CN107084853A - The lower equipment failure prediction method of cloud manufacture - Google Patents
The lower equipment failure prediction method of cloud manufacture Download PDFInfo
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- CN107084853A CN107084853A CN201710127260.9A CN201710127260A CN107084853A CN 107084853 A CN107084853 A CN 107084853A CN 201710127260 A CN201710127260 A CN 201710127260A CN 107084853 A CN107084853 A CN 107084853A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
- G01M99/005—Testing of complete machines, e.g. washing-machines or mobile phones
Abstract
The invention discloses a kind of lower equipment failure prediction method of cloud manufacture.The operating procedure of this method is：First, structure and working principle is constituted according to equipment and sets up the DTC table of comparisons；2nd, fault data model is set up；3rd, association rule mining.Using Socket（The exchange of data is realized by a two-way communication connection, one end of this connection）Transmission means, using intermediate equipment different vendor, the fault data and anomaly parameter of distinct device are integrated into database, using HDFS（Hadoop distributed file systems）Distributed storage, solves big data processing storage problem；Programmed using Java（One Object-Oriented Programming Language）Realize the improvement Apriori algorithm (the frequent item set algorithms of Mining Association Rules) that Map/Reduce (parallel computational model) changes, and add sequential concept rule digging is associated to equipment fault feature, the fault trend of pre- measurement equipment, reaches the purpose of equipment fault prediction.The present invention gives concrete implementation mode using cutting machine as example.
Description
Technical field
The invention discloses a kind of lower equipment failure prediction method of cloud manufacture, belong to equipment fault prediction field.
Background technology
It is the complication of equipment in manufacturing industry, comprehensive with developing rapidly for modern science and technology industrial technology especially information technology
Combination, intelligence degree are improved constantly.Along with the development of equipment, the cost that it is developed, production is especially safeguarded and ensured is got over
Come higher.Simultaneously, because the increase of integral link and influence factor, break down and the probability of disabler is gradually increased, because
How this, give warning in advance to equipment fault, the part or factor that may be broken down are controlled in advance i.e. failure predication by
Gradually turn into researcher's focus of attention.On equipment fault prediction, forefathers have carried out many researchs, have been broadly divided into three classes：It is based on
The failure predication research of physics Time-effect Model, the Zeng Shengkui of such as BJ University of Aeronautics ＆ Astronautics et al. proposes fault diagnosis and fault prediction
People-machine-environment complete cognitive model, the performance requirement of failure predication technology is analyzed；Based on data-driven
Equipment fault forecasting research, such as Tsing-Hua University professor Jiang Dongxiang et al. proposed a kind of integrated intelligent algorithm, this hybrid algorithm
It is combined into by Fuzzy BP network, fuzzy control and expert system；Failure predication research based on reliability theory, such as Tsing-Hua University is big
The multi-functional fault trees of MFFTAAP of nuclear energy technology Research Institute are learned, and then analyze the Failure Distribution of equipment and are carried out in advance
Survey.Existing Predicting Technique has been achieved for larger progress in terms of theoretical research and practical application, still, and there is also many
Deficiency, dependence of the degree of accuracy predicted the outcome to model is larger, lacks self-learning capability, for complex device, to set up one
The high static models of the degree of accuracy are also accurately extremely difficult ensure to predict the outcome.
With the operation of equipment, enterprise possesses a large amount of available equipment condition monitorings and historical data, with business system
Increase, explosion type is presented in the growths of data, and daily data volume can reach tens TB, how seem unrelated number using these
According to being also the focus studied instantly.Forefathers attempt to utilize data mining algorithm, carry out fault correlation rule research, such as Ogilvie
T and Swidenbank E et al. use Apriori algorithm Mining Association Rules in existing power plant's Basis of Database, establish
Equipment state model, carries out Condition Prediction of Equipment.Also there is the research of many parallelizations on correlation rule, such as China Mobile grinds
Study carefully institute and parallel data mining software BC-PDM have developed using cloud computing platform Hadoop, realize the reliable memory of mass data
Excavated with efficient, but combine the two by the way that to equipment fault signature analysis, operation parallelization thought excavates the association between failure
The research that rule carries out failure predication does not have also.
Inventor in this context, passes through the research to association rule algorithm under cloud computing, bonding apparatus
The signature analysis of failure, the method for proposing a kind of lower equipment failure predication of cloud manufacture is exactly that the result of slave unit failure is set out, led to
The improvement to association rules mining algorithm is crossed, with the technology of parallel processing, and the concept of sequential is added, is associated regular pre-
Survey, and analyzed by example of cutting machine, realize that equipment fault is predicted.
The content of the invention
It is an object of the invention to equipment failure prediction method under the not enough manufacture there is provided a kind of cloud for prior art,
Suitable for data volume is huge, the extensive technical grade failure predication of device distribution, reach time that Accurate Prediction failure occurs and therefore
Hinder feature.To reach above-mentioned purpose, idea of the invention is that：Utilize the Hadoop (distributions developed by Apache funds club
System infrastructure) cloud computing parallelization improves traditional Apriori algorithm and realizes failure predication, and research focuses on analysis
Map/Reduce (the parallel computation moulds of equipment fault feature and Apriori algorithm (the frequent item set algorithms of Mining Association Rules)
Type) to realize, fault signature, parameter lookup table are set up in the composition structure of slave unit, principle Analysis equipment fault first, are led to
Cross data acquisition technology to obtain the machining information and fault message of equipment, then realized using parallelization Map/Reduce thoughts
Improved Apriori algorithm, and sequential concept is added, a kind of failure predication based on affair character is realized, finally with cutting machine
For example, concrete implementation process is provided.
Conceived according to foregoing invention, the technical solution adopted by the present invention is：
A kind of lower equipment failure prediction method of cloud manufacture；Comprise the following steps：
Step one：The composition structure and working principle of analytical equipment, sets up the fault signature code table of comparisons.
Step 2：Set up fault data temporal model.Data in event of failure record sheet are carried out according to device numbering
Packet, and time of failure is recorded, invalid data are rejected, timing failure data model is obtained.Defining operation symbol " # ", table
Up to formula # ((I), T), if T is the maximum in orderly item, output 1, on the contrary it is 0, timing planning additive operation (A, Ta)-(B,
Tb)=A->B, so as to calculate A failures to the interval time of B failures.As shown in Figure 2.
Step 3：Association rule mining.Hadoop (the distributed system architecture developed by Apache funds club)
Under to improving Apriori algorithm (the frequent item set algorithms of Mining Association Rules), to carry out Map/Reduce (parallel computational model) real
It is existing.Using distributed mode, mining algorithm is distributed on multiple nodes and stored, realize highly-parallel, excavate event
Hinder the incidence relation between feature, realize failure predication.As shown in Figure 3.
Compared with prior art, the advantages of the present invention are：
Improved Apiori algorithms are applied in equipment fault prediction, and employ cloud computing technology, by finding out
The incidence relation seemed between unrelated equipment fault and anomaly parameter carries out failure predication, by experimental result and it is actual therefore
The situation that barrier occurs is compared, and discovery possesses very strong practicality, greatly reduces economic loss caused by equipment fault, plus
Enter after Map/Reduce parallelization treatment technologies, predetermined speed is also greatly improved.This other algorithm adds sequential concept,
On the basis of improved Apriori algorithm, add after sequential concept, the potential rule between being out of order can not only be excavated, moreover it is possible to
An accurate maintenance time time limit is provided for decision-making, with high practical value.
Brief description of the drawings
Fig. 1 is the main program block diagram of the present invention.
Fig. 2 is the fault data model in the step two of the present invention.
Fig. 3 is the algorithm calculation flow chart in the step three of the present invention.
Fig. 4 is the cutting machine composition structure chart in embodiments of the invention step one.
Embodiment
The present invention is described in detail with preferred embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, give detailed embodiment and specific operating process, but protection scope of the present invention is not limited to
Following embodiments.
Embodiment (by taking cutting equipment as an example)
1. the lower equipment failure prediction method of cloud manufacture.Comprise the following steps：
Step one：Analyze the composition structure and working principle of glass cutting machine.Glass cutting machine by cutting table, cutting bridge with
And the part of computer control cabinet three composition, it is illustrated in fig. 4 shown below, mainly includes：1 represents wool felt pad.2 represent crossbeam.3 represent
Horizontal drive rail.4 represent cutter head.5 represent longitudinal drive rail.6 represent motor.7 represent conveyer belt.8 represent laterally
Feed transmission system.9 represent cable crawler belt.10 represent long feed transmission system.
Step 2：Set up the fault signature code table of comparisons of cutting machine.
The present invention takes Hadoop distributed file system HDFS, is combined with Map/Reduce, the handling capacity of data access
Higher, HDSF application program uses file " write-once is repeatedly read " pattern.First to being cut in certain glass processing enterprise
The failure data analyzing of cutting mill composition and working principle and record, it is whole to causing cutting machine failure and failure performance to carry out
Reason, presses corresponding failure representation by abnormal data, sets up the failure table of comparisons as shown in the table.
| Record failure | DTC | Fault parameter is showed | DTC |
| Engine failure | F1 | Engine speed exceedes the upper limit or less than lower limit | F10 |
| Coolant temperature is abnormal | F2 | Coolant temperature exceedes the upper limit or less than lower limit | F11 |
| Gear distress | F3 | Cutting speed exceedes the upper limit or less than lower limit | F12 |
| Control card failure | F4 | Cutting pressure exceedes the upper limit or less than lower limit | F13 |
| Upper piece machine failure | F5 | Air pressure exceedes the upper limit or less than lower limit | F14 |
| Linearity deviation fault | F6 | Scribe-wheel angle exceedes the upper limit or less than lower limit | F15 |
| Glass part failure during cutting | F7 | Cutting pressure exceedes the upper limit | F16 |
| Hydraulic system fault | F8 | Hydraulic pressure exceedes the upper limit or less than lower limit | F13 |
| Break piece failure | F9 | Break the piece time too early or excessively slow | F17 |
Step 3：Set up fault data model.Data in event of failure record sheet are grouped according to device numbering,
And time of failure is recorded, invalid data are rejected, timing failure data model is obtained.Defining operation symbol " # ", expression formula #
((I), T), wherein I represents fault signature, and T represents time of failure, and form is timestamp.If T is the maximum in orderly item
Value, output 1 (table this moment failure occur), on the contrary it is 0 (failure does not occur), timing planning additive operation (A, Ta)-(B, Tb)=
A—>The time that B and two failure occurs makees poor, so as to calculate A failures to the interval time △ T of B failures.
Following table is the part timing failure data after certain glass processing business processes, and wherein device numbering is same type of
Equipment, the unit of sequential is day.
Step 4：Association rule mining.(dug by the way of Map/Reduce to improving Apriori algorithm under Hadoop
Dig the frequent item set algorithm of correlation rule) realized, algorithm is distributed on multiple nodes and stored, highly-parallel is realized
Change.
Realize that Apriori sequential closes algorithm key step with Map/Reduce parallelization thoughts as follows：If minimum is supported
It is 4, i.e. min_sup=0.2, M=5 (database is divided into 5 pieces), R=1 (1 Reduce task of distribution) to spend threshold value.Frequent Set
Generating process is as shown below.It is assumed that min_conf=75%
(1) result of generation is output in file using HDFS mode, every a line to text is used as one
Cutting.Formed<key1,value1>, key1 represents capable offset, and value1 represents one of frequent episode, the scanning of Map functions
Key value pair, calls generating function, output<Frequent episode, rule>Form.
<Frequent episode, rule>It is expressed as<(Fi,Fj,Fk),”Support, confidence level, △ T ">, Fi, Fj, Fk
Specific DTC is represented, △ T are obtained with temporal mode additive operation.Such as：
When give up.
The output result obtained in this example by Map functions is：<(F1,F2,F4),”Support 0.2,
Confidence level 67%, △ T1 ">；<(F1,F2,F4),”Support 0.2, confidence level 67%, △ T2 ">；<(F1,
F2,F4),”Support 0.2, confidence level 100%, △ T3 ">.
Reduce is by stipulations result：It can be described as:It is such
Equipment is that F2 and F4 occurs simultaneously in DTC, the exception for yard F1 that can be broken down in the △ T3 unit interval afterwards, finally
HDFS (Hadoop distributed file systems) is arrived into output result storage.
The step of according to step 4, the rule generation for being met condition by Map functions is as follows：
It can be described by Reduce stipulations result, exampleThis type
Cutting equipment in DTC be that F10, F12, F13 occur, the exception for yard F1 that can break down in the 14 day time afterwards, most
HDFS is arrived in output result storage at last.
Claims (4)
1. the lower equipment failure prediction method of cloud manufacture；It is characterized in that concrete operation step is as follows：
（1）, set up equipment fault code the table of comparisons：The composition structure and working principle of analytical equipment, sets up the failure control of equipment
Table：The foundation of the failure table of comparisons need to include typical fault feature, and failure performance causes environmental characteristic, the parameter attribute of failure；
（2）, set up fault data model：Data in event of failure record sheet are grouped according to device numbering, and recorded
Time of failure, rejects invalid data, obtains timing failure data model；Defining operation symbol " # ", expression formula # ((I),
T), if T is the maximum in orderly item, output 1 occurs for I table fault occurrences, otherwise be 0, timing planning additive operation
（A, Ta）-（B, Tb）=A—>B, so as to calculate A failures to the interval time of B failures；
（3）, association rule mining：Utilize step（2）In fault data model, in Hadoop --- by Apache funds club
To improving Apriori algorithm --- the frequent item set algorithm of Mining Association Rules under the distributed system architecture of exploitation, enter
Row Map/Reduce parallelizations；Mainly include：Fault data uses distributed mode, and mining algorithm is distributed in into multiple nodes
It is upper to be stored, highly-parallel is realized, Map functions are called, one group of key-value pair one group of new key-value pair is mapped to, to each
Node carries out the calculating of frequent item set, obtains frequent three item collection, calls Reduce functions, it is ensured that in the key-value pair of all mappings
Each shared identical key group, merges to the result that each node is calculated, finally excavates the rule between fault signature
Then.
2. the lower equipment failure prediction method of cloud manufacture according to claim 1, it is characterised in that the step（1）Foundation is set
It is for the DTC table of comparisons：According to the composition structure of particular device, operation principle, environmental factor, the relevant device dimension in enterprise
Shield personnel provide can comprehensively cover the feature and parameter of equipment fault as far as possible, set up the DTC control of feature and parameter
Table.
3. the lower equipment failure prediction method of cloud manufacture according to claim 1, it is characterised in that the step（2）Set up event
Hindering data model is：Defining each things has unique device numbering to identify, the event of all data item corresponding devices in affairs
Hinder data；In the absence of two affairs of identical device numbering；Data item in affairs all represents by one two tuple ＜ i, t ＞,
Referred to as sequential, wherein, i is failure, i.e. i represents failure, and t is the timestamp of sequential.
4. the lower equipment failure prediction method of cloud manufacture according to claim 1, it is characterised in that the step（3）Association rule
Then excavating is：Include under Hadoop to improving the step of Apriori algorithm carries out Map/Reduce parallelizations：A. will entirely it count
Burst is carried out according to storehouse：Transaction database is carried out horizontal segmentation, transaction database D is divided horizontally into n by Map/Reduce
The suitable data block of scale, this process is that InputFormat is completed, and data block is divided into InputSplit, then sent
To m node, start to perform Map tasks；B. local data block is transformed into corresponding matrix：The data of input are sent out
It is sent to above different nodes, matrix is transformed into successively；Run-down local data bank, obtains a frequent item collection and obtains
Local frequently matrix；C. matrix is compressed：It is compressed including row, deletes those and can not be attached with adjacent item collection
Computing the corresponding row vector of item collection, row be compressed；D. the condensation matrix above each node is converted into local
Frequent item set；E. those are used the key-value pair for having identical key<' item collection I in k project ', ' local support '>Merge,
Obtain the global support of k project in item collection I；If meeting minimum support, then it is exactly a frequent item set；These
Frequent item set finally constitutes the frequent item set of the overall situation；F. the confidence level per rule is calculated, output meets the pass imposed a condition
Connection rule, and exported according to the form provided in step e to HDFS --- Hadoop distributed file systems.
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