CN109670551A - The failure prediction method and device of engineering mechanical device - Google Patents
The failure prediction method and device of engineering mechanical device Download PDFInfo
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- CN109670551A CN109670551A CN201811568590.2A CN201811568590A CN109670551A CN 109670551 A CN109670551 A CN 109670551A CN 201811568590 A CN201811568590 A CN 201811568590A CN 109670551 A CN109670551 A CN 109670551A
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Abstract
The present invention provides a kind of failure prediction method of engineering mechanical device and devices, which comprises obtains the data of device parameter, the device parameter includes: working condition, operating ambient temperature and working environment humidity；The data are handled, by data clusters to together, form cluster；The distance that the cluster arrives central point is calculated, evaluation and test value, which is equal to, clusters the distance for arriving central point divided by cluster diameter again divided by preset threshold；According to the evaluation and test value, the failure rate of pre- measurement equipment.Exception and failure are detected in this way, are early provided warning information, are avoided or minimize downtime, the attended operation of optimizing cycle, to greatly improve maintenance efficiency and benefit.
Description
Technical field
The present invention relates to mechanical equipment fault electric powder predictions, in particular to a kind of event of engineering mechanical device
Hinder prediction technique and device.
Background technique
In enterprise's manufacture production, machine has liberated labourer, but these machines or system can break down, some events
Barrier is only made troubles, and some failures are then of vital importance.When risk is very high, need routinely to tie up system
Shield.Therefore it needs to carry out some anticipations to the failure of equipment, the maintenance for everyday devices.
Summary of the invention
For above-mentioned problems of the prior art, the present invention provides a kind of failure predication sides of engineering mechanical device
Method and device.
In a first aspect, the embodiment of the invention provides a kind of failure prediction method of engineering mechanical device, the method packet
It includes:
The data of device parameter are obtained, the device parameter includes: working condition, operating ambient temperature and working environment
Humidity；
The data are handled, by data clusters to together, form cluster；
Calculate it is described cluster arrive central point distance, evaluation and test value be equal to cluster to central point distance divided by cluster diameter again
Divided by preset threshold；
According to the evaluation and test value, the failure rate of pre- measurement equipment.
Further, the calculation method of the cluster diameter, comprising:
Obtain the historical data of device parameter；
The historical data is handled, by the identical data clusters of type in historical data to together, and will be abnormal
Numerical value removal, history of forming cluster, select point centered on the center of Historic Clustering；
Cluster diameter is calculated, cluster diameter is equal to the boundary of Historic Clustering to the distance of the central point.
Further, the data are handled, by data clusters to together, form cluster, comprising:
Abnormal numerical value in the data is rejected.
Further, the data are handled, by data clusters to together, form cluster, comprising:
The identical data clusters of type in the data are formed into cluster to together using Kmeans algorithm.
Further, the data are handled, by data clusters to together, form cluster, comprising:
Dimensionality reduction is carried out to the data.
Second aspect, the embodiment of the invention also provides a kind of fault prediction device of engineering mechanical device, described devices
Include:
Module is obtained, for obtaining the data of device parameter, the device parameter includes: working condition, working environment temperature
Degree and working environment humidity；
Cluster module, by data clusters to together, forms cluster for handling the data；
Computing module, the distance of central point is arrived for calculating the cluster, and evaluation and test value is equal to the distance that cluster arrives central point
Divided by cluster diameter again divided by preset threshold；
Evaluation and test module, for according to the evaluation and test value, the failure rate of pre- measurement equipment.
Further, the calculation method of the cluster diameter, comprising:
Obtain the historical data of device parameter；
The historical data is handled, by the identical data clusters of type in historical data to together, and will be abnormal
Numerical value removal, history of forming cluster, select point centered on the center of Historic Clustering；
Cluster diameter is calculated, cluster diameter is equal to the boundary of Historic Clustering to the distance of the central point.
Further, the cluster module is also used to:
Abnormal numerical value in the data is rejected.
Further, the cluster module is also used to:
The identical data clusters of type in the data are formed into cluster to together using Kmeans algorithm.
The third aspect, the embodiment of the invention provides a kind of computer storage mediums, for being stored as described in second aspect
Device used in computer software instructions.
The embodiment of the present invention bring it is following the utility model has the advantages that
The embodiment of the invention provides a kind of failure prediction method of engineering mechanical device and devices, which comprises
The data of device parameter are obtained, the device parameter includes: working condition, operating ambient temperature and working environment humidity；By institute
It states data to be handled, by data clusters to together, forms cluster；Calculate the distance that the cluster arrives central point, evaluation and test value etc.
In cluster to central point distance divided by cluster diameter again divided by preset threshold；According to the evaluation and test value, the failure of pre- measurement equipment
Rate.Exception and failure are detected in this way, are early provided warning information, are avoided or minimize downtime, optimizing cycle
Attended operation, to greatly improve maintenance efficiency and benefit.
Other features and advantages of the present invention will illustrate in the following description, also, partly become from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention are in specification, claims
And specifically noted structure is achieved and obtained in attached drawing.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art be briefly described, it should be apparent that, it is described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is a kind of flow chart of the failure prediction method of engineering mechanical device provided by first embodiment of the invention；
Fig. 2 is the flow chart of the calculation method of cluster diameter provided by first embodiment of the invention；
Fig. 3 is a kind of structural frames of the fault prediction device of engineering mechanical device provided by second embodiment of the invention
Figure.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with attached drawing to the present invention
Technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.The component of embodiments of the present invention, which are generally described and illustrated herein in the accompanying drawings can be matched with a variety of different
It sets to arrange and design.Therefore, the detailed description of the embodiment of the present invention provided in the accompanying drawings is not intended to limit below
The range of claimed invention, but it is merely representative of selected embodiment of the invention.Based on the embodiments of the present invention, originally
Field those of ordinary skill every other embodiment obtained without making creative work, belongs to the present invention
The range of protection.
Embodiment one
A kind of flow chart of the failure prediction method of engineering mechanical device shown in Figure 1, specifically comprises the following steps:
S101. the data of device parameter are obtained, the device parameter includes: working condition, operating ambient temperature and work
Ambient humidity；
S102. the data are handled, by data clusters to together, forms cluster；
Specifically, dimensionality reduction is carried out to the data first.Then the abnormal numerical value in the data is rejected, it is then sharp
The identical data clusters of type in the data are formed into cluster to together with Kmeans algorithm.
Further, the data are handled, by data clusters to together, form cluster, comprising:
S103. the distance that the cluster arrives central point is calculated, evaluation and test value is equal to cluster to the distance of central point divided by cluster
Diameter is again divided by preset threshold；
Wherein, central point is the historical data using device parameter, and the identical data clusters of type in historical data are arrived
Together, and by abnormal numerical value it removes, history of forming cluster selects point centered on the center of Historic Clustering.
S104. according to the evaluation and test value, the failure rate of pre- measurement equipment.
Further, the calculation method of the cluster diameter, comprising:
S11. the historical data of device parameter is obtained；
S12. the historical data is handled, by the identical data clusters of type in historical data to together, and will
Abnormal numerical value removal, history of forming cluster select point centered on the center of Historic Clustering；
S13. cluster diameter is calculated, cluster diameter is equal to the boundary of Historic Clustering to the distance of the central point.
Further, the data are handled, by data clusters to together, form cluster, comprising:
Abnormal numerical value in the data is rejected.
Further, the data are handled, by data clusters to together, form cluster, comprising:
The identical data clusters of type in the data are formed into cluster to together using Kmeans algorithm.
Further, the data are handled, by data clusters to together, form cluster, comprising:
Dimensionality reduction is carried out to the data.
Embodiment two
For the failure prediction method of engineering mechanical device provided by previous embodiment, the embodiment of the invention provides one
The device of the failure predication of kind engineering mechanical device, a kind of device of the failure predication of engineering mechanical device shown in Figure 3
Structural block diagram, which includes following part:
Module 31 is obtained, for obtaining the data of device parameter, the device parameter includes: working condition, working environment
Temperature and working environment humidity；
Cluster module 32, by data clusters to together, forms cluster for handling the data；
Computing module 33, arrives the distance of central point for calculating the cluster, evaluation and test value be equal to cluster to central point away from
From divided by cluster diameter again divided by preset threshold；
Evaluation and test module 34, for according to the evaluation and test value, the failure rate of pre- measurement equipment.
Further, the calculation method of the cluster diameter, comprising:
Obtain the historical data of device parameter；
The historical data is handled, by the identical data clusters of type in historical data to together, and will be abnormal
Numerical value removal, history of forming cluster, select point centered on the center of Historic Clustering；
Cluster diameter is calculated, cluster diameter is equal to the boundary of Historic Clustering to the distance of the central point.
Further, the cluster module 32 is also used to:
Abnormal numerical value in the data is rejected.
Further, the cluster module 32 is also used to:
The identical data clusters of type in the data are formed into cluster to together using Kmeans algorithm.
The embodiment of the invention also provides a kind of computer storage mediums, for being stored as device provided by the above embodiment
Computer software instructions used.
In addition, in the description of the embodiment of the present invention unless specifically defined or limited otherwise, term " installation ", " phase
Even ", " connection " shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can
To be mechanical connection, it is also possible to be electrically connected；It can be directly connected, can also can be indirectly connected through an intermediary
Connection inside two elements.For the ordinary skill in the art, above-mentioned term can be understood at this with concrete condition
Concrete meaning in invention.
In the description of the present invention, it should be noted that term " center ", "upper", "lower", "left", "right", "vertical",
The orientation or positional relationship of the instructions such as "horizontal", "inner", "outside" be based on the orientation or positional relationship shown in the drawings, merely to
Convenient for description the present invention and simplify description, rather than the device or element of indication or suggestion meaning must have a particular orientation,
It is constructed and operated in a specific orientation, therefore is not considered as limiting the invention.
In addition, term " first ", " second ", " third " are used for description purposes only, it is not understood to indicate or imply phase
To importance.
It should be noted that in embodiment provided by the present invention, it should be understood that disclosed system and method, it can
To realize by another way.The apparatus embodiments described above are merely exemplary, for example, the unit is drawn
Point, only a kind of logical function partition, there may be another division manner in actual implementation, in another example, multiple units or group
Part can be combined or can be integrated into another system, or some features can be ignored or not executed.It is described to be used as separation unit
The unit that part illustrates may or may not be physically separated, and component shown as a unit can be or can also
Not to be physical unit, it can it is in one place, or may be distributed over multiple network units.It can be according to reality
Needs some or all of the units may be selected to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in embodiment provided by the invention can integrate in one processing unit, it can also
To be that each unit physically exists alone, can also be integrated in one unit with two or more units.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, server or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.
And storage medium above-mentioned includes: that USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic or disk.
In addition, term " first ", " second ", " third " are used for description purposes only, it is not understood to indicate or imply phase
To importance.
Finally, it should be noted that embodiment described above, only a specific embodiment of the invention, to illustrate the present invention
Technical solution, rather than its limitations, scope of protection of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, those skilled in the art should understand that: anyone skilled in the art
In the technical scope disclosed by the present invention, it can still modify to technical solution documented by previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of technical solution of the embodiment of the present invention, should all cover in protection of the invention
Within the scope of.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. a kind of failure prediction method of engineering mechanical device, which is characterized in that the described method includes:
The data of device parameter are obtained, the device parameter includes: working condition, operating ambient temperature and working environment humidity；
The data are handled, by data clusters to together, form cluster；
Calculate it is described cluster arrive central point distance, evaluation and test value be equal to cluster to central point distance divided by cluster diameter again divided by
Preset threshold；
According to the evaluation and test value, the failure rate of pre- measurement equipment.
2. the method according to claim 1, wherein the calculation method of the cluster diameter, comprising:
Obtain the historical data of device parameter；
The historical data is handled, by the identical data clusters of type in historical data to together, and by abnormal number
Value removal, history of forming cluster, selects point centered on the center of Historic Clustering；
Cluster diameter is calculated, cluster diameter is equal to the boundary of Historic Clustering to the distance of the central point.
3. the method according to claim 1, wherein the data are handled, by data clusters to together,
Form cluster, comprising:
Abnormal numerical value in the data is rejected.
4. the method according to claim 1, wherein the data are handled, by data clusters to together,
Form cluster, comprising:
The identical data clusters of type in the data are formed into cluster to together using Kmeans algorithm.
5. the method according to claim 1, wherein the data are handled, by data clusters to together,
Form cluster, comprising:
Dimensionality reduction is carried out to the data.
6. a kind of fault prediction device of engineering mechanical device, which is characterized in that described device includes:
Obtain module, for obtaining the data of device parameter, the device parameter includes: working condition, operating ambient temperature and
Working environment humidity；
Cluster module, by data clusters to together, forms cluster for handling the data；
Computing module, for calculate it is described cluster arrive central point distance, evaluation and test value be equal to cluster to central point distance divided by
Diameter is clustered again divided by preset threshold；
Evaluation and test module, for according to the evaluation and test value, the failure rate of pre- measurement equipment.
7. device according to claim 6, which is characterized in that the calculation method of the cluster diameter, comprising:
Obtain the historical data of device parameter；
The historical data is handled, by the identical data clusters of type in historical data to together, and by abnormal number
Value removal, history of forming cluster, selects point centered on the center of Historic Clustering；
Cluster diameter is calculated, cluster diameter is equal to the boundary of Historic Clustering to the distance of the central point.
8. device according to claim 6, which is characterized in that the cluster module is also used to:
Abnormal numerical value in the data is rejected.
9. device according to claim 6, which is characterized in that the cluster module is also used to:
The identical data clusters of type in the data are formed into cluster to together using Kmeans algorithm.
10. a kind of computer storage medium, which is characterized in that for being stored as device described in claim 6 to 9 any one
Computer software instructions used.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110334728A (en) * | 2019-05-06 | 2019-10-15 | 中国联合网络通信集团有限公司 | A kind of fault early warning method and device towards industry internet |
| CN110704676A (en) * | 2019-10-10 | 2020-01-17 | 南京凯盛国际工程有限公司 | Dynamic abnormal information video processing system and method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200288A (en) * | 2014-09-18 | 2014-12-10 | 山东大学 | Equipment fault prediction method based on factor-event correlation recognition |
| CN104820691A (en) * | 2015-04-27 | 2015-08-05 | 电子科技大学 | Database design method in traffic flow forecasting and query vector obtaining method thereof |
| CN106383916A (en) * | 2016-11-09 | 2017-02-08 | 北京许继电气有限公司 | Data processing method based on predictive maintenance of industrial equipment |
| CN108304941A (en) * | 2017-12-18 | 2018-07-20 | 中国软件与技术服务股份有限公司 | A kind of failure prediction method based on machine learning |
-
2018
- 2018-12-20 CN CN201811568590.2A patent/CN109670551A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200288A (en) * | 2014-09-18 | 2014-12-10 | 山东大学 | Equipment fault prediction method based on factor-event correlation recognition |
| CN104820691A (en) * | 2015-04-27 | 2015-08-05 | 电子科技大学 | Database design method in traffic flow forecasting and query vector obtaining method thereof |
| CN106383916A (en) * | 2016-11-09 | 2017-02-08 | 北京许继电气有限公司 | Data processing method based on predictive maintenance of industrial equipment |
| CN108304941A (en) * | 2017-12-18 | 2018-07-20 | 中国软件与技术服务股份有限公司 | A kind of failure prediction method based on machine learning |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110334728A (en) * | 2019-05-06 | 2019-10-15 | 中国联合网络通信集团有限公司 | A kind of fault early warning method and device towards industry internet |
| CN110704676A (en) * | 2019-10-10 | 2020-01-17 | 南京凯盛国际工程有限公司 | Dynamic abnormal information video processing system and method |
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