CN105718620A - Modeling method for reliability statistic analysis of key component of converter valve cooling system - Google Patents
Modeling method for reliability statistic analysis of key component of converter valve cooling system Download PDFInfo
- Publication number
- CN105718620A CN105718620A CN201410738589.5A CN201410738589A CN105718620A CN 105718620 A CN105718620 A CN 105718620A CN 201410738589 A CN201410738589 A CN 201410738589A CN 105718620 A CN105718620 A CN 105718620A
- Authority
- CN
- China
- Prior art keywords
- parameter
- model
- key components
- weibull
- reliability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001816 cooling Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004458 analytical method Methods 0.000 title claims abstract description 29
- 238000005315 distribution function Methods 0.000 claims abstract description 32
- 238000012360 testing method Methods 0.000 claims abstract description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 33
- 238000011160 research Methods 0.000 claims description 20
- 238000007689 inspection Methods 0.000 claims description 13
- 230000001174 ascending effect Effects 0.000 claims description 3
- 238000001276 Kolmogorov–Smirnov test Methods 0.000 abstract 1
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 10
- 230000005540 biological transmission Effects 0.000 description 8
- 238000012423 maintenance Methods 0.000 description 7
- 238000007619 statistical method Methods 0.000 description 6
- 230000001186 cumulative effect Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 5
- 238000010438 heat treatment Methods 0.000 description 5
- 229910052757 nitrogen Inorganic materials 0.000 description 5
- 238000007872 degassing Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 238000007599 discharging Methods 0.000 description 3
- 230000003020 moisturizing effect Effects 0.000 description 3
- 239000004576 sand Substances 0.000 description 3
- 239000007921 spray Substances 0.000 description 3
- 230000005520 electrodynamics Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000009661 fatigue test Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 230000035882 stress Effects 0.000 description 1
Landscapes
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention relates to a modeling method for reliability statistic analysis of key components of a converter valve cooling system. Invalid time data of the key components is obtained; after the statistic analysis is carried out, an experience distribution function is calculated; through taking the experience distribution function as a fitting standard, a fitting distribution function under a Weibull distribution mode is obtained through the data; through adoption of a K-S test method, goodness of fit test is carried out on the fitting function; whether the fitting function and the fitting standard are in a deviation range or not is judged; model parameters in the Weibull distribution mode are recorded, thus obtaining the model base of the invalid time and the model parameters; finally, the reliability analysis model of the key components under Weibull distribution is obtained; and reliability assessment and reliability prediction are carried out on the key components through the determined model parameters. The reliability conditions of the key components can be predicted accurately; scientific and rational suggestions for overhauling the whole system are proposed; and the reliability of the whole system can be improved.
Description
Technical field
The present invention relates to the modeling method of a kind of direct-current transmission converter valve, in particular to the modeling method of a kind of converter valve cooling system key components reliability analysis research.
Background technology
High voltage direct current transmission converter valve cooling system is the significant components of whole straight-flow system, itself is also a system relating to multiple specialties such as HVAC, refrigeration, electricity gas and water process and control.Form this various appliance arrangement of system needs and instrument and meter, therefore its design, installation, debugging, maintenance and detection are proposed significantly high requirement.In recent years; converter valve cooling system is because of reasons such as material ageing, component wear, change of water quality, fouling of heat exchangers burn into sparge pipe electrode and sealing ring fouling corrosion, main pump leak, in-line meter fault and error, Control protection system misoperation, primary equipment design imperfections; the straight-flow system unplanned outage directly resulted in accounts for about the half of sum, and this shows that converter valve cooling system is one of greateset risk threatening straight-flow system safe and reliable operation.Therefore it is highly desirable to carry out the fail-safe analysis work of converter valve cooling system rank.
For the reliability improving complication system, effective way is to find the weak link of system, then carries out the technical research of reliability for the equipment of weak link or system.But in existing converter valve cooling system, owing to lacking corresponding reliability data, and the design of converter valve cooling system, install, debugging, operation maintenance, maintenance and detection etc. there is no the world, country and industry standard and specification, cause that related work is (such as design, maintenance, detection, O&M etc.) without ready patterns to follow, each producer's technical route is obstructed, the job carried out is not comprehensive, cycle is uncertain, technique is nonstandard, detection imperfection, fail to realize the standardization of related work, standardization and institutionalization, reliability and the service life of equipment cannot be improved.Reliability and service life hence for equipment, except the reliability data that manufacturing firm need to provide relevant, the sample that manufacturing firm provides also should be arranged in oneself system to carry out coordinating debugging then to obtain corresponding experimental data by the producers such as design, installation, debugging, according to the investigative technique of reliability, it is carried out reliability upgrading of system.Converter valve cooling system key components directly influences cooling system important technology index, key components is as one of key components, it it is the power-equipment of converter valve cooling system, directly influence the reliability of cooling system, so the reliability requirement for key components is just particularly important.
For the fail-safe analysis of key components, commonly used Statistic analysis models the most widely is Weibull distribution, from theory of probability and statistical angle, and the distributed successional probability distribution of Weibull, its probability density is:
Wherein, x is stochastic variable, λ > 0 it is scale parameter, k > 0 is form parameter, and its cumulative distribution function is the distribution function of extension.Can completely describe the probability density of a real number stochastic variable x, be the integration of probability density function.
Weibull distribution obtains according to the most weak Link Model or series model, can fully reflect defect and the stress impact on components and parts fatigue life of components and parts, have incremental crash rate.Therefore it is suitable using it as component reliability analytical model.Owing to Weibull distribution can utilize probit to infer its distributed constant, it is particularly suited for the distribution situation that wear cumulation lost efficacy.
The Weibull distribution of two parameter is mainly used in the fatigue test of material, and three-parameter weibull distribution is applied to life test or the fail-safe analysis of components and parts.Two parameters of Weibull; its parameter estimation often can bring bigger error; for have with wearout failure be feature mechanical parts life appraisal in; three-parameter Weibull distribution is adopted to be fitted and parameter estimation; higher precision can be obtained; thus compared with two parameters of Weibull, the practical situation of product reliability more can be reflected.
At present the research of reliability Weibull distributed model is had a lot, owing to Weibull distribution is especially suitable for the distribution form that the wear cumulation of engineering goods lost efficacy.But reliability Weibull is distributed majority all to be concentrated on the connectors, research for converter valve cooling system is few, especially the key components of converter valve cooling system, therefore needs the modeling method of the reliability analysis research of the key components of a kind of converter valve cooling system badly.
Summary of the invention
For the deficiencies in the prior art, it is an object of the invention to provide the modeling method of a kind of converter valve cooling system key components reliability analysis research, the present invention adopts the three-parameter weibull distribution being applicable to components and parts life test fail-safe analysis, the reliability of converter valve cooling system key components is carried out statistical analysis, and according to the result analyzed, the key components of converter valve is carried out the practical application of reliability, the reliability situation of key components can be predicted accurately, the maintenance of whole system is proposed scientific and reasonable suggestion, can the overall reliability improving whole system.
It is an object of the invention to adopt following technical proposals to realize:
The present invention provides the modeling method of a kind of converter valve cooling system key components reliability analysis research, described converter valve cooling system key components includes water pump, heat exchanger and air cooler, it thes improvement is that, described modeling method comprises the steps:
(1) converter valve cooling system key components out-of-service time data are obtained；
(2) empirical distribution function of out-of-service time data is analyzed；
(3) with empirical distribution function for fit standard, Weibull distributed model is built；
(4) model parameter in Weibull distributed model is obtained；
(5) the key components reliability analysis research model under Weibull distributed model is built；
(6) parameter of reliability analysis research model is carried out reliability application.
Further, in described step (1), collect the converter valve cooling system key components of same model under same use environment, and obtain its out-of-service time data.
Further, in described step (2), described out-of-service time data are calculated its experience Distribution Value, it is thus achieved that empirical distribution function, and draw empirical distribution function curve based on empirical distribution function, comprise the steps:
A. according to value data size, described fail data being done ascending order arrangement, the data after arrangement are designated as ti successively, wherein i=1, and 2 ..., n；N represents n fail data；
B. the Distribution Value that accumulates experience of each interval point is determined, it is thus achieved that empirical distribution function is F (ti)=(i-0.3)/(n+0.4).
Further, in described step (3), carry out the statistical inference of Weibull distribution with the out-of-service time data of step (2), it is thus achieved that Weibull distributed model, described Weibull distributed model is three-parameter weibull distribution model, with following formula 1) represent:
Wherein: β is form parameter, η is characteristics life parameter, and γ is location parameter, and t is the out-of-service time.
Further, in described step (4), adopt Ke Ermoge love method of inspection (K-S method of inspection) that Weibull distributed model is fitted goodness inspection, it may be judged whether within deviation；If it is, the model parameter in record Weibull distributed model；Otherwise repeating step (2), until test of goodness of fit is within deviation, described deviation can the tables of critical values of Cha Keermoge love inspection obtain.
Further, described model parameter includes form parameter β, characteristics life parameter η corresponding in three-parameter weibull distribution model and location parameter γ.
Further, in described step (5), for the difference using environment and key components type, repeat the above steps (1) to step (4) respectively, set up model parameter to table look-up the data base of file, obtain the key components reliability analysis research model under Weibull distributed model, with following formula 2) represent:
Wherein: β is form parameter, η is characteristics life parameter, and γ is location parameter, and t is the out-of-service time；Form parameter β, characteristics life parameter η and location parameter γ that its model of different key componentses is corresponding are different.
Compared with the prior art, the present invention reaches to provide the benefit that:
1) present invention adopts the three-parameter weibull distribution being applicable to components and parts life test fail-safe analysis, the reliability of converter valve cooling system key components is carried out statistical analysis, and according to the result analyzed, the key components of converter valve is carried out the practical application of reliability, the reliability situation of key components can be predicted accurately, scientific and reasonable suggestion is proposed in the maintenance of whole system, it is possible to the overall reliability improving whole system.
2) fail data is easily obtained.
3) desired parameters is few, calculates uncomplicated, and process is succinct and is easily achieved.
4) reliability prediction can be carried out by calculating data, true and reliable.
5) can be drawn the out-of-service time by calculating, it is simple to purposive maintenance.
Accompanying drawing explanation
Fig. 1 is the flow chart of the modeling method of converter valve cooling system key components reliability analysis research provided by the invention；
Fig. 2 is main circulation pump empirical distribution function and the Weibull distribution function matched curve of specific embodiment provided by the invention；
Fig. 3 is spray pump empirical distribution function and the Weibull distribution function matched curve of specific embodiment provided by the invention；
Fig. 4 is proof submersible sand discharging pump empirical distribution function and the Weibull distribution function matched curve of specific embodiment provided by the invention.
Detailed description of the invention
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.
Direct current transmission converter valve cooling system, including major cycle water pump, heating tank, air radiator, main filter, converter valve and degassing tank；Described major cycle exit of pump, heating tank, air radiator, main filter, converter valve, degassing tank and major cycle unit fixed on water pump suction are connected in turn formation closed circuit by pipeline.
Described direct current transmission converter valve cooling system includes water treatment loop, described water treatment loop includes ion exchanger and accurate filter, described water treatment loop is in parallel with converter valve, and described ion exchanger is connected with accurate filter, and described ion exchanger and accurate filter are equipped with standby.
Described direct current transmission converter valve cooling system includes nitrogen stabilization pressure system, and described nitrogen stabilization pressure system includes expansion drum and nitrogen cylinder, and expansion drum is connected with accurate filter, and nitrogen cylinder expansion drum connects, and described expansion drum is two series connection, and nitrogen cylinder is provided with standby.Direct current transmission converter valve cooling system includes automatic water replenishing system, described automatic water replenishing system includes the raw water pump, moisturizing filter, former water pot, small pump and the moisturizing electrodynamic valve that are sequentially connected with, moisturizing electrodynamic valve is connected with ion exchanger, and described small pump is provided with standby.Heating tank includes at least one electric heater.Degassing tank includes at least one electric heater.
Direct current transmission converter valve cooling system includes electric T-shaped valve, butterfly valve and ball valve, electric T-shaped valve is in parallel in ball valve after connecting with butterfly valve, being connected between heating tank and major cycle water pump, electric T-shaped valve the 3rd end is connected with main filter inlet, and described butterfly valve and electric T-shaped valve are equipped with standby.
Major cycle water pump, heating tank and main filter are equipped with standby.Converter valve and degassing tank are provided with virtue road.Water treatment loop and conductivity measurement branch circuit parallel connection.
The present invention provides modeling method and the application of a kind of converter valve cooling system key components reliability analysis research based on Weibull distribution, it is mainly used in the cooling system key components fail-safe analysis in direct-current transmission converter station and association area thereof, the problem causing cooling system unplanned outage to avoid component reliability to reduce.This method can be depicted the out-of-service time distribution situation of key components and the probability of key components damage appearance accurately, finally gives the statistical analysis rule of key components reliability, then carries out the practical application of reliability according to statistical model.
The present invention utilizes cumulative failure function that the out-of-service time of key components is described, and carry out computing by Weibull distributed model and obtain corresponding model parameter, realize the analysis to key components reliability, complete the practical application to key components reliability based on model parameter.
The flow chart of the modeling method of converter valve cooling system key components reliability analysis research provided by the invention is as it is shown in figure 1, comprise the steps:
(1) the key components out-of-service time data of a number of same type under same working environment are collected；
(2) the out-of-service time data obtained in step (1) are carried out statistical analysis, calculate its experience Distribution Value, it is thus achieved that empirical distribution function, and draw empirical distribution function curve based on this function, comprise the steps:
A. according to value data size, described fail data being done ascending order arrangement, the data after arrangement are designated as ti successively, wherein i=1, and 2 ..., n；N represents n fail data；
B. the Distribution Value that accumulates experience of each interval point is determined, it is thus achieved that empirical distribution function is F (ti)=(i-0.3)/(n+0.4).
(3) with the experience Distribution Value in step (2) for fit standard, carry out the statistical inference of Weibull distribution, draw Weibull fitting function, it is determined that Weibull distributed model；Described Weibull distributed model is three-parameter weibull distribution model, with following formula 1) represent:
Wherein: β is form parameter, η is characteristics life parameter, and γ is location parameter, and t is the out-of-service time.
(4) adopt the Ke Ermoge love method of inspection (also known as K-S method of inspection) that Weibull fitting function is fitted goodness inspection according to the fit standard of step (3), judge whether within deviation, if, the then model parameter in record Weibull distributed model, otherwise repeat step (2), until test of goodness of fit is within deviation；Deviation adopts K-S method of inspection and empirical distribution function to contrast, and the upper limit of deviation value is relevant to sample size and significant level, acquisition of can tabling look-up.
(5) for the difference using environment, the difference of key components type and the difference of other conditions repeat the above steps (1) to step (4) respectively, set up model parameter to table look-up the data base of file, obtain the key components reliability analysis research model under Weibull distributed model；With following formula 2) represent:
Wherein: β is form parameter, η is characteristics life parameter, and γ is location parameter, and t is the out-of-service time；Form parameter β, characteristics life parameter η and location parameter γ that its model of different key componentses is corresponding are different.
(6) with the model parameter in step (5) for benchmark, the practical application of reliability is carried out.Heretofore described modeling method is applicable to all of key components in converter valve cooling system.Heretofore described reliability application is based on the model parameter of Weibull distribution function, for reliability prediction and reliability assessment.
Embodiment
Modeling method and the practical application of the present invention are described below in conjunction with specific embodiment, and key components is water pump, comprises the following steps:
(1) water pump out-of-service time data the record of same model under same use environment are collected, the data that wherein individual other out-of-service time data differ with most of out-of-service time data bigger are rejected, the annual test of water pump is usually 1 year and carries out once, therefore its out-of-service time data were less than 8760 hours, but the out-of-service time of ordinary water pump is thousands of hours, then differ bigger out-of-service time the data of (such as hundreds of hour) with this standard and rejected.Table 1 show the partial failure time data of the water pump of three kinds of different purposes.
Table 1 key components out-of-service time data
(2) the out-of-service time data collected in step (1) being carried out statistical analysis, first according to numerical values recited, these data are done the arrangement of raw sequence, the data after arrangement are designated as t successivelyi(i=1,2 ..., n), the experience estimation value of the cumulative distribution function of each interval point is F (ti)=(i-0.3)/(n+0.4).The estimated value calculated is depicted as empirical distribution function matched curve.
(3) according to the statistical data of step (2) and the experience Distribution Value calculating acquisition, calculate the model parameter of Weibull distribution function, and then obtain Weibull function distributed model.Draw the cumulative failure Function Fitting curve under the Weibull distribution of various water pump respectively.As shown in Fig. 2 Fig. 4.
(4) adopt K-S method of inspection that Weibull fitting function is fitted goodness inspection, it may be judged whether within deviation, if it is, the model parameter in record Weibull distributed model.The present invention, by adopting correlation coefficient method, passes through computer programming calculation, it is determined that the parameter in equation, and then determines form parameter β, characteristics life parameter η corresponding in the cumulative distribution function under three-parameter weibull distribution model and location parameter γ.
(5) step (1) has carried out the key components Weibull model parameter extraction of same model to step (4), next successively the water pump of different purposes is distinguished repeat the above steps (1) to step (4), until water pump in whole converter valve cooling system is carried out the extraction of Weibull model parameter.
Table 2 is the value of form parameter β, the characteristics life parameter η in the three-parameter weibull distribution model that in the implementation case, three kinds of water pump statistical analysis obtain and location parameter γ.
Form parameter β, characteristics life parameter η in table 2 three-parameter weibull distribution model and location parameter γ
| Water pump kind | β | η | γ |
| Main circulation pump | 1.465 | 4881 | 969 |
| Spray pump | 2.599 | 5972 | 0 |
| Proof submersible sand discharging pump | 0.7 | 2980 | 2081 |
(6) according to the out-of-service time data of record and Weibull model parameter, arrange and set up the model library of the file of tabling look-up obtaining out-of-service time and each parameter of Weibull model, obtain the reliability analysis research model of the lower water pump of Weibull distribution.
(7) according to model parameter, water pump actual motion is carried out reliability prediction and reliability assessment, for instance judge the failure type of water pump, it is determined that the on-line monitoring time etc. of water pump.Table 3 is three kinds of water pumps reliability practical application under three-parameter weibull distribution model in the implementation case.
The reliability application of water pump under table 3 three-parameter weibull distribution model
| Water pump kind | Failure type | Normal condition on-line monitoring time (hour) |
| Main circulation pump | Wearout failure | 539 |
| Spray pump | Wearout failure | 530 |
| Proof submersible sand discharging pump | Initial failure | 585 |
Finally should be noted that: above example is only in order to illustrate that technical scheme is not intended to limit; although the present invention being described in detail with reference to above-described embodiment; the specific embodiment of the present invention still can be modified or equivalent replacement by those of ordinary skill in the field; these are without departing from any amendment of spirit and scope of the invention or equivalent replace, within the claims of the present invention all awaited the reply in application.
Claims (7)
1. a modeling method for converter valve cooling system key components reliability analysis research, described converter valve cooling system key components includes water pump, heat exchanger and air cooler, it is characterised in that described modeling method comprises the steps:
(1) converter valve cooling system key components out-of-service time data are obtained；
(2) empirical distribution function of out-of-service time data is analyzed；
(3) with empirical distribution function for fit standard, Weibull distributed model is built；
(4) model parameter in Weibull distributed model is obtained；
(5) the key components reliability analysis research model under Weibull distributed model is built；
(6) parameter of reliability analysis research model is carried out reliability application.
2. modeling method as claimed in claim 1, it is characterised in that in described step (1), collect the converter valve cooling system key components of same model under same use environment, and obtain its out-of-service time data.
3. modeling method as claimed in claim 1, it is characterised in that in described step (2), described out-of-service time data are calculated its experience Distribution Value, obtain empirical distribution function, and draw empirical distribution function curve based on empirical distribution function, comprise the steps:
A. according to value data size, described fail data being done ascending order arrangement, the data after arrangement are designated as ti successively, wherein i=1, and 2 ..., n；N represents n fail data；
B. the Distribution Value that accumulates experience of each interval point is determined, it is thus achieved that empirical distribution function is F (ti)=(i-0.3)/(n+0.4).
4. modeling method as claimed in claim 1, it is characterized in that, in described step (3), the statistical inference of Weibull distribution is carried out with the out-of-service time data of step (2), obtain Weibull distributed model, described Weibull distributed model is three-parameter weibull distribution model, with following formula 1) represent:
Wherein: β is form parameter, η is characteristics life parameter, and γ is location parameter, and t is the out-of-service time.
5. modeling method as claimed in claim 1, it is characterised in that in described step (4), adopts Ke Ermoge love method of inspection that Weibull distributed model is fitted goodness inspection, it may be judged whether within deviation；If it is, the model parameter in record Weibull distributed model；Otherwise repeating step (2), until test of goodness of fit is within deviation, described deviation can the tables of critical values of Cha Keermoge love inspection obtain.
6. modeling method as claimed in claim 5, it is characterised in that described model parameter includes form parameter β, characteristics life parameter η corresponding in three-parameter weibull distribution model and location parameter γ.
7. modeling method as claimed in claim 1, it is characterized in that, in described step (5), for the difference using environment and key components type, repeat the above steps (1) to step (4) respectively, set up model parameter to table look-up the data base of file, obtain the key components reliability analysis research model under Weibull distributed model, with following formula 2) represent:
Wherein: β is form parameter, η is characteristics life parameter, and γ is location parameter, and t is the out-of-service time；Form parameter β, characteristics life parameter η and location parameter γ that its model of different key componentses is corresponding are different.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410738589.5A CN105718620A (en) | 2014-12-04 | 2014-12-04 | Modeling method for reliability statistic analysis of key component of converter valve cooling system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410738589.5A CN105718620A (en) | 2014-12-04 | 2014-12-04 | Modeling method for reliability statistic analysis of key component of converter valve cooling system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105718620A true CN105718620A (en) | 2016-06-29 |
Family
ID=56144123
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410738589.5A Pending CN105718620A (en) | 2014-12-04 | 2014-12-04 | Modeling method for reliability statistic analysis of key component of converter valve cooling system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105718620A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106777577A (en) * | 2016-11-30 | 2017-05-31 | 中国西电电气股份有限公司 | A kind of method that high-voltage switch gear product residual life is predicted based on service data |
| CN107180149A (en) * | 2017-07-18 | 2017-09-19 | 国家电网公司 | A kind of low noise intercepting sewer design method of ultra-high voltage converter station valve tower cooler system |
| CN108399271A (en) * | 2017-12-18 | 2018-08-14 | 广东科鉴检测工程技术有限公司 | Instrument control panel accelerated degradation test method and system |
| CN109683040A (en) * | 2018-12-25 | 2019-04-26 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Reliability checking method, device and the equipment of flexible direct current transmission converter valve |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5736655A (en) * | 1995-07-14 | 1998-04-07 | Kabushiki Kaisha Toshiba | Method for evaluating a remaining life of a rotating machine coil |
| CN1197207A (en) * | 1997-03-28 | 1998-10-28 | 日本碍子株式会社 | Reliability judgment method of suspension insulator |
| CN101038639A (en) * | 2007-04-25 | 2007-09-19 | 上海发电设备成套设计研究院 | Service Life predicting method and system for machine and vulnerable component of generating set |
-
2014
- 2014-12-04 CN CN201410738589.5A patent/CN105718620A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5736655A (en) * | 1995-07-14 | 1998-04-07 | Kabushiki Kaisha Toshiba | Method for evaluating a remaining life of a rotating machine coil |
| CN1197207A (en) * | 1997-03-28 | 1998-10-28 | 日本碍子株式会社 | Reliability judgment method of suspension insulator |
| CN101038639A (en) * | 2007-04-25 | 2007-09-19 | 上海发电设备成套设计研究院 | Service Life predicting method and system for machine and vulnerable component of generating set |
Non-Patent Citations (3)
| Title |
|---|
| 姜海波等: "高压直流输电换流阀冷却***可靠性评估", 《电力安全技术》 * |
| 罗高乔等: "威布尔分布对整体旋转式斯特林制冷机的可靠性计算", 《低温技术》 * |
| 高玮光等: "发电机定子冷却水泵可靠性数据处理及分析", 《原子能科学技术》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106777577A (en) * | 2016-11-30 | 2017-05-31 | 中国西电电气股份有限公司 | A kind of method that high-voltage switch gear product residual life is predicted based on service data |
| CN107180149A (en) * | 2017-07-18 | 2017-09-19 | 国家电网公司 | A kind of low noise intercepting sewer design method of ultra-high voltage converter station valve tower cooler system |
| CN107180149B (en) * | 2017-07-18 | 2020-06-02 | 国家电网公司 | Low-noise shutoff pipe design method for extra-high voltage converter station valve tower cooling system |
| CN108399271A (en) * | 2017-12-18 | 2018-08-14 | 广东科鉴检测工程技术有限公司 | Instrument control panel accelerated degradation test method and system |
| CN108399271B (en) * | 2017-12-18 | 2021-07-06 | 广东科鉴检测工程技术有限公司 | Accelerated degradation test method and system for instrument electronic control equipment |
| CN109683040A (en) * | 2018-12-25 | 2019-04-26 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Reliability checking method, device and the equipment of flexible direct current transmission converter valve |
| CN109683040B (en) * | 2018-12-25 | 2021-10-15 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Method, device and equipment for detecting reliability of flexible direct-current transmission converter valve |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Pretorius et al. | Implementing a DIKW model on a deep mine cooling system | |
| US10839115B2 (en) | Cleansing system for a feed composition based on environmental factors | |
| US20170315543A1 (en) | Evaluating petrochemical plant errors to determine equipment changes for optimized operations | |
| WO2016160910A1 (en) | Advanced data cleansing system and method | |
| CN109710983B (en) | Diesel engine cylinder layered fault diagnosis method based on key performance indexes | |
| KR20140127915A (en) | Method and system for real time gas turbine performance advisory | |
| CN105718620A (en) | Modeling method for reliability statistic analysis of key component of converter valve cooling system | |
| CN105242155A (en) | Transformer fault diagnosis method based on entropy weight method and grey correlation analysis | |
| CN104390657A (en) | Generator set operating parameter measuring sensor fault diagnosis method and system | |
| CN104677997B (en) | A kind of transformer oil chromatographic on-line monitoring differentiation method for early warning | |
| CN109214595A (en) | Nuclear power plant's main pump three-level seal failure prediction technique based on big data analysis | |
| CN109188082A (en) | A kind of Transformer condition evaluation based on BP neural network | |
| CN116383636A (en) | Coal mill fault early warning method based on PCA and LSTM fusion algorithm | |
| CN117308275B (en) | Temperature difference-based pipeline connection abnormality detection method and system | |
| Oscar et al. | The monitoring system of an actual technical condition for pumping units with frequency analysis | |
| US10429828B2 (en) | Plant simulation device and plant simulation method with first parameter adjustable at start and second parameter adjustable during operation of the plant | |
| CN117441180A (en) | Method and system for detecting operational anomalies in a thermal energy exchange system, and a method of training a machine learning model | |
| Zhou | Failure trend analysis using time series model | |
| CN114235108B (en) | Abnormal state detection method and device for gas flowmeter based on data analysis | |
| CN105279553A (en) | Method for identifying fault degree of high-pressure heater water supply system | |
| JP6980034B2 (en) | Device life evaluation method | |
| CN112328590B (en) | Deep cleaning method for operation data of thermal equipment | |
| US8738326B2 (en) | Performance characteristic calculation and comparison | |
| Yadav et al. | Dynamic PRA with component aging and degradation modeled utilizing plant risk monitoring data | |
| Murmanskii et al. | Features of steam turbines diagnostics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20170605 Address after: 100031 Xicheng District West Chang'an Avenue, No. 86, Beijing Applicant after: State Grid Corporation of China Applicant after: China-EPRI Electric Power Engineering Co., Ltd. Applicant after: Electric Power Research Institute of State Grid Shandong Electric Power Company Address before: 100031 Xicheng District West Chang'an Avenue, No. 86, Beijing Applicant before: State Grid Corporation of China Applicant before: State Grid Smart Grid Institute Applicant before: China-EPRI Electric Power Engineering Co., Ltd. Applicant before: Electric Power Research Institute of State Grid Shandong Electric Power Company |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160629 |
|
| RJ01 | Rejection of invention patent application after publication |