CN108266336A - A kind of wind power equipment maintenance strategy decision system - Google Patents
A kind of wind power equipment maintenance strategy decision system Download PDFInfo
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- CN108266336A CN108266336A CN201810013791.XA CN201810013791A CN108266336A CN 108266336 A CN108266336 A CN 108266336A CN 201810013791 A CN201810013791 A CN 201810013791A CN 108266336 A CN108266336 A CN 108266336A
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
- F03D80/50—Maintenance or repair
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a kind of wind power equipment maintenance strategy decision system, including fault parameter determining module, wind power equipment fault parameter classification judgment module, maintenance mode decision-making module, service intervals decision-making module；The fault parameter determining module is connected with wind power equipment fault parameter classification judgment module, and the wind power equipment fault parameter classification judgment module is connected with maintenance mode decision-making module, and the maintenance mode decision-making module is connected with service intervals decision-making module；Maintenance mode decision-making module in the wind power equipment maintenance measures system is according to wind power equipment fault parameter classification judgment module gained judging result, determine the specific maintenance mode of wind power equipment, the maintenance mode determined according to maintenance mode decision-making module, referring concurrently to wind power equipment fault parameter classification judgment module acquired results, the specific service intervals of wind power equipment are further determined that；The simple system is comprehensive, is with a wide range of applications in wind power equipment area of maintenance.
Description
Technical field
The present invention relates to wind power equipment maintenance technology field more particularly to a kind of wind power equipment maintenance strategy decision systems.
Background technology
China's Wind Power Generation Industry is quickly grown, and single-machine capacity and accumulative installed capacity rise year by year, and designing and manufacturing level is continuous
It improves, still, the O＆M low SI of wind power equipment.The maintenance strategy that wind power plant is mainly taken at present adds afterwards for periodic maintenance
Repair.Such maintenance strategy lacks specific aim, serious " cross and repair " and " owing repair " is caused, in face of having serious consequence
Failure it is more aobvious passive, in wind power plant actual motion, maintenance cost occupies the 25-30% of cost of electricity-generating.No matter theoretically
Or in putting into practice, which is proved to be low-level.
In face of currently backward wind power equipment maintenance levels, which type of maintenance strategy can improve equipment dependability, carry
High maintenance efficiency, reducing maintenance cost becomes critical problem urgently to be resolved hurrily.
Invention content
The purpose of the present invention is that solve the deficiencies in the prior art, can be with it is an object of the invention to develop one kind
The conductive hydrogel of 3D printing, by the way that PEGDA hydrogels to be coupled to the function to realize conductive with interfacial polymerization techniques.With reference to 3D
Printing technique can simply and efficiently prepare variously-shaped conductive hydrogel structure.
The present invention is achieved through the following technical solutions above-mentioned purpose：
The present invention includes fault parameter determining module, wind power equipment fault parameter classification judgment module, maintenance mode decision-making module
With service intervals decision-making module；
The fault parameter determining module is connected with wind power equipment fault parameter classification judgment module, the wind power equipment failure ginseng
Several classes of other judgment modules are connected with maintenance mode decision-making module, the maintenance mode decision-making module and service intervals decision-making module phase
Even；
The fault parameter determining module determines four kinds of parameter sizes of wind power equipment failure, including determining importance size, really
Determine average time between failures size, determine state-detection expense size and crash rate type；
The wind power equipment fault parameter classification judgment module, for judging each fault parameter generic feelings to wind power equipment
Condition includes whether the judgement of important judgement, the length judgement of average time between failures, state-detection expense height and difficulty or ease
And the judgement of crash rate type, and will determine that result is transmitted to maintenance mode decision-making module；
The maintenance mode decision-making module makes a policy judgement for the maintenance mode to wind power equipment, the maintenance side that can determine that
Formula includes periodic maintenance, condition maintenarnce, correction maintenance and improves repair, and pass the result to service intervals decision-making module；
The service intervals decision-making module makes a policy judgement for the service intervals to wind power equipment, the foundation judged be therefore
The maintenance mode that barrier parameter determination module acquired results and maintenance mode decision-making module determine.
It is currently preferred, the fault parameter determining module is determined including importance, average time between failures determines,
State-detection expense determines and crash rate type determines；
Fault parameter determining module each section definitive result simultaneously as wind power equipment fault parameter classification judgment module with
And the basis of service intervals decision-making module.
Currently preferred, the wind power equipment fault parameter classification judgment module carries out four part judgements, includes whether
It is important judge, average time between failures length judges, state-detection expense height difficulty or ease judge and crash rate type is sentenced
It is disconnected；
The judging result of described wind power equipment fault parameter classification judgment module each section is determined as maintenance mode decision-making module
Plan foundation.
Currently preferred, the maintenance mode decision-making module result of decision includes periodic maintenance, condition maintenarnce, subsequent dimension
Repair and improve repair；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is long, state-detection feelings
Condition is expensive and difficult, crash rate type is B/C, and maintenance mode decision is periodic maintenance；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is long, state-detection feelings
Condition expense it is small and it is tired easily, crash rate type be B/C, maintenance mode decision be condition maintenarnce；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is long, state-detection feelings
Condition expense is without judging, crash rate type is D/E/F, and maintenance mode decision is condition maintenarnce；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is short, state-detection feelings
Condition expense is without judging, crash rate type is D/E/F, and maintenance mode decision is repaired to improve；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is short, state-detection feelings
Condition expense is without judging, crash rate type is B/C, and maintenance mode decision is condition maintenarnce；
The wind power equipment fault parameter classification judgment module result is inessential, average time between failures without judgement, shape
State detection case expense is without judging, crash rate type is without judging, maintenance mode decision is correction maintenance.
Currently preferred, the service intervals decision-making module is referring especially to the maintenance mode decision-making module decision knot
The length of crash rate type and average time between failures determined by fruit, fault parameter determining module determines.
It is currently preferred, the maintenance mode decision-making module result of decision be periodic maintenance, wind power equipment fault parameter
Classification judgment module crash rate judging result be B/C, the failure rate characteristic of maintenance intervals decision references fault parameter determining module
It determines.
It is currently preferred, the maintenance mode decision-making module result of decision be condition maintenarnce, wind power equipment fault parameter
Classification judgment module crash rate judging result be B/C, the failure rate characteristic of maintenance intervals decision references fault parameter determining module
It determines.
It is currently preferred, the maintenance mode decision-making module result of decision be condition maintenarnce, wind power equipment fault parameter
Classification judgment module crash rate judging result be D/E/F, the mean failure rate of maintenance intervals decision references fault parameter determining module
Interval time determines.
Currently preferred, the maintenance mode decision-making module result of decision is repaired to improve, without repairing interval
Decision；
The maintenance mode decision-making module result of decision be condition maintenarnce, wind power equipment fault parameter classification judgment module crash rate
Judging result is B/C, and the failure rate characteristic of maintenance intervals decision references fault parameter determining module determines；
The maintenance mode decision-making module result of decision is correction maintenance, without repairing interval decision.
The beneficial effects of the present invention are：
The present invention provides a kind of wind power equipment maintenance strategy decision system, which can be according to the reality of each wind power equipment failure
Border characteristic, make separate decisions out maintenance strategy adaptable therewith.
Description of the drawings
Fig. 1 is a kind of structure diagram of wind power equipment maintenance strategy decision system of the present invention.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings：
Embodiment 1
As shown in Figure 1, a kind of wind power equipment maintenance strategy decision system, including fault parameter determining module 1, wind power equipment failure
Clock rate judgment module 2, maintenance mode decision-making module 3 and service intervals decision-making module 4；
Fault parameter determining module 1 is connected with wind power equipment fault parameter classification judgment module 2, wind power equipment fault parameter classification
Judgment module 2 is connected with maintenance mode decision-making module 3, and maintenance mode decision-making module 3 is connected with service intervals decision-making module 4；
Fault parameter determining module 1 determines four kinds of parameter sizes of wind power equipment failure, including determining importance size, determines flat
Equal time between failures size, determines state-detection expense size and crash rate type；
Wind power equipment fault parameter classification judgment module 2, for judging each fault parameter generic situation to wind power equipment, packet
Include whether important judgement, average time between failures length judge, the judgement of state-detection expense height and difficulty or ease and
The judgement of crash rate type, and will determine that result is transmitted to maintenance mode decision-making module；
Maintenance mode decision-making module 3 is for the judgement that makes a policy to the maintenance mode of wind power equipment, the maintenance mode packet that can determine that
It includes periodic maintenance, condition maintenarnce, correction maintenance and improves repair, and pass the result to service intervals decision-making module；
Service intervals decision-making module 4 makes a policy judgement for the service intervals to wind power equipment, and the foundation judged is failure ginseng
The maintenance mode that number determining module acquired results and maintenance mode decision-making module determine.
Above system can be according to the actual characteristic of each wind power equipment failure, and make separate decisions out maintenance adaptable therewith
Strategy.
Specifically, fault parameter determining module 1 determines that 11, average time between failures determines 12, state including importance
Testing cost determine 13 and crash rate type determine 14；
1 each section definitive result of fault parameter determining module is simultaneously as wind power equipment fault parameter classification judgment module and dimension
The basis of shield interval decision-making module.
Specifically, wind power equipment fault parameter classification judgment module 2 carry out four part judgements, i.e., whether it is important judge 21,
Average time between failures length judges that 22, state-detection expense height difficulty or ease judge the judgement 24 of 23 and crash rate type；
The judging result of described wind power equipment fault parameter classification judgment module each section is determined as maintenance mode decision-making module
Plan foundation.
Specifically, 3 result of decision of maintenance mode decision-making module includes periodic maintenance 31, condition maintenarnce 32, subsequent dimension
It repaiies 33 and improves repair 34；
2 result of wind power equipment fault parameter classification judgment module is：It is important, average time between failures is long, state-detection
Situation is expensive and difficult, crash rate type is B/C, and maintenance mode decision is periodic maintenance 31；
2 result of wind power equipment fault parameter classification judgment module is：It is important, average time between failures is long, state-detection
Situation expense it is small and it is tired easily, crash rate type be B/C, maintenance mode decision be condition maintenarnce 32；
2 result of wind power equipment fault parameter classification judgment module is：It is important, average time between failures is long, state-detection
Situation expense is without judging, crash rate type is D/E/F, and maintenance mode decision is condition maintenarnce 32；
2 result of wind power equipment fault parameter classification judgment module is：It is important, average time between failures is short, state-detection
Situation expense is without judging, crash rate type is D/E/F, and maintenance mode decision is improves repair 34；
2 result of wind power equipment fault parameter classification judgment module is：It is important, average time between failures is short, state-detection
Situation expense is without judging, crash rate type is B/C, and maintenance mode decision is condition maintenarnce 32；
The wind power equipment fault parameter classification judgment module result is：Inessential, average time between failures is without judging, shape
State detection case expense is without judging, crash rate type is without judging, maintenance mode decision is correction maintenance 33.
Specifically, the service intervals decision-making module 4 is referring especially to 3 result of decision of maintenance mode decision-making module, event
The length of crash rate type and average time between failures determined by barrier parameter determination module 1 determines；
3 result of decision of maintenance mode decision-making module is periodic maintenance, and wind power equipment fault parameter classification judgment module fails
Rate judging result is B/C, and the failure rate characteristic of 41 fault parameter determining module of maintenance intervals decision references determines；
3 result of decision of maintenance mode decision-making module is condition maintenarnce, and wind power equipment fault parameter classification judgment module fails
Rate judging result is B/C, and the failure rate characteristic of 41 fault parameter determining module of maintenance intervals decision references determines；
3 result of decision of maintenance mode decision-making module is condition maintenarnce, and wind power equipment fault parameter classification judgment module fails
Rate judging result is D/E/F, and the average time between failures of 42 fault parameter determining module of maintenance intervals decision references determines；
3 result of decision of maintenance mode decision-making module is repaired to improve, without repairing interval decision 43；
3 result of decision of maintenance mode decision-making module is condition maintenarnce, and wind power equipment fault parameter classification judgment module fails
Rate judging result is B/C, and the failure rate characteristic of 41 fault parameter determining module of maintenance intervals decision references determines；
3 result of decision of maintenance mode decision-making module is correction maintenance, without repairing interval decision.
The present invention is according to the actual characteristic of each wind power equipment failure, and make separate decisions out maintenance strategy adaptable therewith.
The data source of fault parameter determining module 1 is the fault record data of wind, farm site, including manually recorded
Operation order and the self registering failure of system etc..
In conclusion the present invention provides a kind of wind power equipment maintenance strategy decision system, the wind power equipment maintenance measures system
Maintenance mode decision-making module in system determines that wind-powered electricity generation is set according to wind power equipment fault parameter classification judgment module gained judging result
Standby specific maintenance mode, according to the maintenance mode that maintenance mode decision-making module determines, referring concurrently to wind power equipment fault parameter
Classification judgment module acquired results further determine that the specific service intervals of wind power equipment, and the simple system is comprehensive, is set in wind-powered electricity generation
It is with a wide range of applications in standby area of maintenance.
Those skilled in the art do not depart from the present invention essence and spirit, can there are many deformation scheme realize the present invention,
The foregoing is merely preferably feasible embodiments of the invention, not thereby limit to the interest field of the present invention, all with this
The equivalent structure variation that description of the invention and accompanying drawing content are made, is both contained within the interest field of the present invention.
Claims (9)
1. a kind of wind power equipment maintenance strategy decision system, which is characterized in that including fault parameter determining module, wind power equipment event
Hinder clock rate judgment module, maintenance mode decision-making module and service intervals decision-making module；
The fault parameter determining module is connected with wind power equipment fault parameter classification judgment module, the wind power equipment failure ginseng
Several classes of other judgment modules are connected with maintenance mode decision-making module, the maintenance mode decision-making module and service intervals decision-making module phase
Even；
The fault parameter determining module determines four kinds of parameter sizes of wind power equipment failure, including determining importance size, really
Determine average time between failures size, determine state-detection expense size and crash rate type；
The wind power equipment fault parameter classification judgment module, for judging each fault parameter generic feelings to wind power equipment
Condition includes whether the judgement of important judgement, the length judgement of average time between failures, state-detection expense height and difficulty or ease
And the judgement of crash rate type, and will determine that result is transmitted to maintenance mode decision-making module；
The maintenance mode decision-making module makes a policy judgement for the maintenance mode to wind power equipment, the maintenance side that can determine that
Formula includes periodic maintenance, condition maintenarnce, correction maintenance and improves repair, and pass the result to service intervals decision-making module；
The service intervals decision-making module makes a policy judgement for the service intervals to wind power equipment, the foundation judged be therefore
The maintenance mode that barrier parameter determination module acquired results and maintenance mode decision-making module determine.
2. wind power equipment maintenance strategy decision system according to claim 1, which is characterized in that the fault parameter determines
Module is determined including importance, average time between failures determines, state-detection expense determines and crash rate type determines；
Fault parameter determining module each section definitive result simultaneously as wind power equipment fault parameter classification judgment module with
And the basis of service intervals decision-making module.
3. wind power equipment maintenance strategy decision system according to claim 1, which is characterized in that the wind power equipment failure
Clock rate judgment module carries out four part judgements, includes whether important judgement, the judgement of average time between failures length, state
Testing cost height difficulty or ease judge and the judgement of crash rate type；
The judging result of described wind power equipment fault parameter classification judgment module each section is determined as maintenance mode decision-making module
Plan foundation.
4. wind power equipment maintenance strategy decision system according to claim 1, which is characterized in that the maintenance mode decision
The module result of decision includes periodic maintenance, condition maintenarnce, correction maintenance and improves repair；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is long, state-detection feelings
Condition is expensive and difficult, crash rate type is B/C, and maintenance mode decision is periodic maintenance；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is long, state-detection feelings
Condition expense it is small and it is tired easily, crash rate type be B/C, maintenance mode decision be condition maintenarnce；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is long, state-detection feelings
Condition expense is without judging, crash rate type is D/E/F, and maintenance mode decision is condition maintenarnce；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is short, state-detection feelings
Condition expense is without judging, crash rate type is D/E/F, and maintenance mode decision is repaired to improve；
The wind power equipment fault parameter classification judgment module result is important, average time between failures is short, state-detection feelings
Condition expense is without judging, crash rate type is B/C, and maintenance mode decision is condition maintenarnce；
The wind power equipment fault parameter classification judgment module result is inessential, average time between failures without judgement, shape
State detection case expense is without judging, crash rate type is without judging, maintenance mode decision is correction maintenance.
5. wind power equipment maintenance strategy decision system according to claim 1, which is characterized in that the service intervals decision
Module referring especially to crash rate type determined by the maintenance mode decision-making module result of decision, fault parameter determining module and
The length of average time between failures determines.
6. wind power equipment maintenance strategy decision system according to claim 5, which is characterized in that the maintenance mode decision
The module result of decision is periodic maintenance, and wind power equipment fault parameter classification judgment module crash rate judging result is B/C, between repair
It is determined every the failure rate characteristic of decision references fault parameter determining module.
7. wind power equipment maintenance strategy decision system according to claim 5, which is characterized in that the maintenance mode decision
The module result of decision is condition maintenarnce, and wind power equipment fault parameter classification judgment module crash rate judging result is B/C, between repair
It is determined every the failure rate characteristic of decision references fault parameter determining module.
8. wind power equipment maintenance strategy decision system according to claim 5, which is characterized in that the maintenance mode decision
The module result of decision is condition maintenarnce, and wind power equipment fault parameter classification judgment module crash rate judging result is D/E/F, is repaired
The average time between failures of interval decision references fault parameter determining module determines.
9. wind power equipment maintenance strategy decision system according to claim 5, which is characterized in that
The maintenance mode decision-making module result of decision is repaired to improve, without repairing interval decision；
The maintenance mode decision-making module result of decision be condition maintenarnce, wind power equipment fault parameter classification judgment module crash rate
Judging result is B/C, and the failure rate characteristic of maintenance intervals decision references fault parameter determining module determines；
The maintenance mode decision-making module result of decision is correction maintenance, without repairing interval decision.
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| CN201810013791.XA CN108266336B (en) | 2018-01-08 | 2018-01-08 | Wind power equipment maintenance strategy decision system |
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|---|---|---|---|
| CN201810013791.XA CN108266336B (en) | 2018-01-08 | 2018-01-08 | Wind power equipment maintenance strategy decision system |
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| CN108266336A true CN108266336A (en) | 2018-07-10 |
| CN108266336B CN108266336B (en) | 2023-08-01 |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495549A (en) * | 2011-11-22 | 2012-06-13 | 中联重科股份有限公司 | Remote maintenance decision system of engineering machinery and method thereof |
| CN103020422A (en) * | 2012-11-12 | 2013-04-03 | 中航沈飞民用飞机有限责任公司 | Method for calculating maintenance time interval of civil aircraft system |
| CN103810328A (en) * | 2014-01-16 | 2014-05-21 | 国家电网公司 | Transformer maintenance decision method based on hybrid model |
| CN106096741A (en) * | 2016-06-14 | 2016-11-09 | 国电南瑞科技股份有限公司 | A kind of implementation method of Intelligent fault O&M DSS |
| CN106600095A (en) * | 2016-07-27 | 2017-04-26 | 中国特种设备检测研究院 | Reliability-based maintenance evaluation method |
| CN107229979A (en) * | 2017-04-17 | 2017-10-03 | 北京航空航天大学 | A kind of optimization method of repairable deteriorating system periodicity preventive maintenance strategy |
| CN107544457A (en) * | 2017-08-31 | 2018-01-05 | 广东石油化工学院 | Refinery plant running cycle expert decision system and method based on fail-safe analysis |
-
2018
- 2018-01-08 CN CN201810013791.XA patent/CN108266336B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495549A (en) * | 2011-11-22 | 2012-06-13 | 中联重科股份有限公司 | Remote maintenance decision system of engineering machinery and method thereof |
| CN103020422A (en) * | 2012-11-12 | 2013-04-03 | 中航沈飞民用飞机有限责任公司 | Method for calculating maintenance time interval of civil aircraft system |
| CN103810328A (en) * | 2014-01-16 | 2014-05-21 | 国家电网公司 | Transformer maintenance decision method based on hybrid model |
| CN106096741A (en) * | 2016-06-14 | 2016-11-09 | 国电南瑞科技股份有限公司 | A kind of implementation method of Intelligent fault O&M DSS |
| CN106600095A (en) * | 2016-07-27 | 2017-04-26 | 中国特种设备检测研究院 | Reliability-based maintenance evaluation method |
| CN107229979A (en) * | 2017-04-17 | 2017-10-03 | 北京航空航天大学 | A kind of optimization method of repairable deteriorating system periodicity preventive maintenance strategy |
| CN107544457A (en) * | 2017-08-31 | 2018-01-05 | 广东石油化工学院 | Refinery plant running cycle expert decision system and method based on fail-safe analysis |
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