CN110262460A - A kind of combination Clustering carries out the concrete piston failure prediction method of feature extraction - Google Patents
A kind of combination Clustering carries out the concrete piston failure prediction method of feature extraction Download PDFInfo
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- CN110262460A CN110262460A CN201910585584.6A CN201910585584A CN110262460A CN 110262460 A CN110262460 A CN 110262460A CN 201910585584 A CN201910585584 A CN 201910585584A CN 110262460 A CN110262460 A CN 110262460A
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- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000000605 extraction Methods 0.000 title claims abstract description 19
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 238000003064 k means clustering Methods 0.000 claims abstract description 5
- 238000010220 Pearson correlation analysis Methods 0.000 claims abstract description 4
- 230000000007 visual effect Effects 0.000 claims abstract description 4
- 238000005086 pumping Methods 0.000 claims description 22
- 230000000694 effects Effects 0.000 claims description 6
- 239000000203 mixture Substances 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 4
- 241001269238 Data Species 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 239000010720 hydraulic oil Substances 0.000 claims description 3
- 238000003756 stirring Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 235000013399 edible fruits Nutrition 0.000 claims 1
- 239000003921 oil Substances 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 abstract description 4
- 238000012423 maintenance Methods 0.000 description 7
- 238000012544 monitoring process Methods 0.000 description 5
- 230000004913 activation Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012407 engineering method Methods 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Abstract
The present invention is more particularly directed to the concrete piston failure prediction methods that a kind of combination Clustering carries out feature extraction.This combines Clustering to carry out the concrete piston failure prediction method of feature extraction, acquires floor data and is uploaded to cloud floor data library；Between carrying out visual Pearson correlation analysis each feature, and three clusters are carried out to the feature for the strong correlation selected；Calculate separately the silhouette coefficient of cluster result；Using Light GBM, XGBoost and CatBoost algorithm is trained and predicts to data respectively, finally takes three prediction results to pass through the method averaged and is merged.This combines the concrete piston failure prediction method of Clustering progress feature extraction, the characteristic information for extracting relatively deep in feature is excavated in conjunction with pearson correlation property coefficient and K-means clustering method, enhance the accuracy and robustness of prediction, the reliability and accuracy of concrete piston fault pre-alarming are greatly improved, unnecessary production loss can be reduced.
Description
Technical field
The present invention relates to industrial equipment fault pre-alarming technical field, in particular to a kind of combination Clustering carries out feature and mentions
The concrete piston failure prediction method taken.
Background technique
Maintenance to production equipment, there are two main classes for traditional way, one is etc. failures occur after repair again, but this meeting
Lead to the halt production of unplanned property, economic loss is big；It is for second to be safeguarded with standing plans, but maintenance cost is high, when shutdown
Between it is long.Predictive maintenance, then by analysis malfunction history data and Real-time Monitoring Data, to the remaining life of equipment key part
Or failure carries out look-ahead early warning, and carries out maintenance and repair accordingly, to reduce equipment nonscheduled down time, reduce maintenance
Cost.
But there is no mature approaches of predictive maintenance at present.Traditional Knowledge based engineering method is mainly with related special
Based on the enlightening Heuristics of family and operator, connection relationship during qualitative or quantitative description between each unit,
Fault propagation mode etc., after abnormal sign occurs in equipment through the modes simulation process expert such as reasoning, deduction in monitoring
Inferential capability, to be automatically performed equipment fault early-warning and equipment monitoring.Such method is based on experience, can not establish accurate number
Learn model, it is usually poor with reliability from precision of prediction, it is difficult to according to existing status of equipment accurately early warning to future can
The equipment fault of energy.
A possibility that equipment fault early-warning and status monitoring are obtained according to equipment moving law or observation omen is true in equipment
Before just breaking down, the unusual condition of timely HERALD equipment takes appropriate measures, and can reduce equipment event to the greatest extent
Loss caused by barrier.As the scale and complexity of apparatus and engineering control system increasingly increase, to guarantee to produce
The safety and steady of journey, timely and effectively being monitored by reliable condition monitoring technology and diagnosing process exception just seems especially urgent
With it is important.
Concrete piston is the critical component and expendable parts of concrete mixer, and piston failure will lead to pump truck can not be just
Often work, while may cause other corollary equipments of entire building site can not normal construction, to bring sizable economic loss.
The specific operating condition of piston life and equipment etc. is closely related, and the real-time working condition data etc. of pump truck are uploaded to industry by Internet of Things
Internet cloud platform, the data based on accumulation establish suitable model, are expected to concrete piston during following certain task
The failure being inside likely to occur makes effective prediction and warning, so that operating personnel be reminded to carry out necessary maintenance before construction, keeps away
Exempt from bring economic loss due to UNPLANNED DOWNTIME.
Currently, there are following disadvantages for existing technology:
1. the non-machine learning algorithm of tradition can not predict concrete piston failure, once huge lose will be generated by breaking down
Damage, or even jeopardize worker's personal safety.
2. characteristic processing method is simply single in machine learning algorithm before, and potential information is excavated in harmless effective offer,
Strong feature is provided, forecasting accuracy is reduced.
Based on the above situation, the invention proposes the concrete piston failure predications that a kind of combination Clustering carries out feature extraction
Method.
Summary of the invention
In order to compensate for the shortcomings of the prior art, the present invention provides a kind of combination Clusterings being simple and efficient to carry out feature
The concrete piston failure prediction method of extraction.
The present invention is achieved through the following technical solutions:
A kind of combination Clustering carries out the concrete piston failure prediction method of feature extraction, which is characterized in that including following
Step:
Step 1: floor data is acquired using the industrial Internet of Things edge calculations box being mounted on concrete mixer, and
The floor data of acquisition is uploaded to cloud floor data library；
Step 2: after having acquired floor data, first between carrying out visual Pearson correlation analysis each feature,
The feature of strong correlation is selected, and three clusters are carried out to the feature for the strong correlation selected；
Step 3: calculating separately silhouette coefficient after obtaining cluster result to three central points of three clusters, obtaining three
Column new feature is used to describe the quality of cluster；
Step 4: XGBoost and CatBoost algorithm is trained and predicts to data respectively, most using Light GBM
It takes three prediction results to pass through the method averaged afterwards to be merged.
In the first step, the operating condition number of box acquisition concrete mixer is calculated using sensor and marginal end Internet of Things
According to floor data collected includes pistons work duration (newly changing cumulative activation duration after piston, numeric type), and engine turns
Speed, pump speed, pumping pressure, hydraulic oil temperature, flow gear, distribution pressure, row's magnitude of current (is the correspondence operating condition of pump truck
Value, numeric type), low tension switch, high-voltage switch gear stirs superpressure signal, and it is positive to pump, it is anti-to pump (switching value) and device type (pump truck
Type, classification type).
In the second step, after the floor data based on acquisition is analyzed, observation obtains engine speed, pump speed
And pumping pressure three two-by-two between all with 0.65 or more correlation, therefore by engine speed, pump speed and pumping pressure
Three feature floor datas of power establish new data set with these three features after copying, and utilize K-means clustering algorithm, right
Obtained new data set carries out three clusters.
In the second step, using the KMeans function under sklearn.cluster packet in python to engine speed,
The data set that pump speed and pumping pressure three are constituted carries out three clusters.
To engine speed, when the data set of pump speed and pumping pressure three composition carries out three clusters, of cluster
The accuracy test of many times of number, i.e. the basis of design prediction of K value determines that the K value for selecting prediction result best, parameter is set as
KMeans (K=3).
In the third step, using the silhouette_samples under sklearn.metrics packet in python to poly-
Class result calculates silhouette coefficient.
In the third step, under normal circumstances, the engine speed of concrete piston data, pump speed and three kinds of pumping pressure
The data variation of feature has more consistency, therefore Clustering Effect is more preferable, and silhouette coefficient is bigger；And the concrete to break down in the future
These three features of piston data will get muddled, and Clustering Effect will be deteriorated, and corresponding silhouette coefficient will become smaller.
In 4th step, new feature is added in the data set of the feature composition of original strong correlation, forms new instruction
Practice collection；And corresponding normal tag and faulty tag are recorded in the label file of training set, it is indicated respectively with 0,1；Wherein 0 table
Show that the sample corresponds to piston and do not break down during pumping 2000 side's concrete task in future, 1 indicates that the sample is corresponding
Piston future pump 2000 side's concrete task during failure has occurred.
The beneficial effects of the present invention are: this combines Clustering to carry out the concrete piston failure prediction method of feature extraction, tie
It closes pearson correlation property coefficient and K-means clustering method excavates the characteristic information for extracting relatively deep in feature, enhance
The accuracy and robustness of prediction greatly improve the reliability and accuracy of concrete piston fault pre-alarming, and can reduce need not
The production loss wanted.
Detailed description of the invention
Attached drawing 1 is the concrete piston failure prediction method schematic diagram that the present invention combines Clustering progress feature extraction.
Specific embodiment
In order to which technical problems, technical solutions and advantages to be solved are more clearly understood, tie below
Embodiment is closed, the present invention will be described in detail.It should be noted that specific embodiment described herein is only to explain
The present invention is not intended to limit the present invention.
This combines the concrete piston failure prediction method of Clustering progress feature extraction, comprising the following steps:
Step 1: floor data is acquired using the industrial Internet of Things edge calculations box being mounted on concrete mixer, and
The floor data of acquisition is uploaded to cloud floor data library；
Step 2: after having acquired floor data, first between carrying out visual Pearson correlation analysis each feature,
The feature of strong correlation is selected, and three clusters are carried out to the feature for the strong correlation selected；
Step 3: calculating separately silhouette coefficient after obtaining cluster result to three central points of three clusters, obtaining three
Column new feature is used to describe the quality of cluster；
Step 4: XGBoost and CatBoost algorithm is trained and predicts to data respectively, most using Light GBM
It takes three prediction results to pass through the method averaged afterwards to be merged.
In the first step, the operating condition number of box acquisition concrete mixer is calculated using sensor and marginal end Internet of Things
According to floor data collected includes pistons work duration (newly changing cumulative activation duration after piston, numeric type), and engine turns
Speed, pump speed, pumping pressure, hydraulic oil temperature, flow gear, distribution pressure, row's magnitude of current (is the correspondence operating condition of pump truck
Value, numeric type), low tension switch, high-voltage switch gear stirs superpressure signal, and it is positive to pump, it is anti-to pump (switching value) and device type (pump truck
Type, classification type).
In the second step, after the floor data based on acquisition is analyzed, observation obtains engine speed, pump speed
And pumping pressure three two-by-two between all with 0.65 or more correlation, therefore by engine speed, pump speed and pumping pressure
Three feature floor datas of power establish new data set with these three features after copying, and utilize K-means clustering algorithm, right
Obtained new data set carries out three clusters.
In the second step, using the KMeans function under sklearn.cluster packet in python to engine speed,
The data set that pump speed and pumping pressure three are constituted carries out three clusters.
To engine speed, when the data set of pump speed and pumping pressure three composition carries out three clusters, of cluster
The accuracy test of many times of number, i.e. the basis of design prediction of K value determines that the K value for selecting prediction result best, parameter is set as
KMeans (K=3).
In the third step, using the silhouette_samples under sklearn.metrics packet in python to poly-
Class result calculates silhouette coefficient.
In the third step, under normal circumstances, the engine speed of concrete piston data, pump speed and three kinds of pumping pressure
The data variation of feature has more consistency, therefore Clustering Effect is more preferable, and silhouette coefficient is bigger；And the concrete to break down in the future
These three features of piston data will get muddled, and Clustering Effect will be deteriorated, and corresponding silhouette coefficient will become smaller.
In 4th step, new feature is added in the data set of the feature composition of original strong correlation, forms new instruction
Practice collection；And corresponding normal tag and faulty tag are recorded in the label file of training set, it is indicated respectively with 0,1；Wherein 0 table
Show that the sample corresponds to piston and do not break down during pumping 2000 side's concrete task in future, 1 indicates that the sample is corresponding
Piston future pump 2000 side's concrete task during failure has occurred.
Embodiment described above, only one kind of the specific embodiment of the invention, those skilled in the art is in this hair
The usual variations and alternatives carried out in bright technical proposal scope should be all included within the scope of the present invention.
Claims (8)
1. the concrete piston failure prediction method that a kind of combination Clustering carries out feature extraction, which is characterized in that including following step
It is rapid:
Step 1: acquiring floor data using the industrial Internet of Things edge calculations box being mounted on concrete mixer, and will adopt
The floor data of collection is uploaded to cloud floor data library；
Step 2:, first between visual Pearson correlation analysis is carried out each feature, being selected after having acquired floor data
The feature of strong correlation, and three clusters are carried out to the feature for the strong correlation selected；
Step 3: calculate separately silhouette coefficient after obtaining cluster result to three central points of three clusters, it is new to obtain three column
Feature is used to describe the quality of cluster；
Step 4: XGBoost and CatBoost algorithm is trained and predicts to data respectively using Light GBM, finally take
Three prediction results pass through the method averaged and are merged.
2. combination Clustering according to claim 1 carries out the concrete piston failure prediction method of feature extraction, feature
It is: in the first step, the floor data of box acquisition concrete mixer, institute is calculated using sensor and marginal end Internet of Things
The floor data of acquisition includes pistons work duration, engine speed, pump speed, pumping pressure, hydraulic oil temperature, flow shelves
Position, distribution pressure arrange the magnitude of current, low tension switch, and high-voltage switch gear stirs superpressure signal, positive to pump, anti-pump and device type.
3. combination Clustering according to claim 1 or 2 carries out the concrete piston failure prediction method of feature extraction, special
Sign is: in the second step, after the floor data based on acquisition is analyzed, observation obtains engine speed, pump speed
And pumping pressure three two-by-two between all with 0.65 or more correlation, therefore by engine speed, pump speed and pumping pressure
Three feature floor datas of power establish new data set with these three features after copying, and utilize K-means clustering algorithm, right
Obtained new data set carries out three clusters.
4. combination Clustering according to claim 3 carries out the concrete piston failure prediction method of feature extraction, feature
It is: in the second step, using the KMeans function under sklearn.cluster packet in python to engine speed, oil
The data set that revolution speed and pumping pressure three are constituted carries out three clusters.
5. combination Clustering according to claim 4 carries out the concrete piston failure prediction method of feature extraction, feature
It is: to engine speed, when the data set of pump speed and pumping pressure three composition carries out three clusters, the number of cluster,
That is the accuracy test of many times of the basis of design prediction of K value determines that the K value for selecting prediction result best, parameter is set as
KMeans (K=3).
6. combination Clustering according to claim 3 carries out the concrete piston failure prediction method of feature extraction, feature
It is: in the third step, cluster is tied using the silhouette_samples under sklearn.metrics packet in python
Fruit calculates silhouette coefficient.
7. combination Clustering according to claim 6 carries out the concrete piston failure prediction method of feature extraction, feature
It is: in the third step, under normal circumstances, and the engine speed of concrete piston data, three kinds of spies of pump speed and pumping pressure
The data variation of sign has more consistency, therefore Clustering Effect is more preferable, and silhouette coefficient is bigger；And the concrete to break down in the future is living
These three features of plug data will get muddled, and Clustering Effect will be deteriorated, and corresponding silhouette coefficient will become smaller.
8. combination Clustering according to claim 3 carries out the concrete piston failure prediction method of feature extraction, feature
It is: in the 4th step, new feature is added in the data set of the feature composition of original strong correlation, forms new training
Collection；And corresponding normal tag and faulty tag are recorded in the label file of training set, it is indicated respectively with 0,1；Wherein 0 indicate
The sample corresponds to piston and does not break down during pumping 2000 side's concrete task in future, and 1 indicates that the sample is corresponding
Failure has occurred during pumping 2000 side's concrete task in future in piston.
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