CN117035737A - Maintenance frequency optimization method based on equipment state uncertainty and relevance - Google Patents
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Abstract
The invention discloses a maintenance frequency optimization method based on equipment state uncertainty and relevance, which comprises the steps of firstly, establishing an equipment maintenance state diagram for predicting equipment state and aging and failure. Next, the system state is analyzed in association with the constituent devices, the system state is defined and a state space is determined, associating the system state with the device degradation condition. Then, a degradation model of the equipment degradation rate and the characteristic elements, an initial degradation state judgment matrix, and a maintenance investment and equipment degradation state transition correlation model are established. And finally, solving the optimization decision model by adopting a Monte Carlo method, and determining the optimal overhaul frequency combination of equipment in the system. The method can effectively reduce the overhaul cost of equipment and improve the decision accuracy and the decision efficiency of the power grid.
Description
Technical Field
The invention belongs to the field of power equipment overhaul, and particularly relates to an overhaul frequency optimization method based on equipment state uncertainty and relevance.
Background
Continuous aging of equipment is an important challenge for the grid enterprise to deal with. Preventive Maintenance (PM) is one of the most common types of maintenance activities, wherein tasks are performed according to prescribed criteria or predetermined time intervals. Such maintenance is intended to assess the risk of failure occurring, prevent failure or minimize the consequences of failure. Too little and too much inspection and maintenance may have adverse consequences as they may lead to a dramatic increase in early failure or post failure handling costs. The best equipment maintenance frequency is selected to improve the economy of equipment maintenance work and ensure the reliability of equipment, which is the topic of concern of the current power grid enterprises.
Current methods of equipment servicing frequency arrangements may be summarized as analytical methods, analog methods, or a combination of both. In analytical methods, the effects of degradation processes and maintenance are modeled mathematically, while simulation techniques (e.g., MCS) are used for other categories. Although it is easy to implement analytical methods, they often require some simplifying assumptions, which may lead to erroneous results. As systems have more dimensions, the complexity and computational burden may also increase, which may make these approaches impractical for large systems. On the other hand, the simulation method is applicable to complex problems. However, their main disadvantage is the long implementation time. The technical scheme adopted by the prior art can be analyzed by jin of watermelons, and the following defects and differences can be found to exist:
(1) These methods do not take into account the existing uncertainties and dependencies associated with repair and maintenance costs and durations and transition probabilities, which may overestimate or underestimate the results obtained. Obviously, such results may lead to unsatisfactory planning or operational decisions.
(2) Some schemes estimate the degradation state before performing inspection or maintenance activities by increasing the number of states, but increase the required parameters and computational complexity.
(3) The relationship between the degradation rate of the device and the operation mode of the device is not considered in the vast majority of technical solutions in the prior art.
Aiming at the defects existing in the prior art, the invention
Object of the Invention
The invention aims to solve the defects existing in the prior art, and discloses a maintenance frequency optimization method based on equipment state uncertainty and relevance, which is characterized in that the optimization model and the calculation method are subjected to the following requirements: (1) The model needs to give more accurate results by taking into account the above uncertainties and dependencies. (2) There is a need to propose a method for estimating the degradation state of a current device without increasing the complexity of the model. (3) The optimization objective is to consider the economy of the system and to obtain the best combination of the overhaul rates of the system equipment.
The invention considers the correlation and uncertainty of the maintenance cost and the duration time in the maintenance process and the correlation degradation phenomenon between equipment components, aims to find the optimal maintenance rate of preventive maintenance, can effectively reduce the maintenance cost of equipment and improves the decision accuracy and the decision efficiency of a power grid.
Disclosure of Invention
The invention provides a maintenance frequency optimization method based on equipment state uncertainty and relevance, which comprises the following steps:
step 1, establishing an equipment maintenance state diagram for predicting equipment states and aging and failure faults;
step 2, analyzing the association of the system state and the component equipment, defining the system state, determining a state space, and associating the system state with the equipment degradation condition; the state space refers to a set containing all possible states of the system and the constituent devices;
step 3, establishing a degradation model of equipment degradation rate and characteristic elements, an initial degradation state judgment matrix and a maintenance investment and equipment degradation state transition correlation model;
and 4, solving the optimization decision model by adopting a Monte Carlo method, and determining the optimal overhaul frequency combination of equipment in the system.
Preferably, the construction of the device maintenance state diagram in step 1 includes two parts of establishing a constituent element and a characterization meaning, wherein the constituent element includes a device state D representing a degradation level of the device 1 、D 2 、…、D n Respectively corresponding to equipment state evaluation grades, wherein the equipment state evaluation grades are classified according to grading interval grades;
inspection and secondary and primary maintenance status, denoted I, M and MM, respectively;
aging-related faults AF and random faults RF;
repair rates mu for random and aging-related faults, respectively AF Inspection rate mu RF Deterioration rate gamma 1 -γ n Conversion rate lambda from last deteriorated state to aging-related failure state 1 -λ N-1 Conversion rate lambda to random failure state n And lambda (lambda) 0 ；
P Di 、P Mi 、P MMi And P IMMi Respectively represent transition probabilities between different statesThe rate.
It is assumed that by performing the secondary maintenance M, the deteriorated state will decrease or improve one state or remain unchanged, i.e. if the device is in the deteriorated state D 2 In the process of performing secondary maintenance M 2 After that, the deteriorated state may be D 1 、D 2 Or D 3 ；
Assuming that by performing the primary maintenance MM, the new degraded state is any state before or remains unchanged, i.e. if the device is in degraded state D 3 In executing the main maintenance MM 3 After that, the deteriorated state is D 1 、D 2 Or D 3 ；
The representation meaning means that the equipment maintenance state diagram combines the equipment state, the overhaul mode and the state transfer relation on one diagram for analysis, the evolution condition of equipment fault risks under different overhaul strategies is shown, the equipment state is continuously deteriorated along with equipment aging, but overhaul can improve the equipment state, and further the system risk can be reduced; and optimizing equipment maintenance frequency based on the equipment maintenance state diagram, and searching for an optimal maintenance scheme.
Preferably, in step 2, the random process of the system state is described as setting the system as a system consisting of n components, and assuming that each component is represented by four states, D1, D2, D3 and F, respectively representing a perfect, a slight fault, a severe fault and a damaged device, then all device states are combined to have a total of 4n possible states, and Z (t) represents the system state at time t, which is associated with time, and is determined by the states of all devices in the system, and the state space represents a set of all system states.
Preferably, in step 3, in the process of establishing the degradation model of the equipment degradation rate and the characteristic elements, the equipment degradation model is established based on the equipment characteristic elements, and for the circuit breaker CB, there are four states of D1, D2, D3 and F, and CB contact wear increases after each closing and opening operation, whether there is or is not current; assuming contact wear monitoring data is available when needed, i.e., calculating the maximum contact wear at any stage of each operation, recorded as a percentage and stored in memory, CB can be continuously monitored, where 0% represents new contacts and 100% represents fully worn contacts; from the maximum calculated wear, the degradation state of the contact is defined as follows:
normal state S1: contact wear of more than or equal to 0 and less than or equal to 60 percent
Note state S2:60% < contact wear less than or equal to 95%
Severe state S3:95% < contact wear
As the contact is degraded, the CB degradation rate increases, and the degradation model is modeled by increasing the shape parameter of the weibull distribution, expressed as shown in formula (1):
wherein Mean is the Mean value of the lifetime of the device, β and η are shape and scale parameters, f represents the gamma function; the shape parameter β and the scale parameter η and the average value μ and the standard deviation σ satisfy the relationship as shown in equation (2):
and (3) solving an equation (2) by using a simulated bifurcation algorithm to obtain a shape parameter beta, and then solving an equation (3) by using the obtained shape parameter beta to obtain a scale parameter eta, wherein the equation (3) is as follows:
further preferably, in step 3, the initial degradation state is modeled, specifically, based on the service age of CB, and the degradation states S1, S2 or S3 of the new device, the middle device or the old device and their contact conditions are considered, so as to estimate the initial degradation situation when the device is not maintained, and establish an initial degradation state judgment matrix.
Further preferably, the initial degradation state judgment matrix and the overhaul input in step 3, and the equipment degradation state transition correlation model are characterized by the formula (4):
wherein r refers to a correlation coefficient, and x and y respectively represent two variables.
Further preferably, step 3 further includes modeling equipment cost and availability, including: the total annual cost TC is selected to characterize the overhaul investment cost of a typical equipment system, and comprises an expected operation cost TEAC and an expected power outage cost TEDC, wherein the expected operation cost TEAC is the overhaul cost, the expected power outage cost TEDC is the profit loss of unsold power outage, and the calculation method is respectively shown in formulas (5) - (7):
TC＝TEAC+TDEC (7)，
s in i Representing the status of the device, C I 、C M 、C MM And C F Representing the costs of each inspection, secondary maintenance, primary maintenance and repair, respectively; pi is a vector containing fourteen state probability elements, and represents that the device is in the state probability, pi= [ pi D ] 1 ΠD 2 ΠD 3 ΠAFΠRF 1 ΠRF 2 ΠRF 3 ΠI 1 ΠI 2 ΠI 3 ΠM 2 ΠMM 2 ΠM 3 ΠMM 3 ]Calculated by formula (8):
wherein Q is a 14×14 state transition matrix, and element Q ij The calculation method is shown as a formula (9):
wherein P is SiSj And d Si Respectively represent slave states S i (i.e. maintenance or inspection status) transition to S j Probability and state S of (2) i Average duration of (2); l is an interrupted load, C LPi Hourly profit, representing reduction in interrupt unit load for different equipment states, is characterized as shown in equation (10):
namely, D1, D2 and D3 in 14 states do not generate power outage cost, and other states calculate power outage cost, C LP Indicating an hourly profit with reduced interrupt unit load; f (S) i ) Representing the number of times the device is in the Si state for one year;
the device availability modeling is expressed as shown in equation (11):
preferably, the solution to the optimization decision model in step 4 by using the monte carlo method is based on a half markov chain of MCS, and the optimal solution is solved by using 95% confidence available to the system as a reference, which comprises the following specific sub-steps:
s41, determining input; namely, determining system parameters including the age of CB and contact condition S1, S2 or S3, probability density function of probability input data, correlation parameters, number of deteriorated states, and maximum value gamma of inspection rate max The method comprises the steps of carrying out a first treatment on the surface of the The probability density function of the probability input data includes conversion rate, repair and maintenance costs, and duration;
step S42, estimating the degradation state D1, D2, or D3 of the device;
step S43, determining a Weibull parameter of the degradation rate of each device according to the electric shock contact state of the CB;
s44, setting MCS test times N to enable the simulation times to be large enough and ensure reliable probability solving results;
step S45, changing the required maintenance rate from zero to the set maximum maintenance rate gamma max ；
Step S46, generating a random number as input data;
step S47, calculating and recording the total cost and the availability of the output result by using a Markov equation and a degradation state diagram;
step S48, repeating steps S45-S47 until all required and possible combinations of inspection rates are considered;
step S49, determining the best overhaul rate by investigating the economic optimal result in the system available results under the 95% confidence.
Drawings
FIG. 1 is a schematic diagram of a device state transition in an embodiment of the invention.
FIG. 2 is a schematic diagram of a typical degradation process of a dual device system and discretization of device degradation in an embodiment of the invention.
Fig. 3 is a comparative schematic of a typical degradation process and perfect maintenance and repair of equipment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It will be appreciated by those skilled in the art that the step numbers used herein are for convenience of description only and are not limiting as to the order in which the steps are performed. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. The term "and/or" refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
The invention provides a maintenance frequency optimization method based on equipment state uncertainty and relevance, which comprises the following steps:
step 1, establishing an equipment maintenance state diagram for predicting equipment states and aging and failure faults;
step 2, analyzing the association of the system state and the component equipment, defining the system state, determining a state space, and associating the system state with the equipment degradation condition; the state space refers to a set containing all possible states of the system and the constituent devices;
step 3, establishing a degradation model of equipment degradation rate and characteristic elements, an initial degradation state judgment matrix and a maintenance investment and equipment degradation state transition correlation model;
and 4, solving the optimization decision model by adopting a Monte Carlo method, and determining the optimal overhaul frequency combination of equipment in the system.
The construction of the equipment maintenance state diagram is specifically as follows:
1) The components are as follows:
with device state D 1 、D 2 、…、D n The degradation level of the device is represented, and the current study of the device state evaluation level is generally classified into a scoring section level corresponding to the device state evaluation level. The inspection and secondary and primary maintenance status are denoted I, M and MM, respectively. Further, the failure related to aging and the random failure are denoted by AF and RF, respectively.
μ AF 、μ RF 、γ 1 -γ n 、λ 1 -λ N-1 、λ n And lambda (lambda) 0 The repair rate, the inspection rate, the test result and the test result of the random and aging related faults are respectively displayed,The transition rate of the deterioration rate from the last deterioration state to the aging-related failure state and the transition rate to the random failure state; p (P) Di 、P Mi 、P MMi And P IMMi Respectively representing transition probabilities between different states.
It is assumed that by performing a small amount of maintenance (M), the deteriorated state may decrease or improve one state or remain unchanged. For example, if the device is in the degraded state D 2 M after small maintenance 2 The deteriorated state may be D 1 D2 or D3.
It is assumed that by performing a Main Maintenance (MM), the new deteriorated state may be any state before or remain unchanged. For example, if the device is in a degraded state D3, the MM3 is being serviced, the degraded state may be D 1 、D 2 Or D 3 。
2) Characterization meaning
The equipment maintenance state diagram combines the equipment state, the overhaul mode and the state transition relation on one diagram for analysis, shows the evolution condition of equipment fault risks under different overhaul strategies, and can continuously degrade with equipment aging, but overhaul can improve the equipment state, so that the system risks can be reduced, and the specific state transition is shown in the figure 1. And optimizing equipment overhaul frequency based on the equipment maintenance state diagram, and searching for an optimal overhaul strategy.
The system state randomization process is described as follows: consider a system of n components. It is assumed that each component is represented by four states, D 1 、D 2 、D 3 And F, representing intact, slightly faulty, severely faulty, damaged equipment, respectively, all equipment status combinations being seeded with 4n possible status. The system state at time t is denoted by Z (t), and is determined by the states of all devices in the system in relation to time, and the state space represents a set of all system states. Fig. 2 shows an example of a typical degradation process of a dual device (device 1, device 2) composition system and discretization of device degradation.
The state-to-equipment degradation state relationship for a typical two-device system is represented as follows: (S, E1, E2), S represents a system state, and E1, E2 represent degradation states of the equipment 1 and the equipment 2. The relationship between the system state and the standby degradation state is shown in the following table:
| (0，D 1 ,D 1 ) | (1，D 1 ,D 2 ) | (2，D 1 ,D 3 ) | (3，D 1 ,F) |
| (4，D 2 ,D 1 ) | (5，D 2 ,D 2 ) | (6，D 2 ,D 3 ) | (7，D 2 ,F) |
| (8，D 3 ,D 1 ) | (9，D 3 ,D 2 ) | (10，D 3 ,D 3 ) | (11，D 3 ,F) |
| (12，F,D 1 ) | (13，F,D 2 ) | (14，F,D 3 ) | (15，F,F) |
the system state space is the system state of 0-15Aggregation corresponds to two device degradation processes, such as: 0-t 1 Time period system state is 0, t 1 -t 2 The system state is 1.
3. Model building process
1) Equipment degradation model based on equipment characteristic elements
Taking a Circuit Breaker (CB) as an example, as described above, there is D 1 、D 2 、D 3 And F three states, CB contact wear increases with each closing and opening operation (with or without current). The contact wear monitoring data is assumed to be available when needed, i.e. the maximum contact wear at any stage of each operation is calculated, recorded in percentage form and stored in memory. Thus, it can be continuously monitored. It should be noted that 0% represents a new contact and 100% represents a fully worn contact. From the maximum calculated wear, the following degradation states of the contacts can be considered:
normal state (S) 1 ): contact wear of more than or equal to 0 and less than or equal to 60 percent
Attention state (S) 2 )：60％<Contact wear is less than or equal to 95 percent
Severe state (S) 3 )：95％<Contact wear
As contact deteriorates, CB deterioration rate increases, modeled by increasing the shape parameter of the weibull distribution. If the devices have the same failure mechanism, the device lifetime will typically have the same shape parameters under different conditions. Modeling the degradation model by increasing the shape parameter of the weibull distribution, expressed as shown in formula (1):
where Mean is the Mean value of the lifetime of the device, β and η are the shape and scale parameters, and f represents the gamma function.
The weibull distribution parameters for CB degradation rate based on contact conditions are tabulated as follows:
the mean (mu) and standard deviation (sigma) can be calculated simply from the available data. Shape and scale parameters can be estimated in mean and standard deviation according to the following equations. The shape parameter β and the scale parameter η and the average value μ and the standard deviation σ satisfy the relationship as shown in equation (2):
and (3) solving an equation (2) by using a simulated bifurcation algorithm to obtain a shape parameter beta, and then solving an equation (3) by using the obtained shape parameter beta to obtain a scale parameter eta, wherein the equation (3) is as follows:
2) Initial degradation state modeling
The current degradation state can only be known after a checking or maintenance activity. A simple third-order judgment matrix is assumed based on engineering reality to quickly estimate the degradation state of CB and overcome this defect. Based on the age of service of the CB (new, medium or old) and its contact condition (S1, S2 or S3), the initial degradation condition of the device when maintenance is not performed is estimated.
3) Correlation modeling
Previous related studies only considered the probability transition from a degraded, repaired or inspected state to another state. However, in practice, repair and maintenance costs and durations also vary with time and are interdependent. There is a strong positive correlation between repair and maintenance costs and duration, as they generally rise and fall synchronously. Furthermore, as described above, the equipment may degrade, improve or remain unchanged after maintenance is performed. It is clear that the more the maintenance costs and duration increase, the greater the possibility of improvement of the apparatus, and therefore the costs and maintenance duration are positively correlated with the possibility of improvement. However, for similar reasons, the correlation between cost and maintenance duration and the likelihood of degradation or remaining unchanged is negative.
The correlation can be illustrated by a correlation coefficient characterized by the formula (4):
wherein r refers to a correlation coefficient, and x and y respectively represent two variables.
Step 3 also includes modeling equipment cost and availability, including: the total annual cost TC is selected to characterize the overhaul investment cost of a typical equipment system, and comprises an expected operation cost TEAC and an expected power outage cost TEDC, wherein the expected operation cost TEAC is the overhaul cost, the expected power outage cost TEDC is the profit loss of unsold power outage, and the calculation method is respectively shown in formulas (5) - (7):
TC＝TEAC+TDEC (7)，
s in i Representing the status of the device, C I 、C M 、C MM And C F Representing the costs of each inspection, secondary maintenance, primary maintenance and repair, respectively; pi is a vector containing fourteen state probability elements, and represents that the device is in the state probability, pi= [ pi D ] 1 ΠD 2 ΠD 3 ΠAFΠRF 1 ΠRF 2 ΠRF 3 ΠI 1 ΠI 2 ΠI 3 ΠM 2 ΠMM 2 ΠM 3 ΠMM 3 ]Calculated by formula (8):
wherein Q is a 14×14 state transition matrix, and element Q ij The calculation method is shown as a formula (9):
wherein P is SiSj And d Si Respectively represent slave states S i (i.e. maintenance or inspection status) transition to S j Probability and state S of (2) i Average duration of (2); l is an interrupted load, C LPi Hourly profit, representing reduction in interrupt unit load for different equipment states, is characterized as shown in equation (10):
namely, D1, D2 and D3 in 14 states do not generate power outage cost, and other states calculate power outage cost, C LP Indicating an hourly profit with reduced interrupt unit load; f (S) i ) Representing the number of times the device is in the Si state for one year;
the device availability modeling is expressed as shown in equation (11):
the Monte Carlo solution described in step 4 is a semi-Markov chain based on MCS, and uses the 95% confidence level available by the system as a reference to solve the optimal solution, and the specific steps are as follows:
s41, determining input; that is, determining system parameters including age of CB and contact condition S1, S2 or S3, probability density function of probability input data, correlation parameters, number of deteriorated statesAnd a maximum value gamma of the inspection rate max The method comprises the steps of carrying out a first treatment on the surface of the The probability density function of the probability input data includes conversion rate, repair and maintenance costs, and duration;
step S42, estimating the degradation state D1, D2, or D3 of the device;
step S43, determining a Weibull parameter of the degradation rate of each device according to the electric shock contact state of the CB;
s44, setting MCS test times N to enable the simulation times to be large enough and ensure reliable probability solving results;
step S45, changing the required maintenance rate from zero to the set maximum maintenance rate gamma max ；
Step S46, generating a random number as input data;
step S47, calculating and recording the total cost and the availability of the output result by using a Markov equation and a degradation state diagram;
step S48, repeating steps S45-S47 until all required and possible combinations of inspection rates are considered;
step S49, determining the best overhaul rate by investigating the economic optimal result in the system available results under the 95% confidence.
Fig. 3 is a comparative schematic of a typical degradation process and perfect maintenance and repair of equipment. As shown in the figure, the equipment availability and the maintenance process availability can be effectively improved under the maintenance frequency, and the effects of the same type of maintenance means can be attenuated due to irreversible aging in the use process of the equipment, so that the maintenance effect is optimal when the maintenance force is gradually increased in a critical state.
The invention has the beneficial effects that:
the invention considers the correlation and uncertainty of the maintenance cost and the duration time in the maintenance process and the correlation degradation phenomenon between equipment components, adopts a probability simulation method of equipment state transition to solve the problem, aims at finding the optimal inspection rate of preventive maintenance, can effectively reduce the maintenance cost of equipment and improves the decision accuracy and the decision efficiency of a power grid.
Claims (7)
1. The maintenance frequency optimization method based on the equipment state uncertainty and the relevance is characterized by comprising the following steps of:
step 1, establishing an equipment maintenance state diagram for predicting equipment states and aging and failure faults;
step 2, analyzing the association of the system state and the component equipment, defining the system state, determining a state space, and associating the system state with the equipment degradation condition; the state space refers to a set containing all possible states of the system and the constituent devices;
step 3, establishing a degradation model of equipment degradation rate and characteristic elements, an initial degradation state judgment matrix and a maintenance investment and equipment degradation state transition correlation model;
and 4, solving the optimization decision model by adopting a Monte Carlo method, and determining the optimal overhaul frequency combination of equipment in the system.
2. The maintenance frequency optimization method based on equipment state uncertainty and relevance according to claim 1, wherein the construction of the equipment maintenance state diagram in step 1 comprises two parts of building components and representing meaning, wherein the components comprise equipment state D representing degradation level of equipment 1 、D 2 、…、D n Respectively corresponding to equipment state evaluation grades, wherein the equipment state evaluation grades are classified according to grading interval grades;
inspection and secondary and primary maintenance status, denoted I, M and MM, respectively;
aging-related faults AF and random faults RF;
repair rates mu for random and aging-related faults, respectively AF Inspection rate mu RF Deterioration rate gamma 1 -γ n Conversion rate lambda from last deteriorated state to aging-related failure state 1 -λ N-1 Conversion rate lambda to random failure state n And lambda (lambda) 0 ；
P Di 、P Mi 、P MMi And P IMMi Respectively representing transition probabilities between different states.
It is assumed that by performing the secondary maintenance M, the deteriorated state will decrease or improve one state or remain unchanged, i.e. if the device is in the deteriorated state D 2 In the process of performing secondary maintenance M 2 After that, the deteriorated state may be D 1 、D 2 Or D 3 ；
Assuming that by performing the primary maintenance MM, the new degraded state is any state before or remains unchanged, i.e. if the device is in degraded state D 3 In executing the main maintenance MM 3 After that, the deteriorated state is D 1 、D 2 Or D 3 ；
The representation meaning means that the equipment maintenance state diagram combines the equipment state, the overhaul mode and the state transfer relation on one diagram for analysis, the evolution condition of equipment fault risks under different overhaul strategies is shown, the equipment state is continuously deteriorated along with equipment aging, but overhaul can improve the equipment state, and further the system risk can be reduced; and optimizing equipment maintenance frequency based on the equipment maintenance state diagram, and searching for an optimal maintenance scheme.
3. A method of optimizing service frequency based on uncertainty and relevance of equipment status according to claim 1, characterized in that in step 2, the stochastic process of the system status is described as setting the system as a system of n components, assuming that each component is represented by four states D1, D2, D3 and F, representing a sound, a light fault, a serious fault, equipment damage, respectively, all equipment status combinations have a total of 4n possible states, and Z (t) represents the system status at time t, which is time dependent, determined by the status of all equipment in the system, and the status space represents a set of all system statuses.
4. The maintenance frequency optimization method based on equipment state uncertainty and relevance according to claim 1, wherein in the step 3, in the process of establishing a degradation model of equipment degradation rate and characteristic elements, an equipment degradation model is established based on the equipment characteristic elements, and for a circuit breaker CB, four states of D1, D2, D3 and F exist, and CB contact wear increases after each closing and opening operation, whether current exists or not; assuming contact wear monitoring data is available when needed, i.e., calculating the maximum contact wear at any stage of each operation, recorded as a percentage and stored in memory, CB can be continuously monitored, where 0% represents new contacts and 100% represents fully worn contacts; from the maximum calculated wear, the degradation state of the contact is defined as follows:
normal state S1: contact wear of more than or equal to 0 and less than or equal to 60 percent
Note state S2:60% < contact wear less than or equal to 95%
Severe state S3:95% < contact wear
As the contact is degraded, the CB degradation rate increases, and the degradation model is modeled by increasing the shape parameter of the weibull distribution, expressed as shown in formula (1):
wherein Mean is the Mean value of the lifetime of the device, β and η are shape and scale parameters, f represents the gamma function; the shape parameter β and the scale parameter η and the average value μ and the standard deviation σ satisfy the relationship as shown in equation (2):
and (3) solving an equation (2) by using a simulated bifurcation algorithm to obtain a shape parameter beta, and then solving an equation (3) by using the obtained shape parameter beta to obtain a scale parameter eta, wherein the equation (3) is as follows:
5. the maintenance frequency optimization method based on equipment state uncertainty and relevance according to claim 4, wherein in step 3, an initial degradation state is modeled, specifically, based on CB service age, three conditions of new equipment, medium equipment or old equipment and degradation states S1, S2 or S3 of contact conditions of the new equipment, the medium equipment or the old equipment are considered, the initial degradation conditions of the equipment when maintenance is not performed are estimated, and an initial degradation state judgment matrix is established.
Further preferably, the initial degradation state judgment matrix and the overhaul input in step 3, and the equipment degradation state transition correlation model are characterized by the formula (4):
wherein r refers to a correlation coefficient, and x and y respectively represent two variables.
6. The method for optimizing maintenance frequency based on uncertainty and relevance of equipment status according to claim 5, wherein step 3 further comprises modeling equipment cost and availability, comprising: the total annual cost TC is selected to characterize the overhaul investment cost of a typical equipment system, and comprises an expected operation cost TEAC and an expected power outage cost TEDC, wherein the expected operation cost TEAC is the overhaul cost, the expected power outage cost TEDC is the profit loss of unsold power outage, and the calculation method is respectively shown in formulas (5) - (7):
TC＝TEAC+TDEC (7)，
s in i Representing the status of the device, C I 、C M 、C MM And C F Respectively represent each examinationCosts of secondary maintenance, primary maintenance and repair; pi is a vector containing fourteen state probability elements, and represents that the device is in the state probability, pi= [ pi D ] 1 ΠD 2 ΠD 3 ΠAFΠRF 1 ΠRF 2 ΠRF 3 ΠI 1 ΠI 2 ΠI 3 ΠM 2 ΠMM 2 ΠM 3 ΠMM 3 ]Calculated by formula (8):
wherein Q is a 14×14 state transition matrix, and element Q ij The calculation method is shown as a formula (9):
wherein P is SiSj And d Si Respectively represent slave states S i (i.e. maintenance or inspection status) transition to S j Probability and state S of (2) i Average duration of (2); l is an interrupted load, C LPi Hourly profit, representing reduction in interrupt unit load for different equipment states, is characterized as shown in equation (10):
namely, D1, D2 and D3 in 14 states do not generate power outage cost, and other states calculate power outage cost, C LP Indicating an hourly profit with reduced interrupt unit load; f (S) i ) Representing the number of times the device is in the Si state for one year;
the device availability modeling is expressed as shown in equation (11):
7. the overhaul frequency optimization method based on equipment state uncertainty and relevance according to any of claims 1-6, wherein the solution to the optimization decision model in step 4 by using the monte carlo method is a half markov chain based on MCS, and the solution to the optimal solution using the 95% confidence level available for the system as a reference comprises the following specific sub-steps:
s41, determining input; namely, determining system parameters including the age of CB and contact condition S1, S2 or S3, probability density function of probability input data, correlation parameters, number of deteriorated states, and maximum value gamma of inspection rate max The method comprises the steps of carrying out a first treatment on the surface of the The probability density function of the probability input data includes conversion rate, repair and maintenance costs, and duration;
step S42, estimating the degradation state D1, D2, or D3 of the device;
step S43, determining a Weibull parameter of the degradation rate of each device according to the electric shock contact state of the CB;
s44, setting MCS test times N to enable the simulation times to be large enough and ensure reliable probability solving results;
step S45, changing the required maintenance rate from zero to the set maximum maintenance rate gamma max ；
Step S46, generating a random number as input data;
step S47, calculating and recording the total cost and the availability of the output result by using a Markov equation and a degradation state diagram;
step S48, repeating steps S45-S47 until all required and possible combinations of inspection rates are considered;
step S49, determining the best overhaul rate by investigating the economic optimal result in the system available results under the 95% confidence.
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