CN107146004B - A kind of slag milling system health status identifying system and method based on data mining - Google Patents
A kind of slag milling system health status identifying system and method based on data mining Download PDFInfo
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Abstract
The invention discloses a kind of slag milling system health status identifying system and method based on data mining, mining analysis is carried out to floor data using a kind of Feature Selection method of synthesis, obtain influenceing the stable key parameter of Vertical Mill, the index as Vertical Mill health state evaluation；The index of Vertical Mill health state evaluation based on determination, cluster result analysis is carried out to work condition state, the characteristics of obtained each operating mode cluster, obtain the state distribution situation in history operating mode, define the running status classification in history operating mode；Then the characteristic value training pattern determined in Vertical Mill health status feature acquisition module is predicted to the variation tendency of parameter, identified with predicted value secondary status using ARIMA algorithms.The present invention has higher accuracy of identification and generalization ability, and performance is good, identifies and diagnoses suitable for the health status of slag milling system.
Description
Technical field
It is more particularly to a kind of based on data mining the present invention relates to the identification of the health status of slag milling system and diagnosis
Slag milling system health status identifying system and method.
Background technology
Vertical Mill is a kind of equipment for the materials such as the slag of bulky grain to be ground to fine particle, mainly to building materials, change
Waste residue caused by the industries such as work, steel carries out grinding, realizes the recycling of waste residue, ground obtained micro mist is usually as cement
The raw material of production.But slag milling system process is complicated, working environment is severe, and long-term heavy-duty service, system often goes out
Existing various failures, control system chain reaction can cause whole production line to shut down, and then it is poorly efficient to cause production line to pause
Situation.Therefore, there is an urgent need to which slag milling system health status is identified and assessed, the healthy shape of vertical mill system is predicted
State.
Health status Predicting Technique is to carry out overall merit in the health status to device systems, and health is characterized obtaining
On the basis of state performance parameter, the time series of analytical performance parameter, its variation tendency is extended out to obtain following a period of time
The technology of the health status changing rule of interior equipment.Recent domestic scholar to the health status Predicting Technique of complex equipment,
This problem is studied from the angle of random theory and fuzzy theory, main method has fusion forecasting method, arma modeling prediction
Method, Hidden Markov predicted method, fuzzy neural network predicted method, Kalman filter prediction method.Fusion forecasting method is based on same
Blend curve method as prediction curve of the time series data of class equipment under different weights, method is simple, directly perceived, no
Dependent on system physical model, but need more sample data.Integration technology is applied to the prediction of Gas Outburst by ox Xiao Ling etc.
Problem, obtained preferable prediction result.Arma modeling Predicting Technique is in autoregression model (AR models) and moving average model
A kind of Time Series Forecasting Methods set up on the basis of (MA models), method do not need system model when in use, but
It is only applicable to short-term forecast.Pham etc. predicts the degenerate state of change system with linear arma modeling and Nonlinear GARCH.
Hidden Markov predicted method is to ask hiding according to maximum likelihood theory according to the time series of observable performance parameter value
The markoff process of health status sequence, method are from the life-cycle data extracting parameter of sample equipment, with available for long-term
Prediction, but substantial amounts of sample data is not easy to obtain.Peng Ying is by analyzing the Monitoring Data of hydraulic pump, with considering the hidden of aging factor
Semi-Markov Process (HSMM) Forecasting Methodology can describe pump performance degenerative process well.Fuzzy neural network predicted method
It is the forecast model that neural network weight is constantly trained based on fuzzy reasoning, method handles nonlinear problem using fuzzy theory,
Strong adaptability, but expertise is needed, transplantability is poor.With the automation of micro mist industry and the raising of the level of informatization, DCS
Control system has obtained commonly used in the factory, and a large amount of creation datas are have accumulated in database.
The content of the invention
In order to preferably realize the identification of Vertical Mill health status and diagnosis, the present invention provides a kind of slag based on data mining
Grinding system health status identifying system and method.Concrete technical scheme is as follows：
A kind of slag milling system health status identifying system based on data mining, including：Data preprocessing module, stand
Grind health state evaluation index and excavate module, Vertical Mill health status Cluster Analysis module, the acquisition of Vertical Mill state estimation index feature
Module, Vertical Mill real-time characteristic parameter prediction module, wherein：
Data preprocessing module, outlier processing, processing empty value, sliding-model control are carried out to the data of Vertical Mill collection and returned
One change is handled, and is got ready for the mining analysis of data；
Vertical Mill health state evaluation index excavates module, and floor data is carried out using a kind of Feature Selection method of synthesis
Mining analysis, obtain influenceing the stable key parameter of Vertical Mill, the index as Vertical Mill health state evaluation；
Vertical Mill health status Cluster Analysis module, the index of the Vertical Mill health state evaluation based on determination, to work condition state
Cluster result analysis is carried out, obtains stable mode operating mode storehouse；
Vertical Mill state estimation index feature acquisition module, analyze Vertical Mill running status under collection real time data spy
Point, it is determined that carrying out the characteristic value of real-time status judgement；
Vertical Mill real-time characteristic parameter prediction module, using ARIMA algorithms to true in Vertical Mill health status feature acquisition module
Fixed characteristic value carries out model training, the variation tendency of Prediction Parameters, is identified with predicted value secondary status.
Further, in described data preprocessing module, data outliers processing, processing empty value, data screening is passed through
Realized with data cleansing.Sliding-model control and normalized, realized by feature is brief with data conversion.
Further, described Vertical Mill health state evaluation index is excavated in module, a kind of Feature Selection method of synthesis
This five kinds of method synthesis are eliminated by random lasso, ridge regression, random forest, stable Sexual behavior mode and recursive feature to form.Screening is calculated
Method is by solving the relation between input variable and output variable, and the importance of each feature is given using five kinds of methods respectively
With marking, five kinds of scoring events are handled, the importance of feature assessed according to the scores after processing, it is determined that
Key feature in feature set to be selected.
Further, comprising the following steps that for Vertical Mill operation key feature screening is carried out：
1) using vibration as output y, it is characterized as inputting x with other, feature set to be selected is carried out using five kinds of methods respectively
Screening, calculate the score of each feature；
2) mechanism of different method characteristics screening is different, fraction difference caused by eliminate the difference of Filtering system,
The scores of every kind of algorithm are all handled using the normalization method of maximin, score be limited in [0,1] it
Between, the average of each parameter attribute is then sought, the foundation using average value as feature importance ranking, carries out characteristic value choosing
Select.
3) comprehensive score of parameter is analyzed, the controllability and physical meaning of incorporating parametric determine to influence the pass of vibration
Bond parameter.In terms of scoring event, feeding capacity, micro mist are than table, grinding machine inlet pressure, main exhaust fan rotating speed, circulation valve area mill
The average value of machine inlet temperature excludes the relatively low characteristic parameter of these scores than relatively low.Several parameters of highest scoring, according to from
High to Low order is followed successively by：Thickness of feed layer, grinding machine pressure difference, grinding machine outlet temperature, circulation valve area.
4) analysis in step 2) and step 3), the selection result of characteristic parameter is assessed.The higher ginseng of four scores
In number, three grinding machine pressure difference, thickness of feed layer, Vertical Mill outlet temperature parameters belong to outcome variable, and the value of parameter is at other
The result obtained under the combined influence of controlled variable.And it is that regulation and control variable is not suitable as sentencing for work condition state to circulate valve area
Severed finger mark.
Further, described Vertical Mill health status Cluster Analysis module, the Vertical Mill health state evaluation based on determination
Index, with reference to the data distribution in practical production experience and operating mode storehouse, it is determined that four stable judge index can cause to run different
Normal critical value, pretreated data are further screened in the range of the restriction of multiple critical values, ask satisfaction all
The data of restrictive condition, input data of the obtained the selection result as cluster.Cluster analysis is using K- averages (k-
Means) K operating mode cluster in data set is found.Here K is that user specifies, and the purpose of algorithm is found in data set
K cluster barycenter, the point in data set is distributed to the barycenter nearest apart from the point, and it is corresponding that the point is distributed into the barycenter
Classification.According to the definition for dividing cluster data mode in group, complete to mark the classification of existing operating condition record, it is trustworthy
Determine operating mode class label and be arranged to 0, unsteady-stage conditions label is arranged to 1, and therefrom extracts steady working condition, establishes stable mode work
Condition storehouse.
Described Vertical Mill state estimation index feature acquisition module, exported with vibration, thickness of feed layer, grinding machine pressure difference, grinding machine
Based on the real time data of this 4 state estimation indexs of temperature, average, variance of each parameter in access window time are calculated
With exceptional value occurrence number, the characteristic variable that obtained result is judged as steady working condition.
Further, described Vertical Mill real-time characteristic parameter prediction module, is entered using time series algorithm to running status
Row prediction, and judged with obtained predicted value secondary status.The parameter for needing to predict includes vibration, thickness of feed layer, grinding machine outlet
Temperature, grinding machine pressure difference, exceptional value number, time series models are respectively trained to this five parameters.Obtained model can detect
Whether one section of sequence is stationary sequence, provides the numerical prediction of parameter, is identified with predicted value secondary status.
According to the characteristics of Vertical Mill operating mode, because the external factor such as environment and other specification are to the combined effect of vibration, cause
Operating mode sequence belongs to non-stationary series, and the modeling of time series is carried out using ARIMA models.
Stationary sequence：Pair with a sequence { X (t) }, if numerical value fluctuates in a certain limited range, sequence has constant
Average and constant variance, and it is equal to postpone the auto-covariance of the sequence variables of k phases and auto-correlation coefficient, then and the sequence is
Stationary sequence.
Calculus of differences：It is assumed that the time interval of two sequences is T, calculus of differences is exactly pair the sequence for being mutually divided into k T
It should be worth and do subtraction, during k=1, referred to as first-order difference computing.
The essence of ARIMA models is that calculus of differences is added before ARMA computings, is then modeled using ARMA, is calculated
Formula is as follows：
xt=φ0+φ1xt-1+φ2xt-2+...+φpxt-p+εt-θ1εt-1-θ2εt-2-...-θqεt-q
The model thinks the multiple linear letter of the interference ε of the x values of p phases and preceding q phases before the variable x of t value is
Number.Error term is current random disturbances εt, it is zero-mean white noise sequence.Arma modeling think in the past the p phases sequential value and
The error term joint effect x of phase in past qtValue.
A kind of slag milling system health status recognition methods based on data mining, step are as follows：
1) floor data is analyzed using comprehensive Feature Selection method, it is determined that influenceing stable key parameter, made
For the judge index of stable state.The span of key parameter in analysis of history data, according to its distributed area, it is determined that triggering
The critical value of stable regulation and control；
2) characterized by the stable judge index determined in the step 1), to work condition state progress cluster analysis, using based on
The clustering algorithm of K- averages excavates to data, analysis cluster result obtain each operating mode cluster the characteristics of, obtain history work
State distribution situation in condition；
3) according to the Result of cluster analysis, the running status classification in history operating mode is defined, to the shape belonging to operating mode
State carries out classification mark and screening, obtains stable mode operating mode storehouse；
4) and then to the characteristics of real time data of the collection under Vertical Mill running status analyze, it is determined that carrying out real-time status
The characteristic value of judgement；
5) model training is carried out to the characteristic value determined in step 4) using ARIMA algorithms, the variation tendency of parameter is entered
Row prediction, is judged with predicted value secondary status.
Beneficial effects of the present invention, which are mainly manifested in, can be based on an accurate model to monitor slag milling system in real time
The healthy running status of system, overall merit is carried out in the health status to device systems, obtains and characterizes health status performance ginseng
On the basis of number, the time series of analytical performance parameter, its variation tendency is extended out to obtain the strong of Vertical Mill in following a period of time
Health state change rule, increase the security reliability of slag milling system, be advantageous to prevent accident.The present invention has higher
Accuracy of identification and generalization ability, prediction error it is relatively low, prediction effect is good.
Brief description of the drawings
Fig. 1 is the slag milling system health status identifying system structural representation based on data mining.
Fig. 2 is the preprocessing process figure of Vertical Mill data.
Fig. 3 is Vertical Mill health state evaluation index mining process figure.
Fig. 4 is Vertical Mill health status K-means cluster analysis flow charts.
When Fig. 5 is k=3, cluster analysis divides the parameter distribution probability density figure of group, and (a) is classification 0, and (b) is classification 1,
(c) it is classification 2.
Fig. 6 is that Vertical Mill state estimation index feature obtains flow chart.
Fig. 7 is the time series modeling procedure chart of Vertical Mill real-time characteristic parameter prediction.
Fig. 8 is the original series figure in vibration a period of time.
Fig. 9 is the partial autocorrelation figure after the original series first-order difference in vibration a period of time.
Figure 10 is the predicted value of system and the graph of a relation of actual value.
Embodiment
The present invention can be more fully described in refer to the attached drawing, show certain embodiments of the present invention on figure, but not institute
Some embodiments.In fact, the present invention can by it is many it is different in the form of be embodied as, it should not be regarded as and be only limitted to institute here
The embodiment of elaboration, and embodiments of the invention should be regarded as in order that present disclosure meets applicable conjunction
What method was required and provided.Present invention is elaborated explanation with reference to Figure of description and specific implementation.
Fig. 1 lists the function of each module of slag milling system health status identifying system based on data mining and each
Logical relation between module.
Data preprocessing module, outlier processing, processing empty value, sliding-model control are carried out to the data of Vertical Mill collection and returned
One change is handled, and is got ready for the mining analysis of data；
Vertical Mill health state evaluation index excavates module, and floor data is carried out using a kind of Feature Selection method of synthesis
Mining analysis, obtain influenceing the stable key parameter of Vertical Mill, the index as Vertical Mill health state evaluation；
Vertical Mill health status Cluster Analysis module, the index of the Vertical Mill health state evaluation based on determination, to work condition state
Cluster result analysis is carried out, obtains stable mode operating mode storehouse；
Vertical Mill state estimation index feature acquisition module, analyze Vertical Mill running status under collection real time data spy
Point, it is determined that carrying out the characteristic value of real-time status judgement；
Vertical Mill real-time characteristic parameter prediction module, using ARIMA algorithms to true in Vertical Mill health status feature acquisition module
Fixed characteristic value carries out model training, the variation tendency of Prediction Parameters, is identified with predicted value secondary status.
It is illustrated in figure 2 the preprocessing process figure of Vertical Mill data.The quality of data has very big to the analysis result of data mining
Influence.A large amount of attributes are contained in the Vertical Mill initial data of acquisition, improper value and exceptional value be present, it is necessary to be carried out to data preliminary
Screening, remove improper value and exceptional value, it is ensured that the accuracy of data, and the attribute unrelated with excavation is removed, and to ensure sample
The diversity of notebook data and the completeness of characteristic information.In addition it is also necessary to be handled according to algorithm requirements data, make data
Meet the input requirements of algorithm.
In described data preprocessing module, data outliers processing, processing empty value, pass through data screening and data cleansing
Realize：Vertical Mill feed, grinding, ventilation apparatus, dust separation equipment, hydraulic station, hot-blast stove, warehouse etc. are contained in data with existing
The parameter attribute that 65 partial measuring points obtain.Obtain including 30 main works of Vertical Mill from 65 attributes after attribute selection
The attribute set of skill and performance parameter, including the vibration of Vertical Mill, feeding capacity, electric current, grinding pressure, thickness of feed layer, air feed system
The aperture of cold and hot air-valve, the aperture, powder concentrator rotating speed, each main electrical current etc. for circulating air-valve.In Vertical Mill startup, shutdown and failure
Before and after generation, because operating mode is highly unstable, parameter can big ups and downs.And exist in Vertical Mill data record missing, it is abnormal and
The situation of misregistration.Some records lack some parameter values, have plenty of the factors such as manual entry mistake or sensor fault and lead
Data deviation, missing or the exception of cause.In order to exclude interference of these factors to data, it is necessary to these missing records and mistake
Value is handled, it is ensured that data it is correct, credible, so just can guarantee that the reliable and validity of Result.
In described data preprocessing module, sliding-model control and normalized, converted in fact with data by feature is brief
It is existing：Consider the artificial setting of the feature distribution, enterprise of Vertical Mill data to parameter, and in actual motion parameter controllability
Situations such as, it is brief to data progress, to reduce the dimension of data, save data processing time.By the brief residue of feature
In 14 characteristic parameters, three grinding machine main frame electric current, powder concentrator electric current, main exhaust fan electric current main electrical current parameters are contained.Due to
Reduce can take more concerned be overall energy consumption reduction, rather than single part energy consumption change, therefore construction one
New attribute is used for characterizing the size of power consumption, is named as total current.The value of total current is equal to grinding machine main frame electric current, powder concentrator electricity
Stream, the algebraical sum of main exhaust fan electric current.Feature set so to be selected is simplified to 12 features.
Vertical Mill health state evaluation index mining process figure is illustrated in figure 3, specific excavation step is as follows：
1) using vibration as output y, it is characterized as inputting x with other, feature set to be selected is carried out using five kinds of methods respectively
Screening, calculate the score of each feature；
2) mechanism of different method characteristics screening is different, fraction difference caused by eliminate the difference of Filtering system,
The scores of every kind of algorithm are all handled using the normalization method of maximin, score has been limited in [0,
1] between, the average of each parameter attribute is then sought, the foundation using average value as feature importance ranking, carries out feature
Value selection.Applied in Vertical Mill data, it is as shown in table 1 below that result is obtained after algorithm process.
The scoring event of the different feature selection approach of table 1 feature to be selected
| Method characteristic | Random LASSO | Ridge regression | Random forest | Stable Sexual behavior mode | Recursive feature eliminates | Average |
| Feeding capacity | 0.1 | 0 | 0.03 | 0.08 | 0.18 | 0.08 |
| Micro mist compares table | 0.31 | 0.39 | 0.07 | 0.0 | 0.09 | 0.17 |
| Thickness of feed layer | 0.6 | 1.0 | 1.0 | 0.8 | 0.71 | 0.82 |
| Grinding machine outlet temperature | 0.21 | 0.45 | 0.32 | 0.66 | 0.42 | 0.41 |
| Grinding machine inlet temperature | 0.0 | 0.0 | 0.23 | 0.0 | 0.14 | 0.07 |
| Grinding machine inlet pressure | 0.11 | 0.0 | 0.43 | 0.24 | 0.13 | 0.18 |
| Powder concentrator rotating speed | 0.06 | 0.0 | 0.27 | 0.0 | 0.59 | 0.18 |
| Grinding machine pressure difference | 0.5 | 0.79 | 0.67 | 0.95 | 0.95 | 0.77 |
| Cold wind valve opening | 0.29 | 0.0 | 0.0 | 0.0 | 0.09 | 0.08 |
| Hot blast valve opening | 0.21 | 0.0 | 0.01 | 0.12 | 0.0 | 0.07 |
| Circulate valve area | 0.6 | 0.21 | 0.14 | 0.24 | 0.33 | 0.3 |
| Main exhaust fan rotating speed | 0.01 | 0.1 | 0.01 | 0.0 | 0.0 | 0.02 |
3) comprehensive score of parameter is analyzed, the controllability and physical meaning of incorporating parametric are determined to influenceing vibration
Key parameter.In terms of scoring event, feeding capacity, micro mist are than table, grinding machine inlet pressure, main exhaust fan rotating speed, circulation valve area
The average value of grinding machine inlet temperature excludes the relatively low characteristic parameter of these scores than relatively low.Several parameters of highest scoring, according to
Order from high to low is followed successively by：Thickness of feed layer, grinding machine pressure difference, grinding machine outlet temperature, circulation valve area.
4) analysis in step 2) and step 3), the selection result of characteristic parameter is assessed.The higher ginseng of four scores
In number, three grinding machine pressure difference, thickness of feed layer, Vertical Mill outlet temperature parameters belong to outcome variable, and the value of parameter is at other
The result obtained under the combined influence of controlled variable.And it is that regulation and control variable is not suitable as sentencing for work condition state to circulate valve area
Severed finger mark.
In summary analyze, it is final to determine that 4 vibration, thickness of feed layer, grinding machine pressure difference, grinding machine outlet temperature parameters one act as
The index judged for stable state.
It is illustrated in figure 4 Vertical Mill health status K-means cluster analysis flow charts.With reference to practical production experience and operating mode storehouse
In data distribution, it is determined that four stable judge index can cause the critical value of operation exception, in the restriction of multiple critical values
In the range of pretreated data are further screened, seek the data for meeting all restrictive conditions, obtained the selection result
Input data as cluster.Cluster analysis finds K operating mode cluster in data set using K- averages (k-means).
Here K is that user specifies, and the purpose of algorithm is to find the barycenter of K cluster in data set, and the point in data set is distributed
Classification corresponding to the barycenter is distributed to the barycenter nearest apart from the point, and by the point.
When choosing K=3, cluster result is as follows, and the data point number in cluster centre and each cluster is as shown in table 2, divides group
Parameter distribution probability density figure it is as shown in Figure 5.
Table 2k=3, divide group's cluster centre table
| Classification | Thickness of feed layer | Grinding machine outlet temperature | Grinding machine pressure difference | Grinding machine shell vibrates | Class number |
| 0 | -0.464651 | 0.564229 | -0.110864 | 0.520456 | 2276 |
| 1 | -0.182965 | -0.963877 | -0.437062 | -0.448334 | 2178 |
| 2 | 1.551188 | 0.872879 | 1.284423 | -0.219220 | 937 |
As can be seen from Figure 5：
The feature of classification 0：The thick span of the bed of material between 125~135mm, grinding machine outlet temperature at 100~108 DEG C,
In 2800~3200Pa, vibration values concentrate near 7,8,9 three values grinding machine pressure difference.
The feature of classification 1：The thick span of the bed of material is between 125~144mm, and grinding machine outlet temperature is at 95~103 DEG C, mill
In 2800~3200Pa, vibration values concentrate near 6,7,8 three values machine pressure difference.
The feature of classification 2：The thick span of the bed of material between 140~150mm, grinding machine outlet temperature at 102~108 DEG C,
Grinding machine pressure difference is concentrated between 6~8 in 3200~3500Pa, vibration values.
When choosing K=3, the plyability of vibration is larger, and the distance interval of other three parameters is more reasonable, comes with reference to data
The design production of source Vertical Mill suggests, take three cluster center when obtained classification 0 be defined as unsteady state, in classification 1 and 2
Record is defined as stable state.
It is illustrated in figure 6 Vertical Mill state estimation index feature and obtains flow chart, specific acquisition process is as follows：
1) the real-time working condition data at T moment are gathered, null value and rejecting outliers are carried out to the data collected, if read
During there is null value, give up data or fill up null value with history average.It is disposed, according to the stability index number of setting
According to sampling interval △ t, continue to read the data at next collection moment, carry out Data Detection, repeat this process until obtaining n bars
Record；
2) during collection n bars record, if exceptional value occurs, time that the exceptional value of each parameter occurs is added up
Number.The basis for estimation of exceptional value is with reference to the parameters span obtained from steady working condition pattern base, when the ginseng collected
Number exceeds normal range (NR), then it is assumed that the data at the moment are exceptional value.
3) average and standard deviation of parameters in n bars record are calculated.Each parameter is finally obtained to obtain within the access cycle
Three average, variance and exceptional value number dimension characteristic values that totally 12 numerical value judges as operating mode arrived, to stable state
Judgement.
It is illustrated in figure 7 the time series modeling procedure chart of Vertical Mill real-time characteristic parameter prediction.According to the spy of Vertical Mill operating mode
Point, because the external factor such as environment and other specification are to the combined effect of vibration, cause operating mode sequence to belong to non-stationary series, adopt
The modeling of time series is carried out with ARIMA models.
Stationary sequence：Pair with a sequence { X (t) }, if numerical value fluctuates in a certain limited range, sequence has constant
Average and constant variance, and it is equal to postpone the auto-covariance of the sequence variables of k phases and auto-correlation coefficient, then and the sequence is
Stationary sequence.
Calculus of differences：It is assumed that the time interval of two sequences is T, calculus of differences is exactly pair the sequence for being mutually divided into k T
It should be worth and do subtraction, during k=1, referred to as first-order difference computing.
The essence of ARIMA models is that calculus of differences is added before ARMA computings, is then modeled using ARMA, is calculated
Formula is as follows：
xt=φ0+φ1xt-1+φ2xt-2+...+φpxt-p+εt-θ1εt-1-θ2εt-2-...-θqεt-q
The model thinks the multiple linear letter of the interference ε of the x values of p phases and preceding q phases before the variable x of t value is
Number.Error term is current random disturbances εt, it is zero-mean white noise sequence.Arma modeling think in the past the p phases sequential value and
The error term joint effect x of phase in past qtValue.
The process that explanation is modeled using time series by taking vibration values as an example.First to being collected in one section of continuous time
Vibration values carry out stationarity detection, fetch at intervals of 5 seconds, it is continuous 35 vibration value data be illustrated in fig. 8 shown below, can see
It is on the rise to go out the sequence, belongs to non-stationary series.Auto-correlation coefficient is asked for sequence, the absolute value of coefficient correlation is big for a long time
In zero, show that the sequence has long-term correlation.The partial autocorrelation figure such as Fig. 9 for this sequence obtain after first-order difference
It is shown.It can be seen that the timing diagram of sequence fluctuates near average after first-order difference, and fluctuation range is little, so first-order difference
Sequence afterwards is stationary sequence.
Then white noise sound detection is carried out to the sequence after first-order difference, obtained P values are less than 0.05, so after first-order difference
Sequence belong to steady non-white noise sequence, can be fitted with arma modeling.Next arma modeling is carried out determining rank,
It is exactly the parameter in modulus type, the size of the BIC information content obtained according to p, q all combinations determines that selection makes BIC information
Amount reaches minimum p, q combination.Can is predicted using the ARIMA models of foundation after model order.
Forecast model can provide the predicted value, standard error and confidential interval of continuous 5 minutes, predicted value and actual value
Relation is as shown in Figure 10.As can be seen from the figure predict that error is relatively low, predicted value can reflect the variation tendency of numerical value, mould substantially
The prediction effect of type is good.
Claims (7)
1. a kind of slag milling system health status identifying system based on data mining, it is characterised in that the system includes：Number
Data preprocess module, Vertical Mill health state evaluation index are excavated module, Vertical Mill health status Cluster Analysis module, Vertical Mill state and commented
Estimate index feature acquisition module, Vertical Mill real-time characteristic parameter prediction module, wherein：
Data preprocessing module, outlier processing, processing empty value, sliding-model control and normalization are carried out to the data of Vertical Mill collection
Processing；
Vertical Mill health state evaluation index excavates module, and floor data excavate using a kind of comprehensive characteristics screening technique and divided
Analysis, the comprehensive characteristics screening technique eliminate this by random lasso, ridge regression, random forest, stable Sexual behavior mode and recursive feature
Five kinds of methods synthesis composition, obtain influenceing the stable key parameter of Vertical Mill, it is determined that vibration, thickness of feed layer, grinding machine pressure difference, grinding machine go out
Index of mouth 4 parameters of temperature collectively as Vertical Mill health state evaluation；
Vertical Mill health status Cluster Analysis module, the index of the Vertical Mill health state evaluation based on determination, work condition state is carried out
Cluster result is analyzed, and the characteristics of obtained each operating mode cluster, is obtained the state distribution situation in history operating mode, is defined history operating mode
In running status classification, classification mark and screening are carried out to the state belonging to operating mode, obtain stable mode operating mode storehouse；
Vertical Mill state estimation index feature acquisition module, analyze Vertical Mill running status under collection real time data the characteristics of, with
Vibration, thickness of feed layer, grinding machine pressure difference, grinding machine outlet temperature this 4 state estimation indexs real time data based on, calculate each
Average, variance and exceptional value occurrence number of the parameter in access window time, it is determined that carrying out the characteristic value of real-time status judgement；
Vertical Mill real-time characteristic parameter prediction module, using ARIMA algorithms to true in Vertical Mill state estimation index feature acquisition module
Fixed characteristic value carries out model training, the variation tendency of Prediction Parameters, is identified with predicted value secondary status.
2. the system as claimed in claim 1, it is characterised in that in described data preprocessing module, data outliers processing,
Processing empty value, realized by data screening and data cleansing；Sliding-model control and normalized, become by feature is brief with data
Change realization.
3. the system as claimed in claim 1, it is characterised in that described comprehensive characteristics screening technique, become by solving input
Measure the relation between output variable, to the scores of every kind of algorithm using the normalization method of maximin at
Reason, between score has been limited in [0,1], then seeks the average of each parameter attribute, important using average value as feature
Property sequence foundation, the importance of feature is assessed according to the scores after processing, carry out characteristic value selection.
4. the system as claimed in claim 1, it is characterised in that described cluster result analysis is k-means cluster analyses, choosing
K=3 is taken, obtained classification 0 is defined as unsteady state, and the record in classification 1 and 2 is defined as stable state；The feature of classification 0：
The thick span of the bed of material between 125~135mm, grinding machine outlet temperature at 100~108 DEG C, grinding machine pressure difference 2800~
3200Pa, vibration values are concentrated near 7,8,9 three values；The feature of classification 1：The thick span of the bed of material 125~144mm it
Between, grinding machine outlet temperature is at 95~103 DEG C, and for grinding machine pressure difference in 2800~3200Pa, it is attached that vibration values concentrate on 6,7,8 three values
Closely；The feature of classification 2：The thick span of the bed of material is between 140~150mm, and grinding machine outlet temperature is at 102~108 DEG C, grinding machine pressure
Difference is concentrated between 6~8 in 3200~3500Pa, vibration values.
5. the system as claimed in claim 1, it is characterised in that described Vertical Mill real-time characteristic parameter prediction module, under
The ARIMA algorithms of formula carry out model training to the characteristic value determined in Vertical Mill state estimation index feature acquisition module：
xt=φ0+φ1xt-1+φ2xt-2+...+φpxt-p+εt-θ1εt-1-θ2εt-2-...-θqεt-q
Wherein, xt-pFor the x values of preceding p phases, εt-qFor the interference value of preceding q phases, φ0~φpFor the coefficient of different time x values, θ1
~θqFor the coefficient of different time ε values.
6. a kind of slag milling system health status recognition methods based on data mining, it is characterised in that including step：
1) floor data is analyzed using comprehensive characteristics screening technique, the comprehensive characteristics screening technique by random lasso,
Ridge regression, random forest, stable Sexual behavior mode and recursive feature eliminate this five kinds of method synthesis compositions, obtain influenceing Vertical Mill stabilization
Key parameter, it is determined that vibration, thickness of feed layer, 4 grinding machine pressure difference, grinding machine outlet temperature parameters are commented collectively as Vertical Mill health status
The index estimated；The span of key parameter in analysis of history data, according to its distributed area, it is determined that the stable regulation and control of triggering are faced
Dividing value；
2) characterized by the stable judge index determined in step 1), cluster analysis is carried out to work condition state, uses clustering algorithm
Data are excavated, analysis cluster result obtain each operating mode cluster the characteristics of, obtain history operating mode in state distribution feelings
Condition；
3) according to the Result of cluster analysis, the running status classification in history operating mode is defined, the state belonging to operating mode is entered
Row classification marks and screening, obtains stable mode operating mode storehouse；
4) the characteristics of real time data of the collection under Vertical Mill running status, is analyzed, with vibration, thickness of feed layer, grinding machine pressure
Based on difference, the real time data of grinding machine outlet temperature this 4 state estimation indexs, each parameter is calculated in access window time
Average, variance and exceptional value occurrence number, it is determined that carry out real-time status judgement characteristic value；
5) model training is carried out to the characteristic value determined in step 4) using ARIMA algorithms, the variation tendency of parameter is carried out in advance
Survey, judged with predicted value secondary status.
7. method as claimed in claim 6, it is characterised in that the comprehensive characteristics screening technique, by solving input variable
Relation between output variable, the importance of each feature is given a mark using five kinds of methods respectively, to five kinds of score feelings
Condition is handled, and the importance of feature is assessed according to the scores after processing, determines the key in feature set to be selected
Feature.
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