CN111445043A - Power plant equipment maintenance method, system and device - Google Patents
Power plant equipment maintenance method, system and device Download PDFInfo
- Publication number
- CN111445043A CN111445043A CN202010223969.0A CN202010223969A CN111445043A CN 111445043 A CN111445043 A CN 111445043A CN 202010223969 A CN202010223969 A CN 202010223969A CN 111445043 A CN111445043 A CN 111445043A
- Authority
- CN
- China
- Prior art keywords
- equipment
- function
- fault
- overhaul
- distribution
- 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.)
- Granted
Links
- 238000012423 maintenance Methods 0.000 title claims abstract description 157
- 238000000034 method Methods 0.000 title claims abstract description 67
- 238000005315 distribution function Methods 0.000 claims abstract description 119
- 238000012545 processing Methods 0.000 claims abstract description 53
- 230000006870 function Effects 0.000 claims description 86
- 238000004364 calculation method Methods 0.000 claims description 15
- 238000003066 decision tree Methods 0.000 claims description 12
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 6
- 230000005611 electricity Effects 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 description 13
- 230000000875 corresponding effect Effects 0.000 description 13
- 238000004422 calculation algorithm Methods 0.000 description 8
- 238000010248 power generation Methods 0.000 description 3
- 238000012935 Averaging Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- General Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Strategic Management (AREA)
- Computational Mathematics (AREA)
- Computing Systems (AREA)
- Primary Health Care (AREA)
- Operations Research (AREA)
- Water Supply & Treatment (AREA)
- Algebra (AREA)
- Public Health (AREA)
- Quality & Reliability (AREA)
- Databases & Information Systems (AREA)
- General Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method, a system and a device for overhauling power plant equipment, wherein a distribution function of a failure mode is obtained by carrying out data processing on overhauling record data, and a loss function generated after the failure mode occurs is combined with the distribution function of the failure mode to form a first function of economic loss probability distribution, so that a first minimum equipment failure loss value is obtained; the data of the maintenance record data are processed to obtain a distribution function of reliability, and a loss function generated after the fault mode occurs is combined with the distribution function of reliability to form a second function of economic loss probability distribution to obtain a second minimum equipment fault loss value; and stopping iteration by enabling the first minimum equipment fault loss value and the second minimum equipment fault loss value to meet a certain ratio, wherein the final overhaul strategy is any one detection strategy corresponding to the current first minimum equipment fault loss value and the current second minimum equipment fault loss value, and the final overhaul strategy is used for overhauling the power plant equipment.
Description
Technical Field
The invention relates to the technical field of power plant equipment maintenance, in particular to a method, a system and a device for maintaining power plant equipment.
Background
The traditional equipment maintenance strategy formulation and optimization is to generate an equipment maintenance strategy by taking a maintenance item list and intervals provided by suppliers of equipment of a power plant as a basis, combining the experience of power plant special workers and similar modes of the maintenance strategy of the equipment of the power plant and the like, and optimize the maintenance strategy on the basis; on the other hand, the reliability analysis is in a real situation that no system is used, and therefore, the reliability analysis is completely disconnected from the maintenance strategy.
The technical disadvantage of the current general RCM method is that the method is mainly used for generating a maintenance strategy and is weak in the association of faults and maintenance tasks; 1. the fault analysis layer is carried out, because the fault mode and the influence analysis determined by the analysis method are based on the analysis of the equipment function, the maintenance task is in the equipment layer rather than the maintainable unit layer below the equipment, so that the maintenance task is not further associated with the actual maintainable unit with the fault, the maintenance task cannot be executed, and the dependency relationship of the equipment fault is not clear when the maintenance task is executed; 2. the failure analysis remains in a qualitative method, and quantitative analysis is not performed, so that a corresponding analysis report cannot be generated to further guide maintenance work. 3. Many general RCM analysis means belong to the scope of random human and equipment quality, which results in a large amount of resources being wasted in the analysis process and the function of the analysis cannot be achieved.
Disclosure of Invention
In view of the above, the present invention provides a method for overhauling power plant equipment, so as to solve the problems that the existing overhauling strategy is not highly correlated with the fault and the overhauling task, and the overhauling strategy is disconnected from the reliability analysis. A second object of the invention is to provide a system for servicing power plant equipment.
In order to achieve the first object, the invention provides the following technical scheme:
a method of servicing a power plant device, the method comprising:
acquiring maintenance record data of power plant equipment in real time according to a preset period, wherein the maintenance record data comprises equipment, components, fault modes, maintenance duration and fault time;
processing the data of the maintenance record to obtain a distribution function F of a failure moden(X), wherein n is the number of failure modes, and X is a vector consisting of the equipment, the component, the overhaul mode, the overhaul duration, and the failure time;
processing the data of the maintenance record to obtain a distribution function f of reliabilityn(t), wherein n is the number of failure modes and t is time;
respectively carrying out distribution function F on the fault modes according to the obtained influence coefficients of the running environment and the state of the equipment on the reliability of the fault modesn(X) and a distribution function f of said reliabilitiesn(t) correcting to obtain the distribution function F of the corrected failure modesn' (X) and distribution function f of corrected reliabilityn'(t)；
Obtaining L a loss function generated after an equipment failure mode occursnWherein n is the number of failure modes;
according to the loss function LnAnd a distribution function F of said corrected failure modesn' (X) obtaining a first function of economic loss probability distribution
According to the loss function LnAnd a distribution function f of said corrected reliabilitiesn' (t) obtaining a second function of economic loss probability distribution
Seeking L a first function corresponding to the economic loss probability distribution by a loop iteration methodMRelative first minimum equipment failure loss value CM；
According to the input maintenance strategy data, carrying out a second function l on the economic loss probability distributionRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a second minimum equipment fault loss value C by a loop iteration methodR；
Calculating r-C according to the same maintenance strategyR/CMStopping iteration when r is 0.9-1.1, and setting the final maintenance strategy to be the current first minimum equipment fault loss value CMAnd said second minimum equipment failure loss value CRAnd any corresponding maintenance strategy is utilized to carry out maintenance on the power plant equipment.
Preferably, the second function l for the economic loss probability distribution is based on the inputted overhaul strategy dataRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a second minimum equipment fault loss value C by a loop iteration methodRPreviously, the method further comprises:
obtaining the future planned generating capacity of the unit
Wherein p (t) is a function of the power and time of the unit, t1For optimum start time of overhaul, t2For the maintenance end time, the maintenance time length T is T2-t1；
Best start time t for overhaul1For planning the generation of electricity in the future of the unit
The time corresponding to the minimum;
the overhaul strategy data comprises the equipment, the components, the overhaul mode and the overhaul duration T.
Preferably, said second function/for said economic loss probability distributionRThe data processing is carried out to obtain an equipment fault loss distribution function LRThe method specifically comprises the following steps:
according to the second function l of the economic loss probability distributionRAnd obtaining an equipment fault loss distribution function L by the overhaul strategy data through a Monte Carlo simulation methodR。
Preferably, the data processing is performed on the overhaul record data to obtain a distribution function F of the failure moden(X), specifically including:
classifying historical samples according to six attributes of the equipment, the component, the fault mode, the overhaul duration and the fault time;
taking the distribution rule of the fault mode as a problem to be solved, and respectively taking the five attributes of the equipment, the component, the overhaul mode, the overhaul duration and the fault time as root nodes to construct a decision tree of the distribution rule of the fault mode;
respectively calculating the information gains of the five attributes of the equipment, the component, the overhaul mode, the overhaul duration and the fault time to obtain a distribution function F of a fault moden(X)。
Preferably, the overhaul record data is subjected to data processing to obtain a distribution function f of reliabilityn(t), specifically including:
carrying out data fitting on the maintenance record data by Weibull distribution/normal distribution/Gaussian distribution to obtainDistribution function f to reliabilityn(t)。
The invention provides a maintenance system of power plant equipment, which comprises:
the maintenance record data acquisition module is used for acquiring maintenance record data of the power plant equipment in real time according to a preset period, wherein the maintenance record data comprises equipment, components, a fault mode, a maintenance mode, maintenance time and fault time;
a fault mode distribution function processing module for processing the data of the maintenance record to obtain a fault mode distribution function Fn(X), wherein n is the number of failure modes, and X is a vector consisting of the equipment, the component, the overhaul mode, the overhaul duration, and the failure time;
a reliability distribution function processing module for processing the maintenance record data to obtain a reliability distribution function fn(t); wherein n is the number of the failure modes, and t is time;
an influence coefficient correction module for respectively performing distribution function F on the fault mode according to the obtained influence coefficients of the equipment operating environment and the state on the reliability of the fault moden(X) and a distribution function f of said reliabilitiesn(t) correcting to obtain the distribution function F of the corrected failure modesn' (X) and distribution function f of corrected reliabilityn'(t)；
A loss function obtaining module for obtaining a loss function L generated after the device failure mode occursnWherein n is the number of failure modes;
an economic loss probability distribution first function processing module for processing the economic loss probability distribution according to the loss function LnAnd a distribution function F of said corrected failure modesn' (X) obtaining a first function of economic loss probability distribution
A second function processing module for economic loss probability distribution according to the loss function LnAnd a distribution function f of said corrected reliabilitiesn' (t) obtaining a second function of economic loss probability distribution
A first minimum equipment failure loss value calculation module for searching a first function L of the economic loss probability distribution by a loop iteration methodMRelative first minimum equipment failure loss value CM；
A second minimum equipment fault loss value calculation module for calculating a second function l of the economic loss probability distribution according to the input maintenance strategy dataRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a second minimum equipment fault loss value C by a loop iteration methodR；
A ratio calculation module for calculating r ═ C according to the same maintenance strategyR/CMStopping iteration when r is 0.9-1.1, and setting the final maintenance strategy to be the current first minimum equipment fault loss value CMAnd said second minimum equipment failure loss value CRAnd any corresponding maintenance strategy is utilized to carry out maintenance on the power plant equipment.
Preferably, the system further comprises:
the maintenance time calculation module is used for acquiring the future planned generated energy of the unit
Wherein p (t) is a function of the power and time of the unit, t1For optimum start time of overhaul, t2For the maintenance end time, the maintenance time length T is T2-t1(ii) a Best start time t for overhaul1For planning the generation of electricity in the future of the unit
The time corresponding to the minimum; the overhaul strategy data comprises the equipment, the components, the overhaul mode and the overhaul duration T.
Preferably, the second minimum device fault loss value calculation module is specifically configured to:
according to the second function l of the economic loss probability distributionRAnd obtaining an equipment fault loss distribution function L by the overhaul strategy data through a Monte Carlo simulation methodR。
Preferably, the failure mode distribution function processing module is specifically configured to:
classifying historical samples according to six attributes of the equipment, the component, the fault mode, the overhaul duration and the fault time;
taking the distribution rule of the fault mode as a problem to be solved, and respectively taking the five attributes of the equipment, the component, the overhaul mode, the overhaul duration and the fault time as root nodes to construct a decision tree of the distribution rule of the fault mode;
respectively calculating the information gains of the five attributes of the equipment, the component, the overhaul mode, the overhaul duration and the fault time to obtain a distribution function F of a fault moden(X)。
The present invention also provides an apparatus comprising a memory and a processor, wherein:
the memory is used for storing a computer program;
the processor is configured to execute the computer program to implement the method for overhauling the power plant equipment according to any of the above embodiments.
The method for overhauling the power plant equipment comprises the steps of acquiring overhauling record data of the power plant equipment in real time according to a preset period, wherein the overhauling record data comprises equipment, components, a fault mode, an overhauling mode, overhauling time and fault time; processing the data of the maintenance record to obtain a distribution function F of the failure moden(X), wherein n is the number of failure modes, and X is a vector consisting of equipment, components, a maintenance mode, maintenance time and failure time; processing the data of the maintenance record to obtain a distribution function f of reliabilityn(t), wherein n is the number of failure modesT is time; respectively carrying out distribution function F on the fault modes according to the obtained influence coefficients of the running environment and the state of the equipment on the reliability of the fault modesn(X) and distribution function f of reliabilityn(t) correcting to obtain the distribution function F of the corrected failure modesn' (X) and distribution function f of corrected reliabilityn' (t) obtaining a loss function L generated after the occurrence of the equipment failure modenWhere n is the number of failure modes, according to a loss function LnAnd distribution function F of corrected failure modesn' (X) obtaining a first function of economic loss probability distribution
According to a loss function LnAnd distribution function f of corrected reliabilityn' (t) obtaining a second function of economic loss probability distribution
Seeking and economic loss probability distribution first function L through loop iteration methodMRelative first minimum equipment failure loss value CM(ii) a According to the input maintenance strategy data, a second function l is given to the probability distribution of economic lossRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a second minimum equipment fault loss value C by a loop iteration methodR(ii) a Calculating r-C according to the same maintenance strategyR/CMAnd stopping iteration when r is 0.9-1.1, and finally, setting the maintenance strategy as the current first minimum equipment fault loss value CMAnd a second minimum equipment failure loss value CRAnd (4) any corresponding maintenance strategy is used for maintaining the power plant equipment by utilizing the final maintenance strategy.
By applying the method and the system for overhauling the power plant equipment, provided by the invention, the data of the overhauling record data is processed to obtain the distribution function of the failure mode, and the loss function generated after the failure mode occurs is combined with the distribution function of the failure mode to form a first function of the economic loss probability distribution to obtain a first minimum equipment failure loss value; the data of the maintenance record data are processed to obtain a distribution function of reliability, and a loss function generated after the fault mode occurs is combined with the distribution function of reliability to form a second function of economic loss probability distribution to obtain a second minimum equipment fault loss value; and stopping iteration by enabling the first minimum equipment fault loss value and the second minimum equipment fault loss value to meet a certain ratio, wherein the final maintenance strategy is any one detection strategy corresponding to the current first minimum equipment fault loss value and the current second minimum equipment fault loss value, and the final maintenance strategy is used for maintaining the power plant equipment, so that the accuracy is improved.
The method combines the maintenance strategy, the fault and the maintenance task, combines the reliability distribution function of the equipment fault mode with the economic loss when the fault mode occurs, obtains the economic loss probability distribution function of the equipment, converts the reliability distribution of the equipment into the economic loss distribution of the equipment fault, and meets the requirements of users. The invention also provides a device.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for overhauling power plant equipment according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention discloses a maintenance method of power plant equipment, which aims to solve the problems that the existing maintenance strategy is not high in relevance with faults and maintenance tasks, and the maintenance strategy is disconnected with reliability analysis.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for overhauling power plant equipment according to an embodiment of the present invention.
In a specific embodiment, the invention provides a method for overhauling power plant equipment, which comprises the following steps:
s11: acquiring maintenance record data of the power plant equipment in real time according to a preset period, wherein the maintenance record data comprises equipment, components, fault modes, maintenance duration and fault time;
the method comprises the steps that interface programs such as enterprise asset management can be collected according to different periods, the data comprise equipment maintenance record data, equipment defect data, work ticket data, equipment power generation plan data, equipment maintenance plan data and the like, all the maintenance record data are judged reasonably according to equipment, components, fault modes, maintenance duration and fault time, and repeated and invalid data are removed;
and for the data meeting the conditions, structured maintenance record data is formed according to equipment, components, fault modes, maintenance duration and fault time, a historical equipment fault sample database is formed, and the historical fault sample database of the equipment of the same type can be referred to for the newly-built unit.
S12: processing the data of the maintenance record to obtain a distribution function F of the failure moden(X), wherein n is the number of failure modes, and X is a vector consisting of equipment, components, a maintenance mode, maintenance time and failure time; for example, the distribution function of the failure mode can be obtained through a machine learning algorithm, such as a neuron algorithm or a random forest algorithm.
S13: processing the data of the maintenance record to obtain a distribution function f of reliabilityn(t), where n is the number of failure modes and t is time;
the overhaul record data can be processed by a statistical analysis and data fitting method, and the reliability distribution rule of the fault mode along with time is obtained.
S14: respectively carrying out distribution function F on the fault modes according to the obtained influence coefficients of the running environment and the state of the equipment on the reliability of the fault modesn(X) and distribution function f of reliabilityn(t) correcting to obtain the distribution function F of the corrected failure modesn' (X) and distribution function f of corrected reliabilityn'(t)；
Obtaining an influence coefficient of an equipment operation environment and a state on the reliability of an equipment fault mode, wherein the equipment operation environment comprises an environment where the equipment is located, such as indoor or outdoor environment, ambient temperature and the like; the state characteristics include the operating parameters of the equipment such as load, vibration and temperature.
S15 obtaining a loss function L generated after the equipment failure mode occursnWherein n is the number of failure modes; the losses include equipment losses and economic losses due to less power generation after equipment outage.
S16 according to the loss function LnAnd distribution function F of corrected failure modesn' (X) obtaining a first function of economic loss probability distribution
S17 according to the loss function LnAnd distribution function f of corrected reliabilityn' (t) obtaining a second function of economic loss probability distribution
S18 finding a first function L of the probability distribution of economic losses by a loop iteration methodMRelative first minimum equipment failure loss value CM；
S19: according to the input maintenance strategy data, a second function l is given to the probability distribution of economic lossRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a second minimum equipment fault loss value C by a loop iteration methodR；
S20: calculating r-C according to the same maintenance strategyR/CMAnd stopping iteration when r is 0.9-1.1, and finally, setting the maintenance strategy as the current first minimum equipment fault loss value CMAnd a second minimum equipment failure loss value CRAnd (4) any corresponding maintenance strategy is used for maintaining the power plant equipment by utilizing the final maintenance strategy.
And when the r value does not meet the condition, changing the initial input value of the maintenance strategy until the condition is met.
By applying the method and the system for overhauling the power plant equipment, provided by the invention, the data of the overhauling record data is processed to obtain the distribution function of the failure mode, and the loss function generated after the failure mode occurs is combined with the distribution function of the failure mode to form a first function of the economic loss probability distribution to obtain a first minimum equipment failure loss value; the data of the maintenance record data are processed to obtain a distribution function of reliability, and a loss function generated after the fault mode occurs is combined with the distribution function of reliability to form a second function of economic loss probability distribution to obtain a second minimum equipment fault loss value; and stopping iteration by enabling the first minimum equipment fault loss value and the second minimum equipment fault loss value to meet a certain ratio, wherein the final overhaul strategy is any one detection strategy corresponding to the current first minimum equipment fault loss value and the current second minimum equipment fault loss value, and the final overhaul strategy is used for overhauling the power plant equipment.
The method combines the maintenance strategy, the fault and the maintenance task, combines the reliability distribution function of the equipment fault mode with the economic loss when the fault mode occurs, obtains the economic loss probability distribution function of the equipment, converts the reliability distribution of the equipment into the economic loss distribution of the equipment fault, and meets the requirements of users.
Specifically, according to the input overhaul strategy data, a second function l is distributed to the economic loss probabilityRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a minimum equipment fault loss value C by a loop iteration methodRPreviously, the method further comprises:
obtaining the future of the unitPlanned power generation
Wherein p (t) is a function of the power and time of the unit, t1For optimum start time of overhaul, t2For the maintenance end time, the maintenance time length T is T ═ T2-t1；
Best start time t for overhaul1Planning the generation of electricity for the future of a crash
The time corresponding to the minimum;
the overhaul strategy data comprises equipment, components, overhaul modes and overhaul duration T.
The future planned generating capacity of the unit is an integral value of a function p (t) of the unit power and time in a specified time range, and the optimal start time t of maintenance1Should satisfy
Further, a second function l for the economic loss probability distributionRThe data processing is carried out to obtain an equipment fault loss distribution function LRThe method specifically comprises the following steps:
for the economic loss probability distributionRObtaining an equipment fault loss distribution function L based on overhaul policy data by a Monte Carlo simulation methodR。
For the economic loss probability distributionRAcquiring an equipment fault loss distribution function L obtained based on equipment, components, maintenance modes and maintenance duration by adopting a Monte Carlo simulation methodRSeeking a second minimum equipment fault loss value C from a plurality of maintenance strategies through a loop iteration methodR。
Specifically, the data processing is carried out on the overhaul record data to obtain a distribution function F of the fault moden(X), specifically including:
classifying the historical samples according to six attributes of equipment, components, fault modes, maintenance duration and fault time; for example: the equipment attributes may include an induced draft fan, a feed pump, a steam turbine, a condensate pump and the like; the parts may include bearings, bushings, mechanical seals, oil coolers, etc.; failure modes may include bearing vibration anomalies, bearing temperature anomalies, mechanical seal leakage, oil cooler fouling, etc.; the mode of maintenance may include inspection, maintenance, component replacement, etc.; the overhaul duration is the time taken to eliminate the determined failure mode and can be divided by hours; the failure time is the calendar time when the failure mode of the device occurred.
Taking the distribution rule of the fault mode as a problem to be solved, and respectively taking five attributes of equipment, components, a maintenance mode, maintenance duration and fault time as root nodes to construct a decision tree of the distribution rule of the fault mode;
respectively calculating information gains of five attributes of equipment, components, maintenance mode, maintenance duration and fault time to obtain a distribution function F of a fault moden(X)。
Or in one embodiment, the data processing is carried out on the maintenance record data to obtain a distribution function F of the failure moden(X), specifically including:
classifying the historical samples according to six attributes of equipment, components, fault modes, maintenance duration and fault time;
respectively calculating information gains of five attributes of equipment, components, a maintenance mode, maintenance duration and fault time, and taking a decision tree constructed by the attribute with the maximum information gain as a decision tree of a fault mode distribution rule;
acquiring a distribution function F of a failure mode by adopting an integrated learning algorithm on the constructed decision tree with five attributesn(X)。
Here and above decision tree algorithm may adopt decision tree algorithms such as D3, C4.5, etc., and selection of the decision tree algorithm may be performed according to actual needs, and an integrated learning algorithm such as an averaging method, a voting method, a learning method, etc. is integrated. The number of n depends on the number of failure modes, and X is a vector consisting of 5 attributes of equipment, components, a maintenance mode, maintenance duration and failure time.
Further, data processing is carried out on the overhaul record data to obtain a distribution function f of reliabilityn(t), specifically including:
carrying out Weibull distribution/normal distribution/Gaussian distribution on the maintenance record data for data fitting to obtain a distribution function f of reliabilityn(t)。
Based on the above method embodiment, the present invention further provides a maintenance system of a power plant device, which is compared with the above method embodiment, and the system includes:
the maintenance record data acquisition module is used for acquiring maintenance record data of the power plant equipment in real time according to a preset period, wherein the maintenance record data comprises equipment, components, a fault mode, a maintenance mode, maintenance time and fault time;
a failure mode distribution function processing module for processing the data of the maintenance record to obtain a failure mode distribution function Fn(X), wherein n is the number of failure modes, and X is a vector consisting of equipment, components, a maintenance mode, maintenance time and failure time;
a reliability distribution function processing module for processing the data of the maintenance record to obtain a reliability distribution function fn(t); wherein n is the number of failure modes, and t is time;
an influence coefficient correction module for respectively performing distribution function F on the fault mode according to the obtained influence coefficients of the equipment operating environment and the state on the reliability of the fault moden(X) and distribution function f of reliabilityn(t) correcting to obtain the distribution function F of the corrected failure modesn' (X) and distribution function f of corrected reliabilityn'(t)；
A loss function obtaining module for obtaining a loss function L generated after the device failure mode occursnWherein n is the number of failure modes;
an economic loss probability distribution first function processing module for processing the economic loss probability distribution according to the loss function LnAnd distribution function F of corrected failure modesn' (X) obtaining a first function of economic loss probability distribution
A second function processing module for economic loss probability distribution according to the loss function LnAnd distribution function f of corrected reliabilityn' (t) obtaining a second function of economic loss probability distribution
A first minimum equipment failure loss value calculation module for searching a first function L related to the economic loss probability distribution by a loop iteration methodMRelative first minimum equipment failure loss value CM；
A second minimum equipment fault loss value calculation module for calculating a second function l of the economic loss probability distribution according to the input maintenance strategy dataRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a second minimum equipment fault loss value C by a loop iteration methodR；
A ratio calculation module for calculating r ═ C according to the same maintenance strategyR/CMAnd stopping iteration when r is 0.9-1.1, and finally, setting the maintenance strategy as the current first minimum equipment fault loss value CMAnd a second minimum equipment failure loss value CRAnd (4) any corresponding maintenance strategy is used for maintaining the power plant equipment by utilizing the final maintenance strategy.
Specifically, the system further comprises:
the maintenance time calculation module is used for acquiring the future planned generated energy of the unit
Wherein p (t) is a function of the power and time of the unit, t1For optimum start time of overhaul, t2For the maintenance end time, the maintenance time length T is T ═ T2-t1(ii) a Best start time t for overhaul1Planning the generation of electricity for the future of a crash
The time corresponding to the minimum; the overhaul strategy data comprises equipment, components, overhaul modes and overhaul duration T.
Further, the second minimum device fault loss value calculation module is specifically configured to:
distribution function l for equipment fault lossRObtaining an equipment fault loss distribution function L based on overhaul policy data by a Monte Carlo simulation methodR。
Further, the failure mode distribution function processing module is specifically configured to:
classifying the historical samples according to six attributes of equipment, components, fault modes, maintenance duration and fault time;
taking the distribution rule of the fault mode as a problem to be solved, and respectively taking five attributes of equipment, components, a maintenance mode, maintenance duration and fault time as root nodes to construct a decision tree of the distribution rule of the fault mode;
respectively calculating information gains of five attributes of equipment, components, maintenance mode, maintenance duration and fault time to obtain a distribution function F of a fault moden(X)。
The present invention also provides an apparatus comprising a memory and a processor, wherein:
a memory for storing a computer program;
a processor for executing a computer program for implementing a method of servicing a power plant as in any one of the embodiments described above.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of servicing a power plant, the method comprising:
acquiring maintenance record data of power plant equipment in real time according to a preset period, wherein the maintenance record data comprises equipment, components, fault modes, maintenance duration and fault time;
processing the data of the maintenance record to obtain a distribution function F of a failure moden(X), wherein n is the number of failure modes, and X is a vector consisting of the equipment, the component, the overhaul mode, the overhaul duration, and the failure time;
processing the data of the maintenance record to obtain a distribution function f of reliabilityn(t), wherein n is the number of failure modes and t is time;
respectively carrying out distribution function F on the fault modes according to the obtained influence coefficients of the running environment and the state of the equipment on the reliability of the fault modesn(X) and a distribution function f of said reliabilitiesn(t) correcting to obtain the distribution function F of the corrected failure modesn' (X) and distribution function f of corrected reliabilityn'(t)；
Obtaining L a loss function generated after an equipment failure mode occursnWherein n is the number of failure modes;
according to the loss function LnAnd a distribution function F of said corrected failure modesn' (X) obtaining a first function of economic loss probability distribution
According to the loss function LnAnd a distribution function f of said corrected reliabilitiesn' (t) obtaining a second function of economic loss probability distribution
Seeking L a first function corresponding to the economic loss probability distribution by a loop iteration methodMRelative first minimum equipment failure loss value CM；
According to the input maintenance strategy data, carrying out a second function l on the economic loss probability distributionRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a second minimum equipment fault loss value C by a loop iteration methodR；
Calculating r-C according to the same maintenance strategyR/CMStopping iteration when r is 0.9-1.1, and setting the final maintenance strategy to be the current first minimum equipment fault loss value CMAnd said second minimum equipment failure loss value CRAnd any corresponding maintenance strategy is utilized to carry out maintenance on the power plant equipment.
2. A power plant overhaul method according to claim 1, wherein the economic loss probability distribution is subjected to a second function/, based on the input overhaul strategy dataRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a second minimum equipment fault loss value C by a loop iteration methodRPreviously, the method further comprises:
obtaining the future planned generating capacity of the unit
Wherein p (t) is a function of the power and time of the unit, t1For optimum start time of overhaul, t2For the maintenance end time, the maintenance time length T is T2-t1；
Best start time t for overhaul1For planning the generation of electricity in the future of the unit
The time corresponding to the minimum;
the overhaul strategy data comprises the equipment, the components, the overhaul mode and the overhaul duration T.
3. The power plant equipment servicing method of claim 1, wherein the second function/' of the economic loss probability distributionRThe data processing is carried out to obtain an equipment fault loss distribution function LRThe method specifically comprises the following steps:
according to the second function l of the economic loss probability distributionRAnd obtaining an equipment fault loss distribution function L by the overhaul strategy data through a Monte Carlo simulation methodR。
4. The power plant equipment overhaul method of claim 1, wherein the overhaul record data is subjected to data processing to obtain a distribution function F of failure modesn(X), specifically including:
classifying historical samples according to six attributes of the equipment, the component, the fault mode, the overhaul duration and the fault time;
taking the distribution rule of the fault mode as a problem to be solved, and respectively taking the five attributes of the equipment, the component, the overhaul mode, the overhaul duration and the fault time as root nodes to construct a decision tree of the distribution rule of the fault mode;
respectively calculating the information gains of the five attributes of the equipment, the component, the overhaul mode, the overhaul duration and the fault time to obtain a distribution function F of a fault moden(X)。
5. A power plant overhaul method according to claim 1, wherein the overhaul record data is subjected to data processing to obtain a distribution function f of reliabilityn(t), specifically including:
carrying out Weibull distribution/normal distribution/Gauss on the maintenance record dataData fitting is carried out on the distribution to obtain a distribution function f of reliabilityn(t)。
6. A system for servicing power plant equipment, the system comprising:
the maintenance record data acquisition module is used for acquiring maintenance record data of the power plant equipment in real time according to a preset period, wherein the maintenance record data comprises equipment, components, a fault mode, a maintenance mode, maintenance time and fault time;
a fault mode distribution function processing module for processing the data of the maintenance record to obtain a fault mode distribution function Fn(X), wherein n is the number of failure modes, and X is a vector consisting of the equipment, the component, the overhaul mode, the overhaul duration, and the failure time;
a reliability distribution function processing module for processing the maintenance record data to obtain a reliability distribution function fn(t); wherein n is the number of the failure modes, and t is time;
an influence coefficient correction module for respectively performing distribution function F on the fault mode according to the obtained influence coefficients of the equipment operating environment and the state on the reliability of the fault moden(X) and a distribution function f of said reliabilitiesn(t) correcting to obtain the distribution function F of the corrected failure modesn' (X) and distribution function f of corrected reliabilityn'(t)；
A loss function obtaining module for obtaining a loss function L generated after the device failure mode occursnWherein n is the number of failure modes;
an economic loss probability distribution first function processing module for processing the economic loss probability distribution according to the loss function LnAnd a distribution function F of said corrected failure modesn' (X) obtaining a first function of economic loss probability distribution
A second function processing module for economic loss probability distribution according to the loss function LnAnd a distribution function f of said corrected reliabilitiesn' (t) obtaining a second function of economic loss probability distribution
A first minimum equipment failure loss value calculation module for searching a first function L of the economic loss probability distribution by a loop iteration methodMRelative first minimum equipment failure loss value CM；
A second minimum equipment fault loss value calculation module for calculating a second function l of the economic loss probability distribution according to the input maintenance strategy dataRThe data processing is carried out to obtain an equipment fault loss distribution function LRAnd seeking a second minimum equipment fault loss value C by a loop iteration methodR；
A ratio calculation module for calculating r ═ C according to the same maintenance strategyR/CMStopping iteration when r is 0.9-1.1, and setting the final maintenance strategy to be the current first minimum equipment fault loss value CMAnd said second minimum equipment failure loss value CRAnd any corresponding maintenance strategy is utilized to carry out maintenance on the power plant equipment.
7. The power plant equipment servicing system of claim 6, the system further comprising:
the maintenance time calculation module is used for acquiring the future planned generated energy of the unit
Wherein p (t) is a function of the power and time of the unit, t1For optimum start time of overhaul, t2For the maintenance end time, the maintenance time length T is T2-t1(ii) a Best start time t for overhaul1For planning the generation of electricity in the future of the unit
The time corresponding to the minimum; the overhaul strategy data comprises the equipment, the components, the overhaul mode and the overhaul duration T.
8. The power plant equipment overhaul system of claim 6, wherein the second minimum equipment fault loss value calculation module is specifically configured to:
according to the second function l of the economic loss probability distributionRAnd obtaining an equipment fault loss distribution function L by the overhaul strategy data through a Monte Carlo simulation methodR。
9. The power plant equipment overhaul system of claim 6, wherein the failure mode distribution function processing module is specifically configured to:
classifying historical samples according to six attributes of the equipment, the component, the fault mode, the overhaul duration and the fault time;
taking the distribution rule of the fault mode as a problem to be solved, and respectively taking the five attributes of the equipment, the component, the overhaul mode, the overhaul duration and the fault time as root nodes to construct a decision tree of the distribution rule of the fault mode;
respectively calculating the information gains of the five attributes of the equipment, the component, the overhaul mode, the overhaul duration and the fault time to obtain a distribution function F of a fault moden(X)。
10. An apparatus comprising a memory and a processor, wherein:
the memory is used for storing a computer program;
the processor is configured to execute the computer program to implement the method of servicing the power plant equipment of any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010223969.0A CN111445043B (en) | 2020-03-26 | 2020-03-26 | Maintenance method, system and device for power plant equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010223969.0A CN111445043B (en) | 2020-03-26 | 2020-03-26 | Maintenance method, system and device for power plant equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111445043A true CN111445043A (en) | 2020-07-24 |
| CN111445043B CN111445043B (en) | 2023-07-11 |
Family
ID=71647991
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010223969.0A Active CN111445043B (en) | 2020-03-26 | 2020-03-26 | Maintenance method, system and device for power plant equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111445043B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113627628A (en) * | 2021-08-13 | 2021-11-09 | 西安热工研究院有限公司 | Method for calculating repeated maintenance times of same fan on line based on work ticket |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130283104A1 (en) * | 2012-04-24 | 2013-10-24 | International Business Machines Corporation | Maintenance planning and failure prediction from data observed within a time window |
| CN104636826A (en) * | 2015-01-27 | 2015-05-20 | 中国石油化工股份有限公司 | Method for optimizing reliability and maintenance strategy of chemical refining equipment |
| CN109726833A (en) * | 2018-12-29 | 2019-05-07 | 华润电力技术研究院有限公司 | Dynamic adjustment maintenance policy method, apparatus, terminal and computer storage medium |
| CN109740930A (en) * | 2018-12-29 | 2019-05-10 | 华润电力技术研究院有限公司 | Maintenance policy is formulated and Reliability assessment method, terminal and computer storage medium |
-
2020
- 2020-03-26 CN CN202010223969.0A patent/CN111445043B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130283104A1 (en) * | 2012-04-24 | 2013-10-24 | International Business Machines Corporation | Maintenance planning and failure prediction from data observed within a time window |
| CN104636826A (en) * | 2015-01-27 | 2015-05-20 | 中国石油化工股份有限公司 | Method for optimizing reliability and maintenance strategy of chemical refining equipment |
| CN109726833A (en) * | 2018-12-29 | 2019-05-07 | 华润电力技术研究院有限公司 | Dynamic adjustment maintenance policy method, apparatus, terminal and computer storage medium |
| CN109740930A (en) * | 2018-12-29 | 2019-05-10 | 华润电力技术研究院有限公司 | Maintenance policy is formulated and Reliability assessment method, terminal and computer storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 袁峻等: "一种优化可靠性与经济性的变压器检修决策方法", 《南方电网技术》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113627628A (en) * | 2021-08-13 | 2021-11-09 | 西安热工研究院有限公司 | Method for calculating repeated maintenance times of same fan on line based on work ticket |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111445043B (en) | 2023-07-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Martorell et al. | Maintenance modeling and optimization integrating human and material resources | |
| Mogre et al. | A decision framework to mitigate supply chain risks: an application in the offshore-wind industry | |
| CN111079351A (en) | Power distribution network probability load flow obtaining method and device considering wind power uncertainty | |
| Carlos et al. | Particle Swarm Optimization of safety components and systems of nuclear power plants under uncertain maintenance planning | |
| Pereira et al. | Risk assessment using bayesian belief networks and analytic hierarchy process applicable to jet engine high pressure turbine assembly | |
| Doroshuk | Prospects and efficiency measurement of artificial intelligence in the management of enterprises in the energy sector in the era of Industry 4.0 | |
| Rediske et al. | Management of operation and maintenance practices in photovoltaic plants: Key performance indicators | |
| CN111445043B (en) | Maintenance method, system and device for power plant equipment | |
| Crespo Márquez et al. | The maintenance management framework: A practical view to maintenance management | |
| Ogaji et al. | Novel approach for improving power-plant availability using advanced engine diagnostics | |
| CN111445042B (en) | Maintenance method, system and device for power plant equipment | |
| Morison | On-line dynamic security assessment using intelligent systems | |
| CN111725811A (en) | Determining safety constrained optimal power flow | |
| Tena-García et al. | Implementing data reduction strategies for the optimal design of renewable energy systems | |
| Kamiya et al. | Adaptive-edge search for power plant start-up scheduling | |
| Ding et al. | Study on steam turbine fault diagnosis and maintenance service grid system | |
| CN111445041B (en) | Maintenance method, system and device for power plant equipment | |
| Liu et al. | Research on a case-based decision support system for aircraft maintenance review board report | |
| Jun-guang et al. | Notice of Retraction: Method study of software project risk management | |
| Aqlan et al. | Defect analytics in a high-end server manufacturing environment | |
| Chen et al. | Quality assurance inspection in supply chain production systems | |
| Eddouh et al. | Maximizing Wind Turbine Efficiency: Monte Carlo Simulation Based on Cost and Energy Loss Analysis for Optimal Preventive Maintenance | |
| Liu et al. | Robust scheduling of remanufacturing processes for the repair of turbine blades | |
| Chien et al. | A systematic approach to determine the optimal maintenance policy for an automated manufacturing system | |
| Wang et al. | Study on Decision—Making Method of Single Product Maintenance Considering Maintenance Deterioration Effect |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240408 Address after: 518066 Room 201, building A, No. 1, Qian Wan Road, Qianhai Shenzhen Hong Kong cooperation zone, Shenzhen, Guangdong (Shenzhen Qianhai business secretary Co., Ltd.) Patentee after: Shenzhen goes out new knowledge property right management Co.,Ltd. Country or region after: China Address before: 523808 Room 308, unit 1, building 18, no.6, Libin Road, Songshanhu Park, Dongguan City, Guangdong Province Patentee before: CR POWER TECHNOLOGY INSTITUTE Co.,Ltd. Country or region before: China |