CN109460567A - A kind of maintaining method and system of multi-part equipment - Google Patents
A kind of maintaining method and system of multi-part equipment Download PDFInfo
- Publication number
- CN109460567A CN109460567A CN201811109270.0A CN201811109270A CN109460567A CN 109460567 A CN109460567 A CN 109460567A CN 201811109270 A CN201811109270 A CN 201811109270A CN 109460567 A CN109460567 A CN 109460567A
- Authority
- CN
- China
- Prior art keywords
- equipment
- maintenance
- model
- random
- threshold value
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention belongs to plant maintenance correlative technology fields, it discloses the maintaining methods and system of a kind of multi-part equipment, method includes the following steps: the Condition Monitoring Data of each component and the original state monitoring data after each maintenance when (1) acquisition equipment operation, and establish operation and the maintenance record of equipment；(2) the random degradation model and random maintenance quality model of each component of equipment are established respectively based on operation and maintenance record, and establishes the maintenance optimization model of equipment based on the optimizing index of all random degradation models, random maintenance quality model and selection；(3) go out the best abnormal point threshold value, best-cast failure threshold value and best monitoring interval of equipment based on maintenance optimization model solution, then periodic monitoring is carried out to equipment according to best monitoring interval, while equipment is safeguarded according to best abnormal point threshold value and best-cast failure threshold value.The present invention effectively increases equipment performance driving economy and service life of equipment, and with strong applicability, flexibility is higher.
Description
Technical field
The invention belongs to plant maintenance correlative technology fields, more particularly, to a kind of maintaining method of multi-part equipment
And system.
Background technique
With the development of the modernization of industry, large-scale and complicated device using increasingly extensive, large-scale and complicated device often wraps
Include the multiple component system of each subsystem, functional component and components, each subsystem, functional component and components not only in structure and
It functionally has differences, but also there are various dependences, so that the maintenance measures of large-scale and complicated device are extremely multiple
It is miscellaneous.
With advanced monitoring, the fast development of diagnosis and Predicting Technique, maintenance or condition based maintenance based on state
The pay attention to day by day of (Condition-Based Maintenance, CBM) by people, becoming reduces equipment fault risk and fortune
Row cost, the important means for improving availability.The actual maintenance situation of complexity based on large-scale and complicated device, equipment is mostly
After maintenance, degenerate state makes moderate progress for " non-perfect maintenance ", i.e. equipment, but fail to be restored to completely new original state,
And after safeguarding the state that is restored to be often it is random, there is uncertainty.In some maintaining methods based on state, according to
The degenerative process of equipment is divided into normal operating phase by equipment degenerative character, failure postpones two stages, the boundary in two stages
Point is critical level.In failure delayed phase, equipment starts defect occur but still can work, when Condition Monitoring Data is more than
When failure level, device fails.Since existing CBM maintenance measures method is usually just for failure delayed phase, and
Critical level is rule of thumb or software emulation determines, typically more conservative, causes maintenance activity more frequent, increases and stop
Machine loss and maintenance cost.In addition, the status level of equipment is also influenced by maintenance activity and service intervals time, rather than it is complete
The actual maintenance effect of U.S. maintenance, it is often random.Therefore, equipment after maintenance, imitate with maintenance by status level
The variation of fruit and change at random, and influence subsequent maintaining method in turn.It is existing due to the various dependences in multiple component system
Some for the CBM maintenance measures method of unit system do not consider multipart dependence and maintenance effects not really
It is qualitative, it can not directly apply in multiple component system.
In conclusion in practical projects, the application of existing CBM maintenance measures method is limited by very large, right
It is more in number of components or need while the maintenance measures problem of the complicated multiple component system that considers other influences factor, at present
Also lack effective solution scheme.Correspondingly, there is the maintenances for developing a kind of preferable multi-part equipment of applicability for this field
The technical need of method and system.
Summary of the invention
Aiming at the above defects or improvement requirements of the prior art, the present invention provides a kind of maintaining methods of multi-part equipment
And system, based on the maintenance feature of existing multi-part equipment, study and devise a kind of multi-part equipment maintaining method and
System.The maintaining method has comprehensively considered the imperfection and uncertainty of maintenance effects, at the same consider monitoring interval and
The influence of abnormal point level effectively increases equipment performance driving economy and service life of equipment, with strong applicability, flexibility compared with
It is high.
To achieve the above object, according to one aspect of the present invention, a kind of maintaining method of multi-part equipment is provided, it should
Maintaining method mainly comprises the steps that
(1) the original state monitoring data after acquiring the Condition Monitoring Data of each component when equipment is run and safeguarding every time,
And establish operation and the maintenance record of equipment；
(2) it establishes the random degradation model of each component of equipment respectively based on the operation and maintenance record and ties up at random
Quality model is protected, and is established based on the optimizing index of all random degradation models, the random maintenance quality model and selection
The maintenance optimization model of equipment；
(3) the best abnormal point threshold value of equipment, best-cast failure threshold value and best are gone out based on the maintenance optimization model solution
Monitoring interval then carries out periodic monitoring to equipment according to the best monitoring interval, while according to the best abnormal point threshold
Value and the best-cast failure threshold value safeguard equipment.
Further, the mathematic(al) representation of the random degradation model are as follows:
In formula, biAnd ciIt is constant,For the stochastic variable for obeying exponential distribution.
Further, the mathematic(al) representation of the random maintenance quality model are as follows:
yi(t)=εi
εiNormal Distribution:Wherein, μiFor the average value of normal distyribution function, σiFor normal distribution
The standard deviation of function, μiAnd σiIt can solve and obtain according to the operation and maintenance record.
Further, the optimizing index include equipment operation rate and usable remaining life.
Further, the maintenance optimization model is based on the random degradation model and the random maintenance quality model
It is obtained by vector differential derivation.
Further, in step (3), when part of appliance deterioration level is more than the best abnormal point threshold value of equipment, to setting
Standby component carries out preventive maintenance；When part of appliance deterioration level is more than the best-cast failure threshold value of equipment, then to part of appliance
It is replaced；When equipment deterioration level is lower than best abnormal point threshold value, then equipment continues to run.
Other side according to the invention, provides a kind of maintenance system of multi-part equipment, and the maintenance system is adopted
Multi-part equipment is safeguarded with the maintaining method of multi-part equipment as described above.
Further, the maintenance system includes data acquisition module, quality analysis module, optimization module and maintenance measures
Module, the Condition Monitoring Data of each component and initial after maintenance every time when the data acquisition module is used to acquire equipment operation
Condition Monitoring Data, and establish operation and the maintenance record of equipment；The quality analysis module by statisticalling analyze for being built
Found the random degradation model and random maintenance quality model of each component；The optimization module is used to be based on the random degeneration mould
Type establishes the maintenance optimization model of equipment with selected optimizing index；The optimization module is also used to according to the maintenance optimization
Model calculates the best abnormal point threshold value, best-cast failure threshold value and best monitoring interval of equipment；The maintenance measures module is used
Optimal maintenance scheme is provided online in operating status based on equipment.
Further, according to maintenance data and monitoring data to described random under the quality analysis module is also used to online
The model parameter of degradation model and the random maintenance quality model is estimated.
In general, through the invention it is contemplated above technical scheme is compared with the prior art, it is provided by the invention more
The maintaining method and system of components/devices mainly have the advantages that
1. the original state monitoring data after acquiring the Condition Monitoring Data of each component when equipment is run and safeguarding every time, and
Operation and the maintenance record for establishing equipment establish moving back at random for each component of equipment based on the operation and maintenance record respectively
Change model and random maintenance quality model, the random degradation model of each component is all based on maintenance of the equipment after maintenance
Actual conditions that quality changes at random and establish so that maintenance optimization model is more in line with the real use state of equipment, thus
Actual maintenance decision problem is solved, plant maintenance accuracy and service life of equipment are improved.
2. establishing equipment based on the optimizing index of all random degradation models, the random maintenance quality model and selection
Maintenance optimization model, fully considered the influence of dependence between each component to the maintenance measures of equipment, it is of the invention
Maintenance optimization model provides effective solution scheme for the maintenance measures of multi-part equipment.
3. going out the best abnormal point threshold value, best-cast failure threshold value and best prison of equipment based on the maintenance optimization model solution
Interval is surveyed, periodic monitoring then is carried out to equipment according to the best monitoring interval, while according to the best abnormal point threshold value
And the best-cast failure threshold value safeguards equipment, is conducive to rationally determine maintenance time point and safeguards movable frequency, has
Effect reduces shutdown loss and maintenance cost.
4. the maintenance optimization model is to pass through arrow based on the random degradation model and the random maintenance quality model
The problem of amount differential derivation obtains, so avoids most solutions in majorized function.
Detailed description of the invention
Fig. 1 is the flow chart of the maintaining method for the multi-part equipment that first embodiment of the invention provides.
Fig. 2 is the working state schematic representation of the maintenance system for the multi-part equipment that first embodiment of the invention provides.
Fig. 3 is the random maintenance quality mould that the maintaining method for the multi-part equipment that second embodiment of the invention provides is related to
Type schematic diagram.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
Not constituting a conflict with each other can be combined with each other.
Please refer to Fig. 1 and Fig. 2, the maintaining method for the multi-part equipment that first embodiment of the invention provides mainly include with
Lower step:
Step 1 acquires the Condition Monitoring Data of each component when equipment is run and the original state after maintenance monitors number every time
According to, and establish operation and the maintenance record of equipment.
Step 2, based on it is described operation and maintenance record establish respectively equipment each component random degradation model and with
Machine maintenance quality model, and the optimizing index based on all random degradation models, the random maintenance quality model and selection come
Establish the maintenance optimization model of equipment.
Specifically, random degradation model y is established according to the Condition Monitoring Data of equipmenti(t)=fi(t) estimate under doubling
Model parameter；Random quality model y is established according to maintenance record simultaneouslyi(t)=εi,Mould is estimated under line
Shape parameter μiAnd σi.Wherein, yiIt (t) is the practical deterioration level of i-th (1≤i≤m) a component of the equipment, t is the component
Runing time.Preventive maintenance model, i.e., random maintenance quality model are yi(t)=εi, wherein εiIt is component i by preventing
Property maintenance after deterioration level, εiFor stochastic variable (0≤εi≤Li, LiFor the abnormal point threshold value of part of appliance i), indicate component
State after maintenance has uncertainty, and Normal DistributionμiFor the average value of normal distyribution function,
σiFor the standard deviation of normal distyribution function.
The optimizing index (for establishing the maintenance optimization model of equipment) includes the operation rate of equipment (in the unit time
Operation rate) and available remaining life；The maintenance optimization model optimizes best decision variable simultaneously under optimizing index, such as
Best monitoring interval, best abnormal point threshold value and best-cast failure threshold value.
In present embodiment, the maintenance optimization model is based on the random degradation model and the random maintenance quality
The problem of model is obtained by vector differential derivation, so avoids most solutions in majorized function.
Step 3, based on the maintenance optimization model solution go out the best abnormal point threshold value of equipment, best-cast failure threshold value and
Best monitoring interval then carries out periodic monitoring to equipment according to the best monitoring interval, while according to the best exception
Point threshold value and the best-cast failure threshold value safeguard equipment.
Periodic monitoring is carried out to equipment using best monitoring interval, when part of appliance deterioration level is more than the best different of equipment
Often when point threshold value, preventive maintenance is carried out to part of appliance；When part of appliance deterioration level is more than the best-cast failure threshold value of equipment
When, then part of appliance is replaced；When equipment deterioration level is lower than best abnormal point threshold value, then equipment continues to run.Tool
Body, equipment through degeneration be more than abnormal point threshold value after need to carry out preventive maintenance, after being more than best-cast failure threshold value, need into
Row replacement.The preventive maintenance is non-perfect maintenance, possesses random maintenance quality, indicates that equipment cannot be restored after maintenance
To newest, but it is restored between newest some uncertain state with before maintenance.
First embodiment of the invention additionally provides a kind of maintenance system of multi-part equipment, and the maintenance system is using such as
The maintaining method of the upper multi-part equipment safeguards multi-part equipment comprising data acquisition module, quality analysis
Module, optimization module and maintenance measures module.
Wherein, the data acquisition module is used to acquire the Condition Monitoring Data of each component and maintenance every time when equipment operation
Original state monitoring data afterwards, and establish operation and the maintenance record of equipment.The quality analysis module is for passing through statistics
It analyzes to establish the random degradation model and random maintenance quality model of each component.The optimization module be used for based on it is described with
Machine degradation model establishes the maintenance optimization model of equipment with selected optimizing index.The optimization module is also used to according to
Maintenance optimization model calculates the best abnormal point threshold value, best-cast failure threshold value and best monitoring interval of equipment.The maintenance is determined
Plan module provides optimal maintenance scheme for operating status based on equipment online, and the maintenance scheme includes the side of continuing to run
Case, preventive maintenance scheme and Replacing Scheme.
Referring to Fig. 3, the maintaining method for the multi-part equipment that second embodiment of the invention provides, it is suitable for various machines
Electric equipment.The electromechanical equipment includes multiple monitored components, and such as generating set and turbine, the quantity of component is m.
The maintaining method for the multi-part equipment that second embodiment of the invention provides mainly comprises the steps that
S1, data acquisition.
Specifically, the Condition Monitoring Data of each component and the monitoring of the original state after maintenance every time when the operation of acquisition equipment
Data, and establish operation and the maintenance record of equipment.The operation and maintenance record further include the maintenance cost of each component.
S2 establishes random degradation model and random maintenance quality model.
Specifically, it is recorded based on the operation and maintenance for statistical analysis to establish random degradation model and random maintenance
Quality model.In Practical Project, all parts degenerative process of equipment has randomness and monotonic increase type, and selecting has at random
The degradation model of parameter carrys out the degenerative process of fitting unit.For component i, the mathematic(al) representation of random degradation model isWherein, biAnd ciIt is constant；For the stochastic variable for obeying exponential distribution.Since equipment is by safeguarding
Afterwards, some nondeterministic statement of the practical deterioration level of equipment between completely new state and current degradation level, constructs random dimension
Protect the mathematic(al) representation of quality model are as follows:
yi(t)=εi
Wherein, εiNormal DistributionμiFor the average value of normal distyribution function；σiFor normal distribution
The standard deviation of function；μiAnd σiIt can be recorded according to operation and maintenance, be determined by following steps:
It is recorded to obtain deterioration level ε according to operation and maintenanceiSample be (εi,1,εi,2,...,εi,g), g is sample number,
It is obtained according to moment estimation method:
Wherein,For stochastic variable εiH rank moment of the orign,For institute collecting sample εi?
H rank moment of the orign.
IfThenWherein,For sample (εi,1,εi,2,...,εi,g) average valueFor
Sample (εi,1,εi,2,...,εi,g) second-order moment around mean.
By similarly method, recorded to obtain random degradation model parameter according to operation and maintenanceSample beIt is availableProbability density function:
Wherein, λiFor rate parameter, for parameter lambdai,
It is for sampleAverage value.
S3 establishes the maintenance measures optimization mould of equipment based on the random degradation model established and the optimizing index of selection
Type.
Selected optimizing index, and establish the maintenance measures Optimized model of equipment.The optimizing index can be the list of equipment
The remaining life θ (τ, F, L) of position time operating cost ω (τ, F, L) or equipment.Illustrated respectively below:
When optimizing index is the unit time operating cost ω (τ, F, L) of equipment, the maintenance measures Optimized model pair
ω (τ, F, L) is minimized, unit time operating cost ω (τ, F, L) are as follows:
In formula, C (τ, F, L) is the operating cost of equipment；T (τ, F, L) is the runing time of equipment；F=(F1,...,Fm)、
L=(Li,...,Lm) be respectively m component failure threshold and abnormal point threshold value；CP,iFor the preventive maintenance expense of i-th of component
With；Pi,kτ{ PM } is the probability that i-th of component implements preventive maintenance in monitoring point k τ；Cf,iFor the replacement cost of i-th of component
With；Pi,kτ{ CM } is the probability that i-th of component implements replacement in monitoring point k τ.Pi,kτ{ PM } and Pi,kτ{ CM } can pass through following public affairs
Formula is calculated:
In formula,Corresponding to stochastic variable in i-th of component degradation modelProbability-distribution function, MiForSample
This space；LiFor the failure threshold of i-th of component；For what is given Respectively component i when
Between tk-1 and tk cumulative density function. It can be derived by following formula
It arrives:
If component i without preventive maintenance,
If component i passes through preventive maintenance,
Similarly,Cumulative probability density function may be expressed as:The runing time of equipment are as follows:
Wherein, Pkτ{ CM } is the probability that whole equipment breaks down in k τ point, can be counted according to specific apparatus logic figure
The probability that all parts break down is calculated to obtain.Therefore, the probabilistic quantity of all maintenance measures Optimized models is obtained.
When optimizing index be equipment remaining life when, maintenance measures Optimized model be so that equipment remaining life θ (τ,
F, L) it maximizes, the remaining life θ (τ, F, L) of equipment are as follows:
It can be in the hope of using the maximum residual service life as the maintenance measures Optimized model θ (τ, F, L) of index by integral.
S4 calculates best monitoring interval, best abnormal point threshold value and the best-cast failure threshold value for obtaining equipment.
Since maintenance measures Optimized model has display expression formula, the vector form τ, F=of optimized variable are used
(F1,...,Fm), L=(Li,...,Lm) it can avoid the case where most solutions occur.Thus, it can to Optimized model expression formula derivation
:
By the best decision variable to the available Optimized model of vector independent variable differential, i.e., best monitoring is spaced, most
Good abnormal point threshold value and best-cast failure threshold value.
S5 formulates maintenance measures, and putting maintenance into practice activity.
Specifically, status monitoring is carried out to equipment according to optimal maintenance measures value.When the degeneration water for monitoring part of appliance i
It is flat to reach the horizontal F of best-cast failurei *When, which is replaced.When the deterioration level of component i is more than best abnormal point threshold value
And it is lower than Fi *When, preventive maintenance is carried out to component.
The dimension for the multi-part equipment that second embodiment of the invention is provided by taking a specific electromechanical equipment as an example below
Maintaining method is further elaborated:
Degraded data acquisition experiment is carried out to a multi-part equipment of certain enterprise, is tested according in ISO230-2:2014
Standard operation execute.Equipment component containing there are six, while recording the deterioration level after the maintenance of its all parts.
According to data collected, random degradation model and random maintenance quality model are established, by the above process to model
Parameter estimate under line, as a result respectively as shown in table 1, table 2:
The random degradation model parameter of table 1
The random maintenance quality model parameter of table 2
| Component | Cf,i | Cp,i | The average value of ε | The variance of ε |
| Component 01 | 1.2 | 4.1 | 2.1 | 10 |
| Component 02 | 0.5 | 1.7 | 1.5 | 12 |
| Component 03 | 3.7 | 11.2 | 2.5 | 11 |
| Component 04 | 1.5 | 5.2 | 2.0 | 9 |
| Component 05 | 0.8 | 1.9 | 2 | 14 |
| Component 06 | 0.3 | 1.0 | 1 | 10 |
According to Tables 1 and 2, established based on selected optimizing index equipment maintenance measures Optimized model min ω (τ, F,
) and max θ (τ, F, L) L.Optimizing decision value is acquired using the method for step S4, as shown in table 3.In table 3, when the unit of equipment
Between operating cost be 35.8, the remaining life of equipment is 183.
The optimum results of 3 maintenance measures of table
Maintenance measures method of the invention and other maintenance measures methods are compared, select minimum operating cost respectively
It is optimizing index with the maximum residual service life, as a result ginseng is shown in Table 4.Wherein, other methods 1 are not consider that maintenance effects are uncertain
The maintenance strategy of property, other parameters are consistent with the present invention；Other methods 2 are the maintenance strategy based on the age, it is meant that equipment will
It can periodically be maintained；Maintenance strategy after the failure of other methods 3, only carries out replacement maintenance when equipment fault.As known from Table 4,
The maintenance cost of maintenance measures method of the invention is minimum, and can reach the maximum residual service life.
4 maintenance measures method of table compares
Second embodiment of the invention also provides a kind of maintenance system of multi-part equipment, and the maintenance system is using as above
The maintaining method of the multi-part equipment safeguards equipment.The maintenance system includes data acquisition module, quality point
Analyse module, optimization module and maintenance measures module, the quality analysis module under online according to maintenance data and monitoring number
Estimate according to the model parameter to the random degradation model and the random maintenance quality model.The data acquisition module
Other activities of block, the quality analysis model, the optimization module and the maintenance measures module carry out on line.
The maintaining method and system of multi-part equipment provided by the invention, the maintaining method acquisition equipment each portion when running
The monitoring data of original state after the Condition Monitoring Data of part and every time maintenance, and operation and the maintenance record of equipment are established,
Simultaneously based on it is described operation and maintenance record it is for statistical analysis to establish random degradation model and random maintenance quality model, by
This considers the imperfection and uncertainty of maintenance effects, effectively improves maintenance quality and accuracy, and adaptability is stronger,
And improve the service life of equipment.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should all include
Within protection scope of the present invention.
Claims (9)
1. a kind of maintaining method of multi-part equipment, which is characterized in that method includes the following steps:
(1) the original state monitoring data after acquiring the Condition Monitoring Data of each component when equipment is run and safeguarding every time, and build
Erect standby operation and maintenance record；
(2) it establishes the random degradation model of each component of equipment respectively based on the operation and maintenance record and safeguards matter at random
Model is measured, and equipment is established based on the optimizing index of all random degradation models, the random maintenance quality model and selection
Maintenance optimization model；
(3) go out the best abnormal point threshold value, best-cast failure threshold value and best monitoring of equipment based on the maintenance optimization model solution
Interval, then according to the best monitoring interval to equipment progress periodic monitoring, while according to the best abnormal point threshold value and
The best-cast failure threshold value safeguards equipment.
2. the maintaining method of multi-part equipment as described in claim 1, it is characterised in that: the mathematics of the random degradation model
Expression formula are as follows:
In formula, biAnd ciIt is constant, θiFor the stochastic variable for obeying exponential distribution.
3. the maintaining method of multi-part equipment as claimed in claim 2, it is characterised in that: the random maintenance quality model
Mathematic(al) representation are as follows:
yi(t)=εi
εiNormal Distribution:Wherein, μiFor the average value of normal distyribution function, σiFor normal distyribution function
Standard deviation, μiAnd σiIt can solve and obtain according to the operation and maintenance record.
4. the maintaining method of multi-part equipment as described in claim 1, it is characterised in that: the optimizing index includes equipment
Run rate and usable remaining life.
5. the maintaining method of multi-part equipment as described in claim 1, it is characterised in that: the maintenance optimization model is to be based on
What the random degradation model and the random maintenance quality model were obtained by vector differential derivation.
6. the maintaining method of multi-part equipment as described in claim 1, it is characterised in that: in step (3), when part of appliance moves back
When changing best abnormal point threshold value of the level more than equipment, preventive maintenance is carried out to part of appliance；When part of appliance deterioration level
More than equipment best-cast failure threshold value when, then part of appliance is replaced；When equipment deterioration level is lower than best abnormal point threshold
When value, then equipment continues to run.
7. a kind of maintenance system of multi-part equipment, it is characterised in that: the maintenance system is using any one of claim 1-6 institute
The maintaining method for the multi-part equipment stated safeguards multi-part equipment.
8. the maintenance system of multi-part equipment as claimed in claim 7, it is characterised in that: the maintenance system includes that data are adopted
Collect module, quality analysis module, optimization module and maintenance measures module, when the data acquisition module is for acquiring equipment operation
Original state monitoring data after the Condition Monitoring Data of each component and every time maintenance, and establish the operation and maintenance note of equipment
Record；The quality analysis module be used for established by statisticalling analyze each component random degradation model and random maintenance quality
Model；The optimization module is used to establish the maintenance optimization of equipment based on the random degradation model and selected optimizing index
Model；The optimization module is also used to calculate the best abnormal point threshold value of equipment, best mistake according to the maintenance optimization model
Imitate threshold value and best monitoring interval；The maintenance measures module provides optimal maintenance for operating status based on equipment online
Scheme.
9. the maintenance system of multi-part equipment as claimed in claim 8, it is characterised in that: the quality analysis module is also used to
The model of the random degradation model and the random maintenance quality model is joined according to maintenance data and monitoring data under online
Number is estimated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811109270.0A CN109460567B (en) | 2018-09-21 | 2018-09-21 | Maintenance method and system of multi-component device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811109270.0A CN109460567B (en) | 2018-09-21 | 2018-09-21 | Maintenance method and system of multi-component device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109460567A true CN109460567A (en) | 2019-03-12 |
| CN109460567B CN109460567B (en) | 2021-02-09 |
Family
ID=65606917
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811109270.0A Expired - Fee Related CN109460567B (en) | 2018-09-21 | 2018-09-21 | Maintenance method and system of multi-component device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109460567B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111027719A (en) * | 2019-11-14 | 2020-04-17 | 东华大学 | Maintenance optimization method for multi-component system state opportunity |
| CN111105522A (en) * | 2019-12-10 | 2020-05-05 | 郑州嘉晨电器有限公司 | Vehicle health prediction system and method |
| CN113359449A (en) * | 2021-06-04 | 2021-09-07 | 西安交通大学 | Aeroengine double-parameter index degradation maintenance method based on reinforcement learning |
| US11651627B2 (en) | 2019-11-28 | 2023-05-16 | Oracle International Corporation | Sensor network for optimized maintenance schedule |
| CN116451912A (en) * | 2023-06-19 | 2023-07-18 | 西北工业大学 | Complex electromechanical system performance evaluation method and system under condition of influence of replacement |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102033750A (en) * | 2010-12-21 | 2011-04-27 | 东莞市高鑫机电科技服务有限公司 | Architecture method for SOA-based intelligent enterprise equipment maintenance system and system |
| CN102096770A (en) * | 2011-01-25 | 2011-06-15 | 上海交通大学 | Network-based performance degradation assessment method of rotary machine |
-
2018
- 2018-09-21 CN CN201811109270.0A patent/CN109460567B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102033750A (en) * | 2010-12-21 | 2011-04-27 | 东莞市高鑫机电科技服务有限公司 | Architecture method for SOA-based intelligent enterprise equipment maintenance system and system |
| CN102096770A (en) * | 2011-01-25 | 2011-06-15 | 上海交通大学 | Network-based performance degradation assessment method of rotary machine |
Non-Patent Citations (1)
| Title |
|---|
| 刘勤明: "基于状态监测信息的设备在线健康预测及维护优化研究"", 《中国博士学位论文全文数据库信息科技辑》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111027719A (en) * | 2019-11-14 | 2020-04-17 | 东华大学 | Maintenance optimization method for multi-component system state opportunity |
| US11651627B2 (en) | 2019-11-28 | 2023-05-16 | Oracle International Corporation | Sensor network for optimized maintenance schedule |
| CN111105522A (en) * | 2019-12-10 | 2020-05-05 | 郑州嘉晨电器有限公司 | Vehicle health prediction system and method |
| CN113359449A (en) * | 2021-06-04 | 2021-09-07 | 西安交通大学 | Aeroengine double-parameter index degradation maintenance method based on reinforcement learning |
| CN113359449B (en) * | 2021-06-04 | 2023-01-03 | 西安交通大学 | Aeroengine double-parameter index degradation maintenance method based on reinforcement learning |
| CN116451912A (en) * | 2023-06-19 | 2023-07-18 | 西北工业大学 | Complex electromechanical system performance evaluation method and system under condition of influence of replacement |
| CN116451912B (en) * | 2023-06-19 | 2023-09-19 | 西北工业大学 | Complex electromechanical system performance evaluation method and system under condition of influence of replacement |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109460567B (en) | 2021-02-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109460567A (en) | A kind of maintaining method and system of multi-part equipment | |
| Moghaddass et al. | A hierarchical framework for smart grid anomaly detection using large-scale smart meter data | |
| Baker et al. | Review of delay-time OR modelling of engineering aspects of maintenance | |
| KR102118670B1 (en) | System and method for management of ict infra | |
| KR20220149581A (en) | Hybrid risk model for maintenance optimization and systems for implementing these methods | |
| US10444746B2 (en) | Method for managing subsystems of a process plant using a distributed control system | |
| CN109992895B (en) | Method for extracting and predicting equipment performance degradation trend | |
| CN109033499A (en) | A kind of aero-engine method for predicting residual useful life of multistage consistency check | |
| CN115454778B (en) | Timing sequence index abnormity intelligent monitoring system in large-scale cloud network environment | |
| US20210301644A1 (en) | System and Methods for Developing and Deploying Oil Well Models to Predict Wax/Hydrate Buildups for Oil Well Optimization | |
| Hu et al. | A general model, estimation, and procedure for modeling recurrent failure process of high-voltage circuit breakers considering multivariate impacts | |
| Huang et al. | Remaining useful life estimation for systems with abrupt failures | |
| Marvasti et al. | An enterprise dynamic thresholding system | |
| Barbieri et al. | Sensor-based degradation prediction and prognostics for remaining useful life estimation: Validation on experimental data of electric motors | |
| Ma et al. | Condition-based maintenance optimization for multicomponent systems under imperfect repair—based on RFAD model | |
| Ferraro et al. | A novel approach for predictive maintenance combining GAF encoding strategies and deep networks | |
| El Hayek et al. | Optimizing life cycle cost of complex machinery with rotable modules using simulation | |
| CN115081647A (en) | Industrial intelligent instrument fault pre-diagnosis method based on Bayesian network model | |
| CN112883549B (en) | Establishment method of condition-dependent maintenance model considering random impact influence | |
| BRUZZONE et al. | Innovative solutions based on simulation combined with optimization techniques for maintenance service management within complex systems | |
| CN112380759B (en) | Smart electric meter service life prediction method based on deep learning and CoxPH model | |
| CN115795999B (en) | Early warning method for abnormal performance of long-term service pumped storage unit | |
| Wang et al. | Reinforcement learning based predictive maintenance for a machine with multiple deteriorating yield levels | |
| Kumar et al. | Measuring software reliability: a fuzzy model | |
| Liu et al. | KQis-driven QoE anomaly detection and root cause analysis in cellular networks |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210209 Termination date: 20210921 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |