reality processing product
With the ratio of running time, its height reflect in production equipment idle running it is impossible to statistics little shutdown loss.
Only the rate of starting=processing quantity × reality processing cycle/running time.
The speed rate of starting=theory process-cycle/reality processing cycle.
Accepted product percentage=certified products quantity/processing quantity.
Described equipment operation maintenance unit, for recording equipment operation maintenance record, the equipment maintenance record of user's offer
With trouble hunting knowledge base；
In present embodiment, equipment operation maintenance record：Realize the maintenance of equipment is recorded, and the maintenance equipment
Record, arranges the trouble hunting knowledge base of forming apparatus by analyzing and processing；
Equipment maintenance record：Refer to the logging to equipment fault for the attendant, essential record equipment fault type, occur
The implementor name of fault, maintenance date, the description of fault, release the processing mode of fault；
Trouble hunting knowledge base：Refer to attendant by the service experience to field device failure, typical fault is occurring
Or during difficult fault, record the form of expression, Trouble cause and the processing mode of typical fault or difficult fault,
Generate trouble hunting knowledge base, so that processing equipment fault will make reference from now on.
In present embodiment, equipment operation maintenance record, equipment maintenance record and trouble hunting that record user provides are known
Know storehouse, advantageously reduce the equipment fault time.Due in the equipment management system of full level of factory, not carrying out the long-pending of service experience
Tired, maintenance is still to rely primarily in Personal Skills and experience, lacks standardization and specialized service experience supports, efficiency is not
High.This phenomenon in steel industry generally existing, in order to improve the equipment operating efficiency in ore dressing plant and reduce the equipment fault time,
Therefore need to realize equipment operating maintenance record in preparation equipment operation conditions monitor supervision platform, and according to fault type and rest
Time is mark, forms maintenance of equipment experience storehouse, and the maintenance for ore dressing plant provides specialized service experience to support.
Described equipment operation condition comparative analysis unit, for showing the run time of individual equipment within a certain period of time
Comparison diagram, the run time comparison diagram within a certain period of time of the equipment of whole equipment type, the idle hours of individual equipment exist
Comparison diagram in certain time, the idle hours comparison diagram within a certain period of time of the equipment of whole equipment type.
In present embodiment, the comparison diagram in two months for the run time of display individual equipment, whole equipment type
The run time of equipment is in the comparison diagram in two months, the comparison diagram in two months for the idle hours of individual equipment, entirely set
Comparison diagram in two months for the idle hours of the equipment of standby type.
Described video monitor unit, the real-time working video for the equipment operation of display monitoring and history work video.
In present embodiment, the real-time working video of equipment operation and history work video that display is monitored, make work people
Member can understand the work ruuning situation of field apparatus at any time, realizes equipment drawing as the purpose of mobile monitor, bonding apparatus run
State parameter monitors, and provides basis based on the intelligent diagnostics of image and operational factor with monitoring for follow-up.
The method that preparation equipment running state monitoring is carried out using preparation equipment running state monitoring system, as shown in Fig. 2
Comprise the following steps：
101st, the running state data of the equipment monitored by data sampling sensor Real-time Collection and equipment index data,
And send to home server.
102nd, pass through the work video of video acquisition module each equipment of Real-time Collection, and send to home server.
103rd, pass through home server record and store each Equipment Foundations information of mining plants monitoring.
104th, by running state data and the equipment index data of each equipment of home server display monitoring.
105th, set upper limit threshold and the lower threshold of each equipment index data by home server, when monitoring each
When the equipment index data of equipment exceeds its corresponding upper limit threshold or lower threshold, carry out early warning, and show source of early warning
Whole known fault reasons.
106th, equipment operation maintenance record, equipment maintenance record and fault inspection that home server record user provides are passed through
Repair knowledge base；
107th, by home server, the running state data of each equipment according to monitoring counts the operation of each equipment
Time and the idle hours of each equipment, count each equipment operation maintenance record, each equipment maintenance record and trouble hunting
Knowledge base, and show.
108th, Scheduled Down Time, theoretical process-cycle and the processing quantity of each equipment is set by home server.
109th, pass through home server to be calculated according to the run time of each equipment and the fault idle hours of each equipment
The equipment failure rate of corresponding device.
When the 110th, passing through home server and being shut down according to calendar working time of equipment, Scheduled Down Time, external factor
Between, adjustment initialization time, processing quantity, the reality processing cycle, the theoretical process-cycle, certified products quantity, fault idle hours
Calculate the OEE assay value of corresponding device.
111st, pass through home server normal according to the historical failure data of the running state data of each equipment and history
Data sets up the statistics KPCA model of corresponding device, and the statistics KPCA model of equipment is verified, obtains the diagnosis of equipment
Model, is carried out using the diagnostic model of its corresponding device to the running state data of each equipment of the monitoring of Real-time Collection online
Diagnosis, judges the running status of this monitoring device.
112nd, run time comparison diagram within a certain period of time, the whole equipment of individual equipment is shown by home server
The run time of the equipment of type comparison diagram within a certain period of time, the idle hours contrast within a certain period of time of individual equipment
Figure, the idle hours comparison diagram within a certain period of time of the equipment of whole equipment type.
113rd, the real-time working video being run by the equipment of home server display monitoring and history work video.
In present embodiment, by home server according to the historical failure data of the running state data of each equipment and
History normal data sets up the statistics KPCA model of corresponding device, and the statistics KPCA model of equipment is verified, is set
Standby diagnostic model, adopts the diagnostic model of its corresponding device to the running state data of each equipment of the monitoring of Real-time Collection
Carry out inline diagnosis, judge the detailed process of the running status of this monitoring device, as shown in figure 3, comprising the following steps：
S1：Obtain the historical data of the running state data of a certain equipment of collection.
In present embodiment, the equipment of monitoring includes：Ball mill, shaft furnace, filter, intensity magnetic separator, high gradient magnetic separator,
High frequency fine screen and plunger displacement pump.Part running state data from N=30 group ball mill 3-1 is as shown in table 1 as historical data.
The part running state data of table 1 ball mill 3-1 is as historical data
S2：The N group historical data of running state data is pre-processed, by the running status of equipment same performance index
The historical data of data is unified.
In present embodiment, the method that N group historical data is pre-processed is：Each variable is deducted and removes after its average
With its standard deviation, to eliminate the dimension impact between historical data variable.
S3：Choose M group history normal data in pretreated running state data and, as training set data, choose core letter
Number, carries out nuclear mapping to training set data, obtains nuclear matrix, and carry out centralization process to this nuclear matrix, set up this equipment
Statistics KPCA (Kernel Principal Component Analysis, core principle component analysis) model.
In present embodiment, in the later running state data of selection process, M=20 group history normal data is as training
Collection data is as shown in table 2.
Table 2 training set data
In present embodiment, training set data xi(i=1,2.....M), function is Gaussian radial basis function (RBF) as formula
(1) shown in：
Wherein, σ is the standard deviation of training set data.
Nuclear mapping is carried out to training set data, obtains nuclear matrixAs shown in formula (2)：
Wherein, 1im=1nj=1, m=1,2......M, J=1,2......M, n=1,2......M, kij=Φ
(xi)·Φ(xj), Φ (xi) it is row feature space sample point, Φ (xj) it is row feature space sample point.
S4：Statistics KPCA model according to equipment calculates the T of training set data2Statistic controls limit and SPE statistic control
System limit.
In present embodiment, the statistics KPCA model of the equipment obtaining calculates the T of training set data2Statistic control is limited to
31.847, SPE statistics controls are limited to 6.951*10-4.
S5：Using the K group history normal data in the historical data of running state data and historical failure data as test
Collection data, the statistics KPCA model according to equipment calculates the T of test set data2Statistic and SPE statistic.
In present embodiment, the K=8 group test set data of selection is as shown in table 3.
Table 3 K=8 group test set data
S6：The statistics KPCA model of equipment is verified：If the T of history normal data2Statistic and SPE statistic surpass
Go out its corresponding quantity controlling limit in the range of the model accuracy rate setting, and the T of historical failure data2Statistic and
SPE statistic in the range of the model accuracy rate setting, then executes S7, otherwise holds in its corresponding quantity controlling in the range of limit
Row S8.
In present embodiment, model accuracy rate is 95%.
S7：Using the statistics KPCA model of current device as diagnostic model, execute S9.
S8：Reacquire the historical data of the running state data of this equipment of collection or change kernel function, return step
Rapid S2.
S9：The running state data of each equipment of the monitoring of Real-time Collection is entered using the diagnostic model of its corresponding device
Row inline diagnosis：If the T that the running state data of Real-time Collection calculates according to the diagnostic model of equipment2Statistic and SPE statistics
Amount all controls in limit at it, then this equipment is normal condition, and otherwise, this equipment is malfunction.
Present embodiment, the diagnostic result of the part running state data of group ball mill 3-1 of diagnosis is as shown in table 4.
The diagnostic result of the part running state data of group ball mill 3-1 of table 4 diagnosis
Claims (4)
1. a kind of preparation equipment running state monitoring system is it is characterised in that include home server, multiple data acquisition sensing
Device and video acquisition module；
The plurality of data sampling sensor input connects each equipment of mining plants monitoring, the plurality of data acquisition sensing
The output end of device is all connected with described home server, and the output end of described video acquisition module connects described home server；
Described data sampling sensor, the running state data for each equipment of Real-time Collection monitoring and equipment index number
According to, and send to home server；The equipment of described monitoring includes：Ball mill, shaft furnace, filter, intensity magnetic separator, High-gradient Magnetic
Select machine, high frequency fine screen and plunger displacement pump；The running state data of each equipment of described monitoring includes：Run time, idle hours,
Calendar working time, reality processing cycle and certified products quantity, described idle hours includes：External factor downtime, adjustment
Initialization time and fault idle hours；
Described video acquisition module, for the work video of each equipment of Real-time Collection, and sends to home server；
Described home server has been built equipment account unit, equipment running status monitoring unit, equipment operating statistic unit, has been set
Received shipment row analytic unit, equipment operation condition comparative analysis unit, equipment video monitor unit；
Described equipment account unit, for recording and storing each Equipment Foundations information of mining plants monitoring；
Described equipment running status monitoring unit, the running state data for each equipment of display monitoring and equipment index number
According to；By setting upper limit threshold and the lower threshold of each equipment index data, when the equipment index data of each equipment of monitoring
During beyond its corresponding upper limit threshold or lower threshold, carry out early warning, and show whole known fault reasons of source of early warning；
Described equipment operating statistic unit, for counting the fortune of each equipment according to the running state data of each equipment monitored
Row time and the idle hours of each equipment, count each equipment operation maintenance record, each equipment maintenance record and fault inspection
Repair knowledge base, and show；
Described equipment operating analysis unit, for setting Scheduled Down Time, theoretical process-cycle and the processing number of each equipment
The fault idle hours of amount, the run time according to each equipment and each equipment calculates the equipment failure rate of corresponding device；Root
According to the calendar working time of each equipment, Scheduled Down Time, external factor downtime, adjustment initialization time, processing number
Amount, reality processing cycle, theoretical process-cycle, certified products quantity, the OEE assay value of fault idle hours calculating corresponding device；
The historical failure data of the running state data according to each equipment and history normal data set up statistics KPCA of corresponding device
Model, and the statistics KPCA model of equipment is verified, obtains the diagnostic model of equipment, each to the monitoring of Real-time Collection
The running state data of equipment carries out inline diagnosis using the diagnostic model of its corresponding device, judges the operation shape of this monitoring device
State；
Described equipment operation maintenance unit, for recording equipment operation maintenance record, equipment maintenance record and the event of user's offer
Barrier maintenance knowledge base；
Described equipment operation condition comparative analysis unit, for showing the contrast within a certain period of time of the run time of individual equipment
Figure, the run time comparison diagram within a certain period of time of the equipment of whole equipment type, the idle hours of individual equipment are certain
Comparison diagram in time, the idle hours comparison diagram within a certain period of time of the equipment of whole equipment type；
Described video monitor unit, the real-time working video for the equipment operation of display monitoring and history work video.
2. preparation equipment running state monitoring system according to claim 1 is it is characterised in that the described day according to equipment
Go through working time, Scheduled Down Time, external factor downtime, adjustment initialization time, processing quantity, reality processing week
Phase, theoretical process-cycle, certified products quantity, the formula of the OEE assay value of fault idle hours calculating corresponding device are：
The OEE assay value of the equipment=time rate of starting × Performance Rate × accepted product percentage × 100%；
Wherein, the time rate of starting=running time/duration of load application；
Duration of load application=calendar working time-Scheduled Down Time-external factor downtime；
Running time=duration of load application-fault idle hours-adjustment initialization time；
Performance Rate=only start rate × speed starts rate；
Only the rate of starting=processing quantity × reality processing cycle/running time；
Only the rate of starting=processing quantity × reality processing cycle/running time；
The speed rate of starting=theory process-cycle/reality processing cycle；
Accepted product percentage=certified products quantity/processing quantity.
3. carry out the side of preparation equipment running state monitoring using the preparation equipment running state monitoring system described in claim 1
Method is it is characterised in that comprise the following steps：
The running state data of each equipment monitored by data sampling sensor Real-time Collection and equipment index data, concurrently
Deliver to home server；
By the work video of video acquisition module each equipment of Real-time Collection, and send to home server；
Set Scheduled Down Time, theoretical process-cycle and the processing quantity of each equipment by home server；
Equipment operation maintenance record, equipment maintenance record and the trouble hunting knowledge being provided by home server record user
Storehouse；
By home server record and store mining plants monitoring each Equipment Foundations information；
By running state data and the equipment index data of each equipment of home server display monitoring；
Set upper limit threshold and the lower threshold of each equipment index data by home server, when setting of each equipment monitored
When standby achievement data exceeds its corresponding upper limit threshold or lower threshold, carry out early warning, and show source of early warning whole
Know failure cause；
The run time of each equipment and each is counted by the running state data of each equipment according to monitoring for the home server
The idle hours of individual equipment, count each equipment operation maintenance record, each equipment maintenance record and trouble hunting knowledge base, and
Display；
Corresponding device is calculated according to the run time of each equipment and the fault idle hours of each equipment by home server
Equipment failure rate；
By home server according to calendar working time of each equipment, Scheduled Down Time, external factor downtime, tune
Whole initialization time, processing quantity, reality processing cycle, theoretical process-cycle, certified products quantity, the calculating of fault idle hours are right
Answer the OEE assay value of equipment；
Set up according to the historical failure data of the running state data of each equipment and history normal data by home server
The statistics KPCA model of corresponding device, and the statistics KPCA model of equipment is verified, obtain the diagnostic model of equipment, to reality
When the running state data of each equipment of monitoring that gathers inline diagnosis is carried out using the diagnostic model of its corresponding device, judge
The running status of this monitoring device；
The run time comparison diagram within a certain period of time of individual equipment, the setting of whole equipment type are shown by home server
Standby run time comparison diagram within a certain period of time, the idle hours comparison diagram within a certain period of time of individual equipment, whole
The idle hours of the equipment of device type comparison diagram within a certain period of time；
The real-time working video being run by the equipment of home server display monitoring and history work video.
4. the method for preparation equipment running state monitoring according to claim 3 is it is characterised in that described taken by local
The statistics of corresponding device set up by business device according to the historical failure data of the running state data of each equipment and history normal data
KPCA model, and the statistics KPCA model of equipment is verified, obtains the diagnostic model of equipment, to the monitoring of Real-time Collection
The running state data of each equipment carries out inline diagnosis using the diagnostic model of its corresponding device, judges the fortune of this monitoring device
The detailed process of row state comprises the following steps：
S1：Obtain the historical data of the running state data of a certain equipment of collection；
S2：The N group historical data of running state data is pre-processed, by the running state data of equipment same performance index
Historical data unified；
S3：Choose M group history normal data in pretreated running state data and, as training set data, choose kernel function,
Nuclear mapping is carried out to training set data, obtains nuclear matrix, and centralization process is carried out to this nuclear matrix, set up the statistics of this equipment
KPCA model；
S4：Statistics KPCA model according to equipment calculates the T of training set data2Statistic controls limit and SPE statistic to control limit；
S5：Using the K group history normal data in the historical data of running state data and historical failure data as test set number
According to the statistics KPCA model according to equipment calculates the T of test set data2Statistic and SPE statistic；
S6：The statistics KPCA model of equipment is verified：If the T of history normal data2Statistic and SPE statistic exceed it
The corresponding quantity controlling limit is in the range of the model accuracy rate setting, and the T of historical failure data2Statistic and SPE
Statistic in the range of the model accuracy rate setting, then executes S7, otherwise executes in its corresponding quantity controlling in the range of limit
S8；
S7：Using the statistics KPCA model of current device as diagnostic model, execute S9；
S8：Reacquire the historical data of the running state data of this equipment of collection or change kernel function, return to step S2；
S9：The running state data of this equipment of the monitoring of Real-time Collection is carried out using the diagnostic model of its corresponding device online
Diagnosis：If the T that the running state data of Real-time Collection calculates according to the diagnostic model of equipment2Statistic and SPE statistic all exist
It controls in limit, then this equipment is normal condition, and otherwise, this equipment is malfunction.
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