CN109636072A - A kind of elevator car door system preventive maintenance decision Multipurpose Optimal Method based on non-bad Sorting Genetic Algorithm - Google Patents
A kind of elevator car door system preventive maintenance decision Multipurpose Optimal Method based on non-bad Sorting Genetic Algorithm Download PDFInfo
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Abstract
The elevator car door system preventive maintenance decision Multipurpose Optimal Method based on non-bad Sorting Genetic Algorithm that the invention discloses a kind of, it collects and counts method includes the following steps: 1) carry out historical failure record to elevator car door system critical component, the two parameter Weibull distribution form parameter and scale parameter in elevator car door system critical component service life are obtained with the method for best linear unbiased estimation；2) it calculates the failure rate function of elevator car door system critical component and is maximized, obtain the failure rate function of door system；3) the average maintenance rate objective function in the door system service life period and average coherence objective function in the single preventive maintenance period are constructed, door system preventive maintenance decision optimization model is established；4) using non-bad Sorting Genetic Algorithm, system shortsightedness maintenance decision Optimized model carries out multiple-objection optimization solution on the door, obtains optimal preventive maintenance number and preventive maintenance cycle length.
Description
Technical field
The present invention relates to equipment preventive maintenance strategy study technical fields, and in particular to one kind is calculated based on non-bad sequence heredity
The elevator car door system preventive maintenance decision Multipurpose Optimal Method of method.
Background technique
It is higher and higher to the security reliability requirement of elevator operation as China's elevator ownership increases year by year, accordingly
Maintenance requirement is also higher and higher.Important component of the elevator car door system as elevator has and contacts frequent, building ring with passenger
The feature that border is complicated, failure is high-incidence.And door system once breaks down, and gently then elevator is caused to be shut down, it is heavy then cause " pendant ladder " thing
Part brings serious harm to the safety of life and property of passenger.It is low as far as possible in maintenance cost how by scientific maintenance policy
While, it improves elevator car door system operational reliability, reduce failure rate, be of great significance to guaranteeing that elevator high quality is run.
For elevator car door system, existing tradition correction maintenance strategy not can guarantee equipment operational reliability, and repair at
This is high, and maintenance efficiency is low.And the preventive maintenance strategy generallyd use at present, it is set although can reduce to a certain extent
Standby failure rate, but maintenance cycle length has certain blindness, thus the problem of bringing " maintenance is superfluous " or " maintenance is insufficient ".
In existing electrical category equipment Preventive Maintenance strategy study, need to carry out good fitting to the failure rate of equipment first, after this is
The precondition of continuous maintenance policy Optimal flattening.And elevator car door system is made of several subassemblies, the performance of crucial subassembly
Deterioration will lead to system performance decline, therefore how carry out good fit being research door system prevention dimension to the failure rate of door system
Repair the critical issue of decision.In terms of repairing objective optimization, existing research is that will repair the multiple targets such as rate, reliability mostly
Optimization problem processing is single-object problem to simplify processing, to be extremely difficult in terms of the property taken into account between target optimal.
Therefore the multiple-objection optimization of research maintenance rate and reliability, finds out globally optimal solution, determines to the preventive maintenance of elevator car door system
Plan is of great significance.
Summary of the invention
Goal of the invention: it is an object of the present invention to provide a kind of elevator car door system preventive maintenances based on non-bad Sorting Genetic Algorithm
Decision Multipurpose Optimal Method, the science that can be realized elevator car door system preventive maintenance strategy are formulated, and maintenance cost is being reduced
Meanwhile door system reliability of operation is improved, to improve the efficiency of maintenance, solve existing research mostly by maintenance cost
The processing of the multi-objective optimization questions such as rate, reliability is that single-object problem is handled with simplifying, in terms of the property taken into account between target
It is extremely difficult to optimal problem.
A kind of technical solution: elevator car door system preventive maintenance decision multiple target based on non-bad Sorting Genetic Algorithm of the present invention
Optimization method, method includes the following steps:
1) it carries out historical failure record to elevator car door system critical component to collect and count, with best linear unbiased estimation
Method obtain the elevator car door system critical component service life two parameter Weibull distribution form parameter and scale parameter；
2) it calculates the failure rate function of elevator car door system critical component and is maximized, obtain the failure rate letter of door system
Number；
3) it constructs in the average maintenance rate objective function and single preventive maintenance period in the door system service life period
Average coherence objective function establishes door system preventive maintenance decision optimization model；
4) using non-bad Sorting Genetic Algorithm, system shortsightedness maintenance decision Optimized model carries out multiple-objection optimization solution on the door,
Obtain optimal preventive maintenance number and preventive maintenance cycle length.
Further, the step 2) specifically includes the following steps:
21) assume that door system has K critical component, the service life of each critical component obeys two parameter Weibull distribution,
Then the failure rate of k-th of critical component may be expressed as:
In formula, mkAnd ηkRespectively indicate the form parameter and dimensional parameters of k-th of crucial subassembly service life Weibull distribution, 1
≤ k≤K, t indicate the working time；
22) assume that door system every operation a period of time just carries out a preventive maintenance to door system, so that each critical component
Performance restored；Prevention replacement is carried out at the end of door system life cycle, so that each critical component restores as new；It is single pre-
Each critical component does not break down simultaneously in anti-maintenance cycle, and light maintenance is carried out if breaking down, and light maintenance does not change unit failure
Rate；
23) failure rate of k-th of critical component within i-th of preventive maintenance period may be expressed as:
In formula, i indicates that preventive maintenance periodicity, t indicate the runing time of door system in the adjacent period of preventive maintenance twice,
T indicates that preventive maintenance cycle length, N indicate total preventive maintenance number, and wherein n-th is prevention replacement；λk,i(t) it indicates i-th
Failure rate of k-th of critical component in t moment in a preventive maintenance period；θkIndicate the decline of k-th critical component failure rate because
Son；Δk,jIndicate service age reduction factor of k-th of critical component in jth time preventive maintenance, Δk,j=ak j, akFor preventive maintenance
Put into Dynamic gene, 0 < ak< 1；
24) failure rate of door system may be expressed as:
λi(t)=max { λk,i(t)|1≤k≤K,1≤i≤N}
In formula, λi(t) failure rate of door system t moment within i-th of preventative maintenance period is indicated, K is door system pass
Key member sum.
Further, the step 3) specifically includes the following steps:
31) the average maintenance rate objective function in the door system service life period may be expressed as:
In formula, C (N, T) indicates average maintenance rate in life cycle, CfIndicate that door system breaks down when progress every time
The average cost of light maintenance, CpIndicate the average cost of preventive maintenance, CrIndicate the average cost of prevention replacement, tpIndicate prevention dimension
The average time-consuming repaired, trIndicate the average time-consuming of prevention replacement, N indicates total preventive maintenance number, fiIndicate door system at i-th
The number of stoppages occurred in the preventative maintenance period:
In formula, λi(t) failure rate of door system t moment within i-th of preventative maintenance period is indicated；
32) it calculates average coherence of each critical component of door system within i-th of preventive maintenance period and is minimized, obtain
To average coherence of the door system within i-th of preventive maintenance period:
ri=min { rk,i|1≤k≤K,1≤i≤N}
In formula, rk,iIndicate average coherence of k-th of crucial subassembly within i-th of preventive maintenance period, riIndicate door
Average coherence of the system within i-th of preventive maintenance period, rk(t) Reliability Function of k-th of critical component is indicated:
In formula, mkAnd ηkRespectively indicate the form parameter and dimensional parameters of k-th of crucial subassembly service life Weibull distribution；
33) door system average coherence objective function within the single preventive maintenance period indicates are as follows:
R (N, T)=min { ri|1≤i≤N}
In formula, r (N, T) indicates the minimum value of door system average coherence within each preventive maintenance period；
34) the preventive maintenance decision optimization model of door system is expressed as:
Further, non-bad Sorting Genetic Algorithm system shortsightedness maintenance decision Optimized model on the door is used in the step 4)
It optimizes, wherein decision variable is that preventive maintenance times N and preventive maintenance cycle length T, door system average maintenance take
Rate C and average coherence minimum value r is as two optimization aims.What the non-final iteration optimizing of bad Sorting Genetic Algorithm obtained
Pareto optimal solution set takes certain constraint, filters out the optimal solution of the condition of satisfaction, obtains the optimal preventive maintenance of door system
Times N and preventive maintenance cycle length T.
Caused by of the invention the utility model has the advantages that
1, can failure rate to elevator car door system and Reliability Function carry out it is good fit, to be conducive to subsequent dimension
It repairs policy optimization and formulates.
2, it constructs in the average maintenance rate objective function and single preventive maintenance period in the door system service life period
Average coherence objective function establishes door system preventive maintenance decision optimization model, is carried out using non-bad Sorting Genetic Algorithm more
Objective optimization, can be realized global optimizing, and formulated maintenance policy can satisfy that maintenance cost is low and the high requirement of reliability.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is the algorithm flow chart using non-bad Sorting Genetic Algorithm multiple-objection optimization.
Specific embodiment
The present invention is described further with reference to the accompanying drawings and examples:
As shown in Figure 1, a kind of more mesh of elevator car door system preventive maintenance decision based on non-bad Sorting Genetic Algorithm of the present invention
Optimization method is marked, method includes the following steps:
1) critical component for determining elevator car door system first, by carrying out historical failure note to elevator car door system critical component
Record is collected and statistics, and the two parameter prestige cloth in elevator car door system critical component service life is obtained with the method for best linear unbiased estimation
Your profile shape parameter and scale parameter, shown in the critical component the following table 1 determined in the present embodiment:
Table 1, elevator car door system critical component
2) it calculates the failure rate function of elevator car door system critical component and is maximized, obtain the failure rate letter of door system
Number, specifically includes the following steps:
21) assume that door system has K critical component, the service life of each critical component obeys two parameter Weibull distribution,
Then the failure rate of k-th of critical component may be expressed as:
In formula, mkAnd ηkRespectively indicate the form parameter and dimensional parameters of k-th of crucial subassembly service life Weibull distribution, 1
≤ k≤K, t indicate the working time；
22) assume that door system every operation a period of time just carries out a preventive maintenance to door system, so that each critical component
Performance restored；Prevention replacement is carried out at the end of door system life cycle, so that each critical component restores as new；It is single pre-
Each critical component does not break down simultaneously in anti-maintenance cycle, and light maintenance is carried out if breaking down, and light maintenance does not change unit failure
Rate；
23) failure rate of k-th of critical component within i-th of preventive maintenance period may be expressed as:
In formula, i indicates that preventive maintenance periodicity, t indicate the runing time of door system in the adjacent period of preventive maintenance twice,
T indicates that preventive maintenance cycle length, N indicate total preventive maintenance number, and wherein n-th is prevention replacement；λk,i(t) it indicates i-th
Failure rate of k-th of critical component in t moment in a preventive maintenance period；θkIndicate the decline of k-th critical component failure rate because
Son；Δk,jIndicate service age reduction factor of k-th of critical component in jth time preventive maintenance, Δk,j=ak j, akFor preventive maintenance
Put into Dynamic gene, 0 < ak< 1；Each critical component failure rate relevant parameter determined in the present embodiment is as shown in table 2 below:
Table 2, critical component failure rate relevant parameter
24) failure rate of door system may be expressed as:
λi(t)=max { λk,i(t)|1≤k≤K,1≤i≤N}
In formula, λi(t) failure rate of door system t moment within i-th of preventative maintenance period is indicated, K is door system pass
Key member is total, K=4 in the present embodiment.
3) it constructs in the average maintenance rate objective function and single preventive maintenance period in the door system service life period
Average coherence objective function establishes door system preventive maintenance decision optimization model.Specifically includes the following steps:
31) the average maintenance rate objective function in the door system service life period may be expressed as:
In formula, C (N, T) indicates average maintenance rate in life cycle, CfIndicate that door system breaks down when progress every time
The average cost of light maintenance, CpIndicate the average cost of preventive maintenance, CrIndicate the average cost of prevention replacement, tpIndicate prevention dimension
The average time-consuming repaired, trIndicate the average time-consuming of prevention replacement, N indicates total preventive maintenance number, fiIndicate door system at i-th
The number of stoppages occurred in the preventative maintenance period:
In formula, λi(t) failure rate of door system t moment within i-th of preventative maintenance period is indicated；In the present embodiment with
Door system average maintenance rate relevant parameter is as shown in table 3 below:
Table 3 and door system average maintenance rate relevant parameter
32) it calculates average coherence of each critical component of door system within i-th of preventive maintenance period and is minimized, obtain
To average coherence of the door system within i-th of preventive maintenance period:
ri=min { rk,i|1≤k≤K,1≤i≤N}
In formula, rk,iIndicate average coherence of k-th of crucial subassembly within i-th of preventive maintenance period, riIndicate door
Average coherence of the system within i-th of preventive maintenance period, rk(t) Reliability Function of k-th of critical component is indicated:
In formula, mkAnd ηkRespectively indicate the form parameter and dimensional parameters of k-th of crucial subassembly service life Weibull distribution；
33) door system average coherence objective function within the single preventive maintenance period indicates are as follows:
R (N, T)=min { ri|1≤i≤N}
In formula, r (N, T) indicates the minimum value of door system average coherence within each preventive maintenance period；
34) the preventive maintenance decision optimization model of door system is expressed as:
4) using non-bad Sorting Genetic Algorithm, system shortsightedness maintenance decision Optimized model carries out multiple-objection optimization solution on the door,
Wherein decision variable is preventive maintenance times N and preventive maintenance cycle length T, door system average maintenance rate C and average reliable
Minimum value r is spent as two optimization aims.The Pareto optimal solution centralized procurement that the non-final iteration optimizing of bad Sorting Genetic Algorithm obtains
Certain constraint is taken, the optimal solution of the condition of satisfaction is filtered out, obtains the optimal preventive maintenance times N of door system and preventive maintenance week
Phase length T.That takes in the present embodiment is constrained under conditions of door system average coherence minimum value r is not less than 0.85, takes
Solution concentrates the smallest individual of average maintenance rate.The algorithm flow of non-bad Sorting Genetic Algorithm multiple-objection optimization is as shown in Figure 2.
Claims (3)
1. a kind of elevator car door system preventive maintenance decision Multipurpose Optimal Method based on non-bad Sorting Genetic Algorithm, feature exist
In, method includes the following steps:
1) it carries out historical failure record to elevator car door system critical component to collect and count, with the side of best linear unbiased estimation
The two parameter Weibull distribution form parameter and scale parameter in method acquisition elevator car door system critical component service life；
2) it calculates the failure rate function of elevator car door system critical component and is maximized, obtain the failure rate function of door system；
3) it constructs the average maintenance rate objective function in the door system service life period and is averaged in the single preventive maintenance period
Reliability objective function establishes door system preventive maintenance decision optimization model；
4) using non-bad Sorting Genetic Algorithm, system shortsightedness maintenance decision Optimized model carries out multiple-objection optimization solution on the door, obtains
Optimal preventive maintenance number and preventive maintenance cycle length.
2. a kind of more mesh of elevator car door system preventive maintenance decision based on non-bad Sorting Genetic Algorithm according to claim 1
Mark optimization method, it is characterised in that: the step 2) specifically includes the following steps:
21) assume that door system has K critical component, the service life of each critical component obeys two parameter Weibull distribution, then kth
The failure rate of a critical component may be expressed as:
In formula, mkAnd ηkRespectively indicate the form parameter and dimensional parameters of k-th of crucial subassembly service life Weibull distribution, 1≤k
≤ K, t indicate the working time；
22) assume that door system every operation a period of time just carries out a preventive maintenance to door system, so that the property of each critical component
It can be restored；Prevention replacement is carried out at the end of door system life cycle, so that each critical component restores as new；Single prevention dimension
It repairs each critical component in the period not break down simultaneously, light maintenance is carried out if breaking down, light maintenance does not change unit failure rate；
23) failure rate of k-th of critical component within i-th of preventive maintenance period may be expressed as:
In formula, i indicates that preventive maintenance periodicity, t indicate the runing time of door system in the adjacent period of preventive maintenance twice, T table
Show that preventive maintenance cycle length, N indicate total preventive maintenance number, wherein n-th is prevention replacement；λk,i(t) it indicates at i-th
Failure rate of k-th of critical component in t moment in the preventive maintenance period；θkIndicate k-th of critical component failure rate decline factor；
Δk,jIndicate service age reduction factor of k-th of critical component in jth time preventive maintenance, Δk,j=ak j, akFor preventive maintenance throwing
Enter Dynamic gene, 0 < ak< 1；
24) failure rate of door system may be expressed as:
λi(t)=max { λk,i(t)|1≤k≤K,1≤i≤N}
In formula, λi(t) failure rate of door system t moment within i-th of preventative maintenance period is indicated, K is door system critical component
Sum.
3. a kind of more mesh of elevator car door system preventive maintenance decision based on non-bad Sorting Genetic Algorithm according to claim 1
Mark optimization method, it is characterised in that: the step 3) specifically includes the following steps:
31) the average maintenance rate objective function in the door system service life period may be expressed as:
In formula, C (N, T) indicates average maintenance rate in life cycle, CfIt indicates to carry out light maintenance when door system breaks down every time
Average cost, CpIndicate the average cost of preventive maintenance, CrIndicate the average cost of prevention replacement, tpIndicate the flat of preventive maintenance
It is time-consuming, trIndicate the average time-consuming of prevention replacement, N indicates total preventive maintenance number, fiIndicate that door system is preventative at i-th
The number of stoppages occurred in maintenance cycle:
In formula, λi(t) failure rate of door system t moment within i-th of preventative maintenance period is indicated；
32) it calculates average coherence of each critical component of door system within i-th of preventive maintenance period and is minimized, obtain door
Average coherence of the system within i-th of preventive maintenance period:
ri=min { rk,i|1≤k≤K,1≤i≤N}
In formula, rk,iIndicate average coherence of k-th of crucial subassembly within i-th of preventive maintenance period, riIndicate door system
Average coherence within i-th of preventive maintenance period, rk(t) Reliability Function of k-th of critical component is indicated:
In formula, mkAnd ηkRespectively indicate the form parameter and dimensional parameters of k-th of crucial subassembly service life Weibull distribution；
33) door system average coherence objective function within the single preventive maintenance period indicates are as follows:
R (N, T)=min { ri|1≤i≤N}
In formula, r (N, T) indicates the minimum value of door system average coherence within each preventive maintenance period；
34) the preventive maintenance decision optimization model of door system is expressed as:
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288109A (en) * | 2020-11-26 | 2021-01-29 | 上海电机学院 | Maintenance method for electric system of electric sweeping machine |
| CN112561091A (en) * | 2020-12-14 | 2021-03-26 | 东北大学 | Maintenance method and system for elevator mechanical parts |
| CN116664105A (en) * | 2023-05-31 | 2023-08-29 | 上海能源科技发展有限公司 | Marine wind turbine generator system based part state division and maintenance decision method and system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107229979A (en) * | 2017-04-17 | 2017-10-03 | 北京航空航天大学 | A kind of optimization method of repairable deteriorating system periodicity preventive maintenance strategy |
-
2019
- 2019-02-01 CN CN201910102612.4A patent/CN109636072B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107229979A (en) * | 2017-04-17 | 2017-10-03 | 北京航空航天大学 | A kind of optimization method of repairable deteriorating system periodicity preventive maintenance strategy |
Non-Patent Citations (2)
| Title |
|---|
| 吕言等: "基于非劣排序遗传算法的核电厂维修决策多目标优化方法研究", 《核动力工程》 * |
| 许蓉等: "基于威布尔模型的高校大型仪器设备预防维修周期研究", 《实验技术与管理》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288109A (en) * | 2020-11-26 | 2021-01-29 | 上海电机学院 | Maintenance method for electric system of electric sweeping machine |
| CN112288109B (en) * | 2020-11-26 | 2022-12-02 | 上海电机学院 | Maintenance method for electric system of electric sweeping machine |
| CN112561091A (en) * | 2020-12-14 | 2021-03-26 | 东北大学 | Maintenance method and system for elevator mechanical parts |
| CN112561091B (en) * | 2020-12-14 | 2024-02-02 | 东北大学 | Maintenance method and system for mechanical parts of elevator |
| CN116664105A (en) * | 2023-05-31 | 2023-08-29 | 上海能源科技发展有限公司 | Marine wind turbine generator system based part state division and maintenance decision method and system |
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