CN109740769A - Equipment fault data analysing method and computer storage medium - Google Patents
Equipment fault data analysing method and computer storage medium Download PDFInfo
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Abstract
The invention discloses a kind of equipment fault data analysing method and computer storage mediums, this method comprises: obtaining the historical failure data of a certain failure cause of equipment, and carry out integrality judgement to each group of historical failure data；Effective judgement is carried out to each group of full failure data；Regression analysis is carried out to all effective full failure data, and obtains corresponding regression parameter；Judge whether the value of each regression parameter is reasonable according to the regression parameter value range table prestored.According to the technique and scheme of the present invention, by considering the validity of equipment fault data and integrity issue and returning etc., realize the fail-safe analysis to the equipment fault reason, reasonability judgement etc. also is carried out to the regression parameter of acquisition simultaneously, the precision of analysis of the failure cause regularity of distribution can be effectively improved, and then improves the fail-safe analysis etc. to equipment and system.
Description
Technical field
The present invention relates to equipment fault technical fields more particularly to a kind of equipment fault data analysing method and computer to deposit
Storage media.
Background technique
There are two types of the common methods for calculating equipment reliability, and one is the method for equipment life analysis, another kind is to use
Equipment fault is counted, the historical failure data of equipment is returned to obtain the parameter of failure cause characteristic.Wherein, every assessment
The failure cause of one equipment needs to find the corresponding maintenance record of this failure cause in Mishap Database, further according to each
The data such as the maintenance whole story time that maintenance record is recorded and maintenance duration are calculated.
But it is lack of standardization due to factory repair record, often such that the fault data that is collected into and not meeting recurrence meter
The demand of calculation, and due to the missing of fault data, the failure cause characterisitic parameter for causing recurrence to come out is inaccurate and causes
Evaluation error, such that the analysis of reliability often loses meaning etc..
Summary of the invention
In view of the above problems, the present invention proposes a kind of equipment fault data analysing method and computer storage medium, passes through
It to the integrality and efficiency analysis of historical failure data, and is returned, can be solved in the prior art using Weibull function
The problems such as obtaining the assessment result of deviation using lack of standardization or incomplete fault data.
The embodiment of the present invention proposes a kind of equipment fault data analysing method, comprising:
The historical failure data of a certain failure cause of equipment is obtained, and integrality is carried out to each group of historical failure data
Judgement；
If being judged as complete, the historical failure data of respective sets is extracted as full failure data, and in all history
When the percentage of head rice of fault data reaches default percentage of head rice threshold value, the full failure data described in each group carry out Effective judgement；
If being judged as effectively, the full failure data of respective sets are extracted as effective full failure data, and all
The effective percentage of full failure data reaches when being preset with efficiency threshold, return point to all effective full failure data
Analysis, and obtain corresponding regression parameter；
Judge whether the value of each regression parameter is reasonable according to the regression parameter value range table prestored, and in institute
When some regression parameters is reasonable, the regression analysis data of the failure cause are exported.
Further, the equipment of the embodiment of the present invention failure data analyzing method, further includes:
If having at least one unreasonable in the regression parameter, notify user with to effective full failure data into
Pedestrian's work investigation, and the regression analysis is re-started when getting the fault data after manually checking.
Further, the equipment of the embodiment of the present invention failure data analyzing method, further includes:
If the percentage of head rice is not up to the default percentage of head rice threshold value, according to the percentage of head rice and the default percentage of head rice threshold
At least one set of incomplete fault data interpolation is effective full failure data by difference between value, so that the percentage of head rice is big
In equal to the default percentage of head rice threshold value.
Further, the equipment of the embodiment of the present invention failure data analyzing method, further includes:
If the effective percentage is not up to described to be preset with efficiency threshold, according to the described efficient and described default effective percentage threshold
At least one set of invalid full failure data interpolation is effective full failure data by difference between value, so that the effective percentage
Efficiency threshold is preset with more than or equal to described.
Further, using the historical failure data of the same fault reason of same equipment, to the incomplete failure
Data or the invalid full failure data carry out the interpolation.
Further, the historical failure data of the same fault reason of the same equipment if it does not exist, then using similar
The historical failure data of the same fault reason of type equipment carries out the interpolation.
Further, the equipment of the embodiment of the present invention failure data analyzing method, further includes:
Get the newly-increased historical failure data of preset quantity, or the newly-increased history event according to prefixed time interval to acquisition
Hinder data and carries out the integrality judgement and the Effective judgement；
When judging the newly-increased historical failure data for effective full failure data, by the newly-increased historical failure data
The regression analysis is carried out with last effective full failure data.
Further, the regression analysis using Two-parameter Weibull Distribution, three-parameter Weibull distribution, normal distribution,
Exponential distribution, logarithm normal distribution are returned.
Further, described " integrality judgement is carried out to each group of historical failure data ", comprising:
Preset keyword section is matched with all fields in each group of historical failure data；
If being successfully matched to all preset keyword sections in the historical failure data, the historical failure number is judged
According to for the full failure data.
Another embodiment of the present invention proposes a kind of standby failure data analyzing device, comprising:
Data integrity judgment module, the historical failure data of a certain failure cause for obtaining equipment, and to each
Group historical failure data carries out integrality judgement；
The historical failure data of respective sets is extracted as complete event if complete for being judged as by Effective judgement module
Hinder data, and when the percentage of head rice of all historical failure datas reaches default percentage of head rice threshold value, the full failure described in each group
Data carry out Effective judgement；
Regression analysis module, if being extracted as the full failure data of respective sets effectively complete for being judged as effectively
Fault data, and when the effective percentage of all full failure data reaches and is preset with efficiency threshold, to all effective complete events
Hinder data and carry out regression analysis, and obtains corresponding regression parameter；
Rational Parameters judgment module, for judging each recurrence ginseng according to the regression parameter value range table prestored
Whether several values is reasonable, and when all regression parameters are reasonable, exports the regression analysis data of the failure cause.
One more embodiment of the present invention proposes a kind of computer storage medium, is stored with computer program, in the calculating
Machine program is performed above-mentioned equipment fault data analysing method.
The technical solution of the embodiment of the present invention passes through the validity for considering equipment fault data and integrity issue and uses
Weibull Function etc. is returned, and more accurate regression parameter can be obtained, and is distributed to effectively improve to failure cause
The precision of analysis of rule, and then improve the fail-safe analysis etc. to equipment and system.
Detailed description of the invention
In order to illustrate more clearly of technical solution of the present invention, letter will be made to attached drawing needed in the embodiment below
It singly introduces, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as to the present invention
The restriction of protection scope.
Fig. 1 is the flow diagram of the equipment fault data analysing method of the embodiment of the present invention 1；
Fig. 2 is the first structure diagram of the equipment fault data analysis set-up of the embodiment of the present invention 2；
Fig. 3 is the second structural schematic diagram of the equipment fault data analysis set-up of the embodiment of the present invention 2.
Main element symbol description:
100- equipment fault data analysis set-up；10- data integrity judgment module；20- Effective judgement module；30-
Regression analysis module；40- Rational Parameters judgment module；50- data interpolation module.
Specific embodiment
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, is not intended to limit claimed invention to the detailed description of the embodiment of the present invention provided in the accompanying drawings below
Range, but it is merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include one or more of the features.In the description of the present invention, the meaning of " plurality " is two or more,
Unless otherwise specifically defined.
Unless otherwise defined, all technical and scientific terms used herein and belong to technical field of the invention
The normally understood meaning of technical staff is identical.Used term is intended merely to describe specifically to implement in the description herein
The purpose of example, it is not intended that the limitation present invention.Term " and or " used herein includes one or more relevant institute's lists
Any and all combinations of purpose.
Below with reference to specific embodiment, the present invention is described in detail.
Embodiment 1
Fig. 1 is please referred to, the present embodiment provides a kind of equipment fault data analysing methods, can be applied in equipment management system
Equipment dependability analysis etc..By being analyzed using the failure cause regularity of distribution of the equipment fault data to equipment, in turn
The reliability of the equipment can be assessed etc..The equipment fault data analysing method is described in detail below.
Step S100 obtains the historical failure data of a certain failure cause of equipment.
In the present embodiment, the historical failure data may include all fault datas since the putting equipment in service.It can lead to
It crosses and failure cause screening is carried out to all historical failure datas of the equipment, to obtain the historical failure data of the failure cause.
It should include multiple fields and corresponding field generally for the fault data of the concrete condition for recording equipment failure
Content.
Step S200 carries out integrality judgement to each group of historical failure data, and judges all historical failure datas
Percentage of head rice whether reach default percentage of head rice threshold value.
May be not necessarily all complete effective in view of the breakdown maintenance data in reality, recorded each time situations such as,
This will cause biggish deviation for the failure cause analysis of distribution to the equipment.It, can be first to each group in the present embodiment
Historical failure data carries out integrality judgement, and using complete fault data for analyzing, to guarantee fail-safe analysis
Accuracy etc..
It, can be by presetting some critical fielies, and by these preset critical fielies and each group of history in the present embodiment
All fields in fault data are matched.If being successfully matched to all preset keywords in the historical failure data
Section then can determine whether that the historical failure data is full failure data.
By taking the historical failure data table of a certain equipment as an example, as shown in table 1, if preset keyword section include device name,
Time started, maintenance end time, maintenance title, downtime, fault mode and failure cause are repaired, is then passed through to the group
Fault data carries out the matching of above-mentioned critical field, due to including above-mentioned all fields in this group of fault data, therefore can determine whether
This group of fault data is a full failure data out.
Table 1
It is appreciated that these preset keyword sections may include but be not limited to include device name, maintenance time started, maintenance
End time, maintenance title, downtime, fault mode and failure cause etc., can specifically be set according to actual needs.
Then, the matching of preset keyword section is carried out to judge each group by all groups of data to the failure cause
Whether fault data is complete.If being judged as complete, the historical failure data of respective sets is extracted as full failure data, and it is remaining
Under then be imperfect fault data.
In the present embodiment, which refers to the ratio of the complete total historical failure data of fault data Zhan.
Exemplarily, if getting 100 groups of historical failure datas, and the percentage of head rice threshold value is 90%, then should have at least 90 groups to go through
When history fault data is all complete, then these complete fault datas are subjected to efficiency analysis.It can prevent because of fault data in this way
Cause regression analysis that there may be relatively large deviation etc. very little.
In above-mentioned steps S200, if the percentage of head rice of all historical failure datas reaches default percentage of head rice threshold value, to mentioning
All full failure data taken out carry out efficiency analysis, i.e. execution step S300.
Step S300 judges whether each group full failure data are effective, and judges having for all full failure data
Whether efficiency, which reaches, is preset with efficiency threshold.
, will be by whether effectively being carried out to the corresponding content of these critical fielies when judging validity in the present embodiment
Judgement.Exemplarily, can be by whether being empty, unreasonable or not with the presence or absence of logic to the corresponding content detection of these fields
The modes such as matching exclude all invalid failures data, and remaining is then effective full failure data.
Multiple groups full failure data instance with a certain equipment got, as shown in table 2, in the 5th group of fault data
Fault mode and failure cause are sky, then can determine whether that this group of fault data is invalid data.Certainly, the unreasonable master of the logic
Refer to and judge whether each group of fault data has actual physical meaning, for example, being opened if maintenance dwell time is less than maintenance
Begin the time, then can determine whether as invalid data etc..Or such as the 1st group of fault data, it can be by pre-establishing fault mode column
Table etc., then to the fault mode and failure cause progress fields match in this group of fault data, the failure established due to system
Content corresponding to mode can't have the fault mode of " D is repaired ", therefore can determine whether that this group of fault data is invalid data.
Table 2
In the present embodiment, this is preset with efficiency threshold and refers to that effective and complete fault data accounts for all full failure numbers
According to ratio.Exemplarily, if getting 90 groups of full failure data, and it is 95% that this, which is preset with efficiency threshold, then should have
When at least 86 groups of full failure data are all effective, then these effective and complete fault datas are subjected to regression analysis.Equally,
It can guarantee a certain amount of fault data sample in this way, so that the result of regression analysis is more accurate etc..
In above-mentioned steps S300, if the effective percentage of all full failure data reaches when being preset with efficiency threshold,
Execute step S400.
Step S400 carries out regression analysis to all effective full failure data, and obtains corresponding regression parameter.
Preferably, which can be used Two-parameter Weibull Distribution, three-parameter Weibull distribution, normal distribution, refers to
Number distribution, logarithm normal distribution etc. are fitted recurrence.Exemplarily, it is returned according to Two-parameter Weibull Distribution,
And two regression parameters of the failure cause can be obtained, i.e., it is characteristic parameter and form parameter respectively.It can using the two parameters
The analysis such as rate of breakdown and failure phase is carried out to the failure cause, and then the reliability etc. of the equipment can be evaluated.
Step S500 judges whether the value of each regression parameter closes according to the regression parameter value range table prestored
Reason.
Recurrence for the regression parameter of above-mentioned acquisition, using pre-stored regression parameter value range table to acquisition
Parameter carries out reasonability judgement.Exemplarily, if obtaining is Weibull parameter, which can
For Weibull parameter area table typical in mechanical part industry.It is of course also possible to use experience formed in the sector takes
Value range carries out the rational judgement.
Then, if the regression parameter obtained can determine whether in corresponding regression parameter value range table to be reasonable, and
Execute step S600.If there is at least one unreasonable, illustrating the regression data, there are deviations, optionally, will further perform
Step S900.
Step S600 exports the regression analysis data of the failure cause.
Step S900 notifies user to described effectively complete if having at least one unreasonable in the regression parameter
Fault data is manually checked.
If being judged as unreasonable, it can notify the user that and manually checked, with historical failure effective and complete to these
Data are further verified.After artificial investigation, then the data after investigation are re-started into above-mentioned recurrence, and exported corresponding
Far.
In the failure data analyzing method, still optionally further, for above-mentioned steps S200, if percentage of head rice is not up to pre-
If percentage of head rice threshold value, then data interpolation can also be carried out to incomplete fault data, i.e. execution step S700.
At least one set of incomplete fault data interpolation is effective full failure data by step S700.
It exemplarily, can will be at least one set of endless according to the difference between the percentage of head rice and the default percentage of head rice threshold value
Whole fault data interpolation is effective full failure data, so that the percentage of head rice is more than or equal to the default percentage of head rice threshold value.
For example, if there is 100 groups of fault datas, wherein default percentage of head rice threshold value is 90%, and the percentage of head rice of current historical failure data is
85%, then 5% incomplete fault data is at least subjected to data interpolation.
Still optionally further, for above-mentioned steps S300, if the effective percentage is not up to described to be preset with efficiency threshold,
Data interpolation can also be carried out to invalid full failure data, i.e. execution step S800.
At least one set of invalid full failure data interpolation is effective full failure data by step S800.
It exemplarily, can will be at least one set of invalid according to the described efficient and described difference being preset between efficiency threshold
Full failure data interpolation be effective full failure data efficient be more than or equal to the default efficient threshold so that described
Value.
In above-mentioned steps S700 and step S800, the interpolation refers to the word by incomplete fault data as required
Section or the corresponding content of field are supplemented or are corrected, so that it meets the needs of integrality or validity.
Preferably, using the historical failure data of the same fault reason of same equipment, to these incomplete failures
Data or invalid full failure data carry out the interpolation.Exemplarily, if having the downtime or maintenance of one group of data
Between it is incorrect or unreasonable, occur using the last time of the equipment downtime in the fault data of same fault reason or
Maintenance time is filled or corrects.
Certainly, the historical failure data of the same fault reason of the same equipment if it does not exist, it is further, available
The historical failure data of the same fault reason of same type equipment carries out the interpolation.Exemplarily, if the equipment is fore pump,
The then imperfect fault data using the fault data of another fore pump with phase same-action to the equipment or invalid event
Barrier data are filled or correct.
In the present embodiment, when carrying out data interpolation, can also according to the similarity of each field recorded in fault data come
Selection is for the same equipment of interpolation or the historical failure data of same type equipment.Wherein, the similarity meter of above-mentioned each field
Euclidean distance, manhatton distance, Chebyshev can be used apart from scheduling algorithm in calculation.Exemplarily, the 5th group of failure as shown in Table 2
Data can such as be tieed up when carrying out interpolation by the way that the corresponding content of other fields of this group of fault data is carried out similarity mode
The corresponding content of fields such as title and maintenance description is repaired, then by the fault mode in highest that group of fault data of similarity
Content replaces " D grades maintenance " in the invalid fault data, to realize data interpolation.
Still optionally further, which may also include that the newly-increased history for getting preset quantity
Fault data, or sentence according to the integrality that newly-increased historical failure data of the prefixed time interval to acquisition executes above-mentioned steps S200
Disconnected and step S300 Effective judgement.
Exemplarily, if judge the newly-increased historical failure data for effective full failure data, described increase newly is gone through
History fault data and last effective full failure data carry out the regression analysis, i.e. execution step S400, so as to
To new regression analysis data, and the rate of breakdown that the failure cause can be evaluated according to the new regression analysis data and set
Standby reliability etc..
The equipment fault data analysing method of the present embodiment passes through the validity and integrality progress to equipment fault data
First judge, with guarantee the fault data for regression analysis be it is complete and effective, then using Weibull Function etc.
It carries out regression analysis and carries out the reasonability judgement of parameter, so as to obtain more accurate regression parameter, not only may be implemented
Fail-safe analysis to the failure cause of the equipment can also effectively improve the precision of analysis to the failure cause regularity of distribution.
In addition, utilizing same equipment or same category of device to the fault data for being unsatisfactory for requiring also by introducing data interpolation mode
Fault data under same fault cause field carries out interpolation, to guarantee to be used for the data sample amount etc. of regression analysis, so as to
More accurate Regression Analysis Result is further obtained, and then improves the fail-safe analysis etc. to equipment and system.
Embodiment 2
Referring to figure 2., 1 equipment fault data analysing method based on the above embodiment, the present embodiment provides a kind of equipment
Failure data analyzing device 100 can be applied to the fail-safe analysis etc. of equipment.In the present embodiment, equipment fault data analysis
Device 100 includes:
Data integrity judgment module 10, the historical failure data of a certain failure cause for obtaining equipment, and to every
One group of historical failure data carries out integrality judgement；
If the historical failure data of respective sets is extracted as completely by Effective judgement module 20 complete for being judged as
Fault data, and when the percentage of head rice of all historical failure datas reaches default percentage of head rice threshold value, the complete event described in each group
Hinder data and carries out Effective judgement；
Regression analysis module 30, if being extracted as the full failure data of respective sets effectively complete for being judged as effectively
Whole fault data, and when the effective percentage of all full failure data reaches and is preset with efficiency threshold, to all effective complete
Fault data carries out regression analysis, and obtains corresponding regression parameter；
Rational Parameters judgment module 40, for judging each recurrence according to the regression parameter value range table prestored
Whether the value of parameter is reasonable, and when all regression parameters are reasonable, exports the regression analysis data of the failure cause.
Further, as shown in figure 3, the equipment fault data analysis set-up 100 further includes data interpolation module 50.
In the present embodiment, if the data interpolation module 50 is not up to the default percentage of head rice threshold value for the percentage of head rice,
It is by least one set of incomplete fault data interpolation according to the difference between the percentage of head rice and the default percentage of head rice threshold value
Effective full failure data, so that the percentage of head rice is more than or equal to the default percentage of head rice threshold value.
If the data interpolation module 50 is also used to, the effective percentage is not up to described to be preset with efficiency threshold, is had according to described
At least one set of invalid full failure data interpolation is effectively complete by efficiency and the difference being preset between efficiency threshold
Fault data, so that the effective percentage is preset with efficiency threshold more than or equal to described.
Above-mentioned equipment fault data analysis set-up 100 corresponds to the equipment fault data analysing method of embodiment 1.Implement
Any option in example 1 is also applied for the present embodiment, and I will not elaborate.
The present invention also provides a kind of terminal, which may include computer, server etc..The terminal includes memory
And processor, memory can be used for storing computer program, processor is by running the computer program, to make the terminal
Execute the function of above equipment failure data analyzing method or the modules in above equipment failure data analyzing device.
Memory may include storing program area and storage data area, wherein storing program area can storage program area, at least
Application program needed for one function；Storage data area, which can be stored, uses created data (such as sound according to mobile terminal
Frequency evidence, phone directory etc.) etc..In addition, memory may include high-speed random access memory, it can also include non-volatile deposit
Reservoir, for example, at least a disk memory, flush memory device or other volatile solid-state parts.
The present invention also provides a kind of computer storage mediums, for storing the computer journey used in above-mentioned terminal
Sequence.
In several embodiments provided herein, it should be understood that disclosed device and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, flow chart and structure in attached drawing
Figure shows the system frame in the cards of the device of multiple embodiments according to the present invention, method and computer program product
Structure, function and operation.In this regard, each box in flowchart or block diagram can represent a module, section or code
A part, a part of the module, section or code includes one or more for implementing the specified logical function
Executable instruction.
It should also be noted that function marked in the box can also be attached to be different from the implementation as replacement
The sequence marked in figure occurs.For example, two continuous boxes can actually be basically executed in parallel, they sometimes may be used
To execute in the opposite order, this depends on the function involved.It is also noted that every in structure chart and/or flow chart
The combination of a box and the box in structure chart and/or flow chart, can function or movement as defined in executing it is dedicated
Hardware based system is realized, or can be realized using a combination of dedicated hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present invention can integrate one independence of formation together
Part, be also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
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 intelligence
Can mobile phone, personal computer, server or network equipment etc.) execute each embodiment the method for the present invention whole or
Part steps.And storage medium above-mentioned include: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory),
Random access memory (RAM, Random Access Memory), magnetic or disk etc. be various to can store program code
Medium.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.
Claims (10)
1. a kind of equipment fault data analysing method characterized by comprising
The historical failure data of a certain failure cause of equipment is obtained, and integrality is carried out to each group of historical failure data and is sentenced
It is disconnected；
If being judged as complete, the historical failure data of respective sets is extracted as full failure data, and in all historical failures
When the percentage of head rice of data reaches default percentage of head rice threshold value, the full failure data described in each group carry out Effective judgement；
If being judged as effectively, the full failure data of respective sets are extracted as effective full failure data, and all complete
The effective percentage of fault data reaches when being preset with efficiency threshold, carries out regression analysis to all effective full failure data, and
Obtain corresponding regression parameter；
Judge whether the value of each regression parameter is reasonable according to the regression parameter value range table prestored, and all
When regression parameter is reasonable, the regression analysis data of the failure cause are exported.
2. equipment fault data analysing method according to claim 1, which is characterized in that further include:
If having at least one unreasonable in the regression parameter, user is notified to carry out people to effective full failure data
Work investigation, and the regression analysis is re-started when getting the fault data after manually checking.
3. equipment fault data analysing method according to claim 1, which is characterized in that further include:
If the percentage of head rice is not up to the default percentage of head rice threshold value, according to the percentage of head rice and the default percentage of head rice threshold value it
Between difference by least one set of incomplete fault data interpolation be effective full failure data so that the percentage of head rice is greater than etc.
In the default percentage of head rice threshold value.
4. equipment fault data analysing method according to claim 1, which is characterized in that further include:
If it is described it is efficient it is not up to described be preset with efficiency threshold, according to it is described efficient and it is described be preset with efficiency threshold it
Between difference by least one set of invalid full failure data interpolation be effective full failure data so that the effective percentage is greater than
Efficiency threshold is preset with equal to described.
5. equipment fault data analysing method according to claim 3 or 4, which is characterized in that utilize the phase of same equipment
With the historical failure data of failure cause, institute is carried out to the incomplete fault data or the invalid full failure data
State interpolation.
6. equipment fault data analysing method according to claim 5, which is characterized in that the same equipment if it does not exist
Same fault reason historical failure data, then utilize the same fault reason of same type equipment historical failure data carry out
The interpolation.
7. equipment fault data analysing method according to claim 1, which is characterized in that further include:
The newly-increased historical failure data of preset quantity is got, or according to prefixed time interval to the newly-increased historical failure number of acquisition
Judge and the Effective judgement according to the integrality is carried out；
When judging the newly-increased historical failure data for effective full failure data, by the newly-increased historical failure data with it is upper
Primary effective full failure data carry out the regression analysis.
8. equipment fault data analysing method according to claim 1, which is characterized in that the regression analysis is using double ginsengs
Number Weibull distribution, three-parameter Weibull distribution, normal distribution, exponential distribution or logarithm normal distribution are returned.
9. equipment fault data analysing method according to claim 1, which is characterized in that described " to each group of history event
Hinder data and carry out integrality judgement ", comprising:
Preset keyword section is matched with all fields in each group of historical failure data；
If being successfully matched to all preset keyword sections in the historical failure data, judge that the historical failure data is
The full failure data.
10. a kind of computer storage medium, which is characterized in that be stored with computer program, be performed in the computer program
When implement such as the described in any item equipment fault data analysing methods of claim 1-9.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116090911A (en) * | 2023-04-11 | 2023-05-09 | 西南科技大学 | Equipment fault analysis method, device and system based on multi-core clustering |
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