Reliability evaluation and maintenance control method for high-dimensional environment characteristic time-varying coupling

Abstract

The invention provides a reliability evaluation and maintenance control method for time-varying coupling of high-dimensional environmental characteristics. The method comprises the following steps: step 1, modeling a single-factor environmental effect; step 2, multi-factor environmental effect coupling; step 3, reliability modeling and evaluation; 4, planning a maintenance strategy; 5, calculating the operation and maintenance cost; and step 6, optimizing the operation and maintenance cost. Through the steps, the equipment reliability evaluation model capable of adaptively coupling the multi-source environmental effect is constructed, a preventive maintenance strategy planning method based on real-time reliability evaluation is designed, a heuristic algorithm of an operation and maintenance cost model is researched, and the reliability of the equipment under the multi-source environmental effect is obtained. And a reliability evaluation method and an optimal control strategy planning method of the equipment.

Description

Technical Field

[0001] The present invention provides a reliability assessment and maintenance control method for time-varying coupling of high-dimensional environmental characteristics. This method introduces environmental covariate parameters for equipment that is subject to continuous performance degradation and failure risks under the influence of multi-source environmental effects in a dynamic service environment, constructs an equipment reliability assessment model that can adaptively couple multiple random environmental effects, formulates a maintenance strategy planning method based on real-time reliability assessment, and designs a joint optimization algorithm for detection cycles and maintenance control. It provides technical support for improving equipment service reliability and reducing failure risk losses. It is suitable for reliability modeling and assessment of equipment whose performance degradation is affected by complex environmental effects and for maintenance control decision optimization. Background Art

[0002] Large-scale mechanical equipment such as ocean-going ships and aerospace vehicles are subjected to dynamic and variable environmental stresses during their service, including temperature changes, humidity changes, vibration shocks and other typical factors. These environmental factors have a significant impact on the performance degradation process of the equipment, which will lead to the acceleration of equipment degradation accumulation and a significant increase in the risk of failure. Traditional reliability modeling and evaluation methods often only consider the performance degradation process of the equipment itself, while ignoring the impact of the time-varying characteristics of the service environment on the degradation process, resulting in a large deviation between the reliability evaluation and life prediction results of the equipment and the actual remaining life of the equipment.

[0003] At present, when planning maintenance for equipment, the evaluation results of the reliability model are usually used as the basis to formulate a preventive maintenance strategy based on the working age and performance degradation status of the equipment: when the cumulative performance degradation of the equipment reaches the preset maintenance threshold, the equipment is triggered to shut down for maintenance. However, in the actual service process of the equipment, due to the time-varying characteristics of external environmental factors, the static maintenance threshold will cause the maintenance strategy to be conservative or radical, resulting in higher maintenance costs or safety risks.

[0004] In order to cope with the performance degradation and failure risks of equipment under the influence of complex time-varying random environmental effects and ensure the service safety and stability of the equipment, for the continuously degraded equipment affected by the multi-source environmental coupling effects, with the goal of ensuring the service effectiveness of the equipment and improving the economic benefits of the equipment service, the traditional reliability modeling method and static maintenance strategy are improved, the dynamic adaptive coupling of multi-source time-varying environmental effects is realized, a reliability assessment model that can consider complex environmental effects is constructed, a dynamic preventive maintenance strategy based on the improved reliability assessment model is designed, and the adaptive joint optimization of the degradation state detection interval and the preventive maintenance reliability threshold is carried out. Summary of the invention

[0005] (1) Purpose of the present invention: The present invention innovatively constructs a reliability assessment and maintenance control method based on time-varying coupling of high-dimensional environmental characteristics for continuously degraded equipment affected by multi-source environmental coupling effects, and designs a dynamic maintenance planning method based on real-time reliability assessment to achieve the goal of ensuring equipment service reliability and reducing equipment downtime losses and maintenance costs.

[0006] (2) Technical solution:

[0007] During the service life of the equipment, due to the instability of the operating conditions, random measurement errors and other factors, the degradation process model has uncertainty, so the random process model has good adaptability to degradation modeling. The random variable {X(t), t≥0} is used to describe the cumulative performance degradation during the operation of the equipment. This paper adopts a generalized random process to describe equipment degradation, namely in, represents the degradation trajectory function affected by multi-source environmental effects, β is the degradation parameter set inherent to the device itself and is not affected by environmental factors; θ is the parameter set affected by environmental effects; is a random variable about time t, which characterizes the random factors in the operation process. is a fixed diffusion parameter, representing the randomness of the degradation process.

[0008] The present invention provides a reliability assessment and maintenance control method for time-varying coupling of high-dimensional environmental characteristics, comprising the following steps: Figure 5 As shown;

[0009] Step 1: Single factor environmental effect modeling;

[0010] For a single environmental factor, its state can be divided into a finite state set E = {e1, e2, …, e N},e i ∈E represents the environmental state, such as the temperature in a certain range. In the time interval Δt, the environmental state is determined by e i ∈E transfers to e j The probability of ∈E can be expressed as P ij (Δt), which has the following properties:

[0011]

[0012] When the time interval approaches 0, that is, Δt→0, there is the following limiting expression:

[0013]

[0014] Among them, P ii (Δt) represents the environmental state e i The probability of staying at time Δt, q ij and qi Respectively represent the environmental state e i To e j The state transition probability density and the environment state stays at e i The state transition rate. Usually, the state transition probability density under the Markov process is stable, so we can get:

[0015]

[0016] Therefore, according to the environmental instantaneous transfer rate matrix According to Kolmogorov's forward equation, for any time interval Δt, the state transition probability matrix is

[0017] Considering the influence of environmental factors on the degradation process, it is represented in the form of an acceleration factor, that is, for a parameter θ in the parameter set θ affected by the environmental effect, j , in the environmental state e i ∈E, with an acceleration factor α ij ,Right now α is the impact effect matrix of the environmental factor.

[0018] Step 2: Coupling of multi-factor environmental effects;

[0019] Considering that the equipment degradation is affected by K environmental effects, its state sets are E k ,0<k<K. Then the impact effect matrix corresponding to the environmental factors is α k .

[0020] At a certain time τ, the state probability vector of the kth environmental factor is P k,τ , where the i-th element represents the probability that the environmental factor is in the i-th state at time τ. Due to the time-varying characteristics of environmental effects, the parameters of the degradation process also have time characteristics. Under the influence of the k-th environmental factor, the parameter set θ affected by the environmental effect is converted into the time-varying parameter set θ k,τ Under the influence of this environmental factor, θ k,τ The expectation is:

[0021]

[0022] in, is the expansion matrix of θ, namely diag(θ). It satisfies:

[0023]

[0024] Under the influence of environmental effects, under the influence of multiple environmental factors, the parameter set θ affected by environmental effects is converted into a time-varying parameter set θ τ . is θτ The expansion matrix, namely diag(θ τ ). The degradation parameter can be expressed as:

[0025]

[0026] Represents the expanded matrix diag(P k,τ α k ). The expectation of the set of equipment degradation parameters under the combined influence of K environmental effects. Extract The diagonal elements of θ can be obtained τ The expected value E[θ τ ].

[0027] Step 3: Reliability modeling and evaluation;

[0028] The equipment degradation process is a random process The initial operation state of the equipment is taken as the cumulative degradation amount 0, and the failure threshold L is set, that is, X(t)>L, Indicates that the device has failed. It means that the device starts from time τ with fixed parameter θ τ The probability density distribution of the degradation accumulation x within the next time t.

[0029] First, calculate the state transition probability of the kth environmental factor experiencing the iδ environmental state before the next decision point:

[0030]

[0031] in, 1×m k The vector, m k represents the number of possible states of the kth element, π k Represents the kth element in the environmental state Π, that is, the state of the kth environmental factor. The πth k elements are 1 and the rest are 0. is the state transition matrix of the kth environmental factor experiencing iδ, which can be obtained by Calculate. Then when iδ to (i+1)δ, the expected degradation parameter of the equipment is:

[0032]

[0033] Extracting the diagonal elements gives the parameter vector E[θ τ+iδ |Π].

[0034] Due to the time-varying characteristics of environmental effects, the parameter set θ has uncertainty over time. The minimum time unit of the environmental effect duration is set to δ. Then at any time τ, the cumulative amount of equipment degradation is X τ , the environmental state is Π τ , then its reliability within time ωδ is:

[0035]

[0036] Among them, E[θ τ+iδ |Π τ ] indicates that in the initial environment state Π τ Under this condition, the expected degradation parameter set at τ+nδ can be derived from formula (6): Then extract the diagonal elements to get x i represents the degradation amount at iδ. At the same time, the probability density distribution of the cumulative degradation amount of the equipment at τ+ωδ can be given as:

[0037]

[0038] Step 4: Maintenance strategy planning;

[0039] Based on the reliability assessment model given above that considers multi-source environmental effects, the equipment maintenance strategy is planned as follows:

[0040] (1) Perform regular degradation state detection with ωδ as the detection interval, and the detection cost is C I ,Degradation state detection can :(a) determine whether the equipment has failed;(b) measure the equipment degradation state;

[0041] (2) If the system is not found to have failed during the degradation state detection, one of the following actions can be performed: (a) No maintenance is performed and the equipment continues to be in service; (b) Preventive maintenance is performed and the maintenance cost C is borne. P ; An example of a degradation trajectory for performing preventive maintenance can be seen Figure 1 ;

[0042] (3) If the system fails during degradation detection, corrective maintenance must be performed and the maintenance cost C F , and there is C F >C P ; An example of a degradation trajectory for corrective repair can be seen Figure 2 ;

[0043] (4) If a failure occurs within the detection interval, the downtime cost is C per δ time. down .

[0044] Step 5: Calculate operation and maintenance costs;

[0045] At any decision point nωδ, the cumulative state of equipment degradation X n , and obtain the current equipment operating environment state Π. (n,x,Π) constitutes a discrete-time Markov chain, whose transition probability is related to the equipment age. At the nth detection point, the optimal equipment operation and maintenance cost is given as:

[0046]

[0047] Among them, C ω (n,x,Π) represents the expected cost of not performing preventive maintenance when the equipment does not fail, specifically:

[0048]

[0049] Among them, e -rδ represents the discount factor; R(·) and g(·) are defined by equations (9) and (10), E[V δ (n+1,x′,Π′)|Π] represents the expectation of equipment maintenance cost at the next detection point under the condition of (n+1,x′). k,ω (Π) can represent the state probability of the kth environmental factor at the next detection point, which can be obtained from formula (9):

[0050]

[0051] in, Indicates P k,ω (Π)ith k elements, m1…m K Represents the number of possible states from the 1st to the Kth environmental factors.

[0052] P ω (n,x,Π) represents the expected cost of performing preventive maintenance if the equipment does not fail, specifically:

[0053] P ω (n,x,Π)=C P +V ω (0,0,Π). (14)

[0054] C F +V ω (0,0,Π) represents the cost that the equipment must bear if it fails at the detection point.

[0055] Step 6: Optimize operation and maintenance costs;

[0056] It can be noted that due to the monotonicity of R(ωδ|nωδ,x,Π) with respect to x, the cost function C ω (n,x,Π) Obviously, the degradation cumulative amount x is monotonically increasing, which means that there is a control threshold x* (n,Π)<ξ, when the cumulative degradation exceeds the threshold, performing preventive maintenance is the optimal strategy.

[0057] Since the cost function calculation is too complicated, the present invention develops a value iteration optimization algorithm, through which the optimal cost function under a fixed detection interval δ can be quickly obtained. The algorithm flow is as follows: Figure 3 shown.

[0058] Value iteration optimization algorithm:

[0059] 1. Input device degradation process Time parameter ωδ, cost parameter C I , C F , C P , C down and allowable error ε;

[0060] 2. Initialize the cost function, let any (n,x,Π) have

[0061] 3. Initialize the number of iterations i = 0;

[0062] 4. Calculate the reliability function R(ωδ|nωδ,x,Π) and the transition probability density function g(x′|ωδ,nωδ,x,Π) corresponding to (n,x,Π) according to equations (9) and (10);

[0063] 5. Determine whether x<L is true. If not, V ω (n,x,Π)=C F +V ω (0,0,Π), go to step 8, if yes, go to step 6;

[0064] 6. According to x′∈[0,L] and formula (12) are used to calculate

[0065] 7.Judgment Is it established? If established, then yes If not established

[0066] 8.Judgment Is it true? If it is true, go to step 9. If it is not true, then i=i+1 and return to step 6.

[0067] 9. Output

[0068] Through the value iteration optimization algorithm, the optimal cost function of the device in the state (n, x, Π) at a fixed detection interval can be quickly calculated, the optimal action in any state can be obtained, and the optimal control strategy at a fixed detection interval can be formed.

[0069] Through the above steps, the present invention realizes the construction of an equipment reliability assessment model that can adaptively couple multi-source environmental effects, designs a preventive maintenance strategy planning method based on real-time reliability assessment, studies the heuristic algorithm of the operation and maintenance cost model, and obtains the equipment reliability assessment method and optimal control strategy planning method under multi-source environmental effects.

[0070] (3) Advantages and effects:

[0071] 1. Aiming at the continuously degraded equipment affected by the multi-source environmental coupling effect, the present invention constructs a reliability assessment model and an operation and maintenance cost model, and studies the optimal maintenance strategy planning method for equipment in a time-varying random environment;

[0072] 2. Compared with the existing reliability model that only considers the degradation of the equipment itself, the present invention considers the impact of the dynamic service environment on the equipment degradation, and the evaluation result is more accurate and more applicable;

[0073] 3. Compared with the existing static maintenance strategy, the present invention designs a dynamic maintenance strategy based on real-time reliability evaluation results, which is not only compatible with the maintenance strategy of static threshold, but also more suitable for equipment with dynamic optimal control strategy under random environmental effects, with stronger adaptability and better optimization results;

[0074] 4. The heuristic algorithm provided by the present invention is simple and fast to calculate and has a lot of research theoretical support;

[0075] 5. The method described in the present invention is scientific, and its application objects are closely matched with mechanical equipment in dynamic service environments, and it has broad promotion and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a schematic diagram of the preventive maintenance of the equipment after the maintenance rule is triggered.

[0077] Figure 2 Schematic diagram of corrective maintenance for equipment failure before the maintenance rule is triggered.

[0078] Figure 3 Flowchart of the proposed heuristic optimization algorithm.

[0079] Figure 4 Figure 2 is an example of a drive degradation trajectory.

[0080] Figure 5 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0081] The present invention will be further described below with reference to examples.

[0082] The ship radar driver is a key device for controlling the steering of the radar. It adjusts the radar angle according to instructions at any time to ensure the normal operation of the radar. During the ship's mission, the driver may experience unexpected failures due to its own internal degradation and external environmental impact, such as structural cracking caused by corrosion. Cracks caused by corrosion are the most common failure mode observed during the driver maintenance process, and the main cause is corrosion (such as pitting, crevice corrosion, and stress corrosion). Since ships perform missions across multiple sea areas and the mission time is long, shipboard equipment will be subject to a variety of different environmental stresses, such as temperature changes, humidity changes, etc. These environmental stresses will have a significant impact on the degradation process of the equipment.

[0083] The radar driver degradation state detection is usually performed by crack detection equipment, such as ultrasonic flaw detection equipment. In this example, the crack propagation process of the driver is described by the Wiener process, that is, the device degradation amount at time t is:

[0084] X(t)=x0+vt+σB(t); where vt is the degradation trajectory affected by environmental effects; σB(t) is the random diffusion term. There are three main environmental factors that affect the crack growth rate of the driver: temperature, humidity and vibration. The temperature is divided into three categories: low temperature, normal temperature and high temperature, and the factors affecting the degradation rate are 1.04, 1 and 1.09 respectively. The humidity is divided into five categories from low to high, and the factors affecting the degradation rate are 1, 1.01, 1.02, 1.05 and 1.1 respectively. The vibration is divided into five categories, and the factors affecting the degradation rate are 1, 1.04, 1.07, 1.12 and 1.16 respectively. The specific degradation parameters and maintenance cost parameters of the driver are shown in Table 1, which are obtained by statistics of historical maintenance records. The minimum time unit is 1 day, denoted as δ=1. The detection interval is ωδ=10.

[0085] Table 1 Driver degradation and cost parameters

[0086]

[0087] The method proposed by the present invention is described below with reference to examples:

[0088] Step 1: Single factor environmental effect modeling;

[0089] Define temperature, humidity, and vibration as environmental effects 1, 2, and 3 respectively. Their corresponding instantaneous state transfer matrices are:

[0090]

[0091] according to When calculating i∈[1,ω], the transition probability matrix of the three environmental factors is

[0092] Step 2: Coupling of multi-factor environmental effects;

[0093] According to the influence of the three environmental effects on the parameter v given above, the influence effect matrix corresponding to the environmental factors can be obtained as α k .

[0094]

[0095] The three environmental factors have 3, 3, and 5 states respectively, so π has π1∈[1,3], π2∈[1,5], π3∈[1,5]. When calculating i∈[1,ω], the probability matrix of the three environmental factors in all π cases is:

[0096]

[0097] in, 1×m k The vector, m k represents the number of possible states of the kth element, π k Represents the kth element in the environmental state Π, that is, the state of the kth environmental factor. The πth k elements are 1 and the rest are 0. is the state transition matrix of the kth environmental factor experiencing iδ. When iδ to (i+1)δ, the expected degradation parameter of the equipment is:

[0098]

[0099] Extracting the diagonal elements gives the parameter vector E[θ τ+iδ |Π].

[0100] Step 3: Reliability modeling and evaluation;

[0101] The degradation accumulation is discretized with a discrete interval of 0.01. Substituting the calculation results of equations (16)-(18) and the probability density function f(·) of the random process X(t)=x0+vt+σB(t) into equations (9) and (10), the reliability function in all state spaces and the degradation cumulative probability density distribution of the next detection point can be obtained.

[0102] Step 4: Maintenance strategy planning;

[0103] Based on the reliability assessment model given above that considers multi-source environmental effects, the equipment maintenance strategy is planned as follows:

[0104] (1) Perform regular degradation state detection with a detection interval of ωδ=5, and the detection cost is C I=500, degradation status detection can: (a) determine whether the equipment has failed; (b) measure the degradation status of the equipment;

[0105] (2) If the system is not found to have failed during the degradation state detection, one of the following actions can be performed: (a) No maintenance is performed and the equipment continues to be in service; (2) Preventive maintenance is performed and the maintenance cost C is borne. P =10000;

[0106] (3) If the system fails during degradation detection, corrective repairs must be performed and the repair costs must be borne.

[0107] C F =30000;

[0108] (4) If a failure occurs within the detection interval, the downtime cost is C per δ time. down =2500.

[0109] Step 5: Calculate operation and maintenance costs;

[0110] At any decision point nωδ, the cumulative state of equipment degradation X n , and obtain the current device operating environment state Π. (n,x,Π) constitutes a discrete-time Markov chain, whose transition probability is related to the device age. Set Substituting into equation (11) and equation (14), we can obtain the cost of performing preventive maintenance and corrective maintenance in the first iteration;

[0111] Substitute the calculation results of equations (16)-(18), the reliability function R(·) in all state spaces (n, x, Π) obtained in step 3, and the degradation cumulative probability density distribution g(·) of the next detection point into equation (12) to obtain the cost of not performing preventive maintenance in the first iteration; the optimal value function and optimal strategy are obtained through equation (11);

[0112] Step 6: Optimize operation and maintenance costs;

[0113] Substitute the above parameters and calculation results into the value iteration optimization algorithm:

[0114] 1. Input device degradation process Time parameter ωδ, cost parameter C I , C F , C P , C down and allowable error ε;

[0115] 2. Initialize the cost function, let any (n,x,Π) have

[0116] 3. Initialize the number of iterations i = 0;

[0117] 4. According to the result of step 3, input the reliability function R(ωδ|nωδ,x,Π) and the transition probability density function g(x′|ωδ,nωδ,x,Π) corresponding to (n,x,Π);

[0118] 5. Determine whether x<L is true. If not, V ω (n,x,Π)=C F +V ω (0,0,Π), go to step 8, if yes, go to step 6;

[0119] 6. According to x′∈[0,L] and formula (12) are used to calculate

[0120] 7.Judgment Is it established? If established, then yes If not established

[0121] 8.Judgment Is it true? If it is true, go to step 9. If it is not true, then i=i+1 and return to step 6.

[0122] 9. Output

[0123] Through the value iteration optimization algorithm, the optimal action under any state is obtained, forming the optimal control strategy under a fixed detection interval.

[0124] In order to further illustrate the effectiveness of the present invention, Figure 4 The example diagrams of 10 drive degradation trajectories are given. Based on the cost parameters in Table 2 and the optimal control strategy obtained in the above steps, the average cost of the strategy applied to the 10 degradation trajectories can be calculated. The calculation method is as follows:

[0125] 1. Extract degradation data from the degradation trajectory according to the optimal detection interval;

[0126] 2. Based on the parameter update function in step 1, update the degradation parameters of the corresponding detection points;

[0127] 3. Using the control strategy obtained by the proposed value iteration algorithm, the operation and maintenance costs under 10 degradation trajectories are calculated;

[0128] Table 2 shows the results of operation and maintenance control using the algorithm proposed in the present invention, that is, the average operation and maintenance cost obtained by applying it to 10 degradation trajectories, and compares it with the maintenance strategy planning method without parameter updating and the maintenance strategy planning method with fixed maintenance thresholds.

[0129] Table 2 Optimal availability of radar driver and its maintenance scheme under different maintenance strategies

[0130] Maintenance strategy Detection interval Preventive Maintenance Threshold Average operation and maintenance cost The strategy of the present invention 2.4 -- 7841 Strategy without parameter update 2.4 -- 8369 Strategy for fixed maintenance threshold 3.2 13.4 9107

[0131] The results show that the optimal maintenance strategy obtained by the maintenance strategy planning method proposed in the present invention has the best average operation and maintenance cost for 10 drives compared with the existing strategy planning method without parameter update or the strategy planning method with fixed preventive maintenance threshold. Therefore, the present invention realizes the continuous degradation equipment affected by the multi-source environmental coupling effect, constructs a reliability model considering the impact of environmental effects on equipment degradation, designs a preventive maintenance strategy planning method based on impact environment and degradation state, and conducts research on optimization methods for condition monitoring intervals and preventive maintenance decisions, solving the problem of how to scientifically plan maintenance strategies and optimize operation and maintenance costs for equipment considering random environmental effects.

[0132] In summary, the present invention provides a reliability assessment and maintenance control method for time-varying coupling of high-dimensional environmental characteristics. This method introduces environmental covariate parameters for equipment that is subject to continuous performance degradation and failure risks under the influence of multi-source environmental effects in a dynamic service environment, constructs an equipment reliability assessment model that can adaptively couple multiple random environmental effects, develops a maintenance strategy planning method based on real-time reliability assessment, and designs a joint optimization algorithm for detection cycles and maintenance control. It can optimize the average maintenance cost of equipment in the long term, and provide theoretical and technical support for improving the reliability and safety of equipment and reducing equipment operation and maintenance costs.

Claims

1. A reliability assessment and maintenance control method for time-varying coupling of high-dimensional environmental characteristics, characterized in that: The following steps are involved: Step 1: Single factor environmental effect modeling; For a single environmental factor, the state is divided into a finite state set E = {e1, e2, …, e N },e i ∈E represents the state of the environment. In the time interval Δt, the state of the environment is i ∈E transfers to e j The probability of ∈E is denoted as P ij (Δt), which has the following properties: Step 2: Coupling of multi-factor environmental effects; Considering that the equipment degradation is affected by K environmental effects, the state sets are E k ,0<k<K; then the impact effect matrix corresponding to the environmental factor is α k ; Step 3: Reliability modeling and evaluation; The equipment degradation process is a random process Description: The initial operating state of the equipment is the cumulative degradation of 0, and the failure threshold L is set, that is, X(t)>L, Indicates that the equipment has failed; It means that the device starts from time τ with fixed parameter θ τ The probability density distribution of the degradation accumulation x within the next time t; Step 4: Maintenance strategy planning; Plan equipment maintenance strategies based on the reliability assessment model considering multi-source environmental effects; Step 5: Calculate operation and maintenance costs; At any decision point nωδ, the cumulative state of equipment degradation X n , and obtain the current equipment operating environment state Π; (n, x, Π) constitutes a discrete-time Markov chain, and the transition probability is related to the equipment age; at the nth detection point, the optimal equipment operation and maintenance cost is given as: Among them, C ω (n,x,Π) represents the expected cost if the equipment does not fail and preventive maintenance is not performed; Step 6: Optimize operation and maintenance costs; Due to the monotonicity of R(ωδ|nωδ,x,Π) with respect to x, the cost function C ω (n,x,Π) is monotonically increasing with respect to the degradation cumulative amount x, that is, there exists a control threshold x * (n,Π)<ξ, when the cumulative degradation exceeds the threshold, performing preventive maintenance is the optimal strategy.

2. The reliability assessment and maintenance optimization method of adaptively coupling time-varying environmental information according to claim 1 is characterized by: In step 1, when the time interval approaches 0, that is, Δt→0, the limiting expression is: Among them, P ii (Δt) represents the environmental state e i The probability of staying at time Δt, q ij and q i Represents the environmental state e i To e j The state transition probability density and the environment state stays at e i The state transition rate; the state transition probability density under the Markov process is stable, thus: Therefore, according to the environmental instantaneous transfer rate matrix According to Kolmogorov's forward equation, for any time interval Δt, the state transition probability matrix is 3. The reliability assessment and maintenance optimization method of adaptively coupling time-varying environmental information according to claim 2 is characterized by: Considering the influence of environmental factors on the degradation process, it is represented in the form of an acceleration factor, that is, for a parameter θ in the parameter set θ affected by the environmental effect, j , in the environmental state e i ∈E, with an acceleration factor α ij ,Right now α is the impact effect matrix of the environmental factor.

4. The reliability assessment and maintenance optimization method of adaptively coupling time-varying environmental information according to claim 1 is characterized by: In step 2, at a certain time τ, the state probability vector of the kth environmental factor is P k,τ , where the i-th element represents the probability that the environmental factor is in the i-th state at time τ; due to the time-varying characteristics of environmental effects, the parameters of the degradation process also have time characteristics. Under the influence of the k-th environmental factor, the parameter set θ affected by the environmental effect is converted into the time-varying parameter set θ k,τ ; Under the influence of this environmental factor, θ k,τ The expectation is: Among them, θ is the expansion matrix of θ, that is, diag(θ); it satisfies:

5. The reliability assessment and maintenance optimization method of adaptively coupling time-varying environmental information according to claim 4 is characterized by: Under the influence of environmental effects, under the influence of multiple environmental factors, the parameter set θ affected by environmental effects is converted into a time-varying parameter set θ τ ; is θ τ The expansion matrix, namely diag(θ τ ); the degradation parameter is expressed as: Represents the expanded matrix diag(P k,τ α k );Expectation of the set of equipment degradation parameters under the combined influence of K environmental effects;Extract The diagonal elements of θ τ The expected value E[θ τ ].

6. The reliability assessment and maintenance optimization method of adaptively coupling time-varying environmental information according to claim 1 is characterized by: In step 3, the state transition probability of the kth environmental factor experiencing an environmental state of iδ before the next decision point is calculated: in, 1×m k The vector, m k represents the number of possible states of the kth element, π k represents the kth element in the environmental state Π, that is, the state of the kth environmental factor; The πth k elements are 1, and the rest are 0; is the state transition matrix of the kth environmental factor undergoing iδ, given by Calculate; then when iδ to (i+1)δ, the expected degradation parameter of the equipment is: Extract the diagonal elements to get the parameter vector E[θ τ+iδ |Π].

7. The reliability assessment and maintenance optimization method of adaptively coupling time-varying environmental information according to claim 6 is characterized by: Due to the time-varying characteristics of environmental effects, the parameter set θ has uncertainty over time; the minimum time unit of the duration of environmental effects is set to δ; then at any time τ, the cumulative amount of equipment degradation is measured to be X τ , the environmental state is Π τ , then its reliability within time ωδ is: Among them, E[θ τ+iδ |Π τ ] indicates that in the initial environment state Π τ Under this condition, the expected degradation parameter set at τ+nδ is derived Then extract the diagonal elements to get; x i represents the degradation amount at iδ; at the same time, the probability density distribution of the cumulative degradation amount of the equipment at τ+ωδ is given as:

8. The reliability assessment and maintenance optimization method of adaptively coupling time-varying environmental information according to claim 1 is characterized by: In step 4, specifically: (1) Perform regular degradation state detection with ωδ as the detection interval, and the detection cost is C I ,Degradation state detection: (a) determine whether the equipment has failed; (b) measure the degradation state of the equipment; (2) If the system is not found to have failed during the degradation state detection, perform one of the following actions: (a) do not perform maintenance and keep the equipment in service; (b) perform preventive maintenance and bear the maintenance cost C P ; (3) If the system fails during degradation detection, corrective maintenance must be performed and the maintenance cost C F , And there is C F >C P ; (4) If a failure occurs within the detection interval, the downtime cost is C per δ time. down .

9. The reliability assessment and maintenance optimization method of adaptively coupling time-varying environmental information according to claim 1 is characterized by: In step 5, specifically: Among them, e -r δ represents the discount factor; E[V δ (n+1,x′,Π′)|Π] represents the expectation of equipment maintenance cost at the next detection point under the condition of (n+1,x′); P k,ω (Π) represents the state probability of the kth environmental factor at the next detection point, then: in, Indicates P k,ω (Π)ith k elements, m1…m K Represents the number of possible states of the 1st to the Kth environmental factors respectively; P ω (n,x,Π) represents the expected cost of performing preventive maintenance if the equipment does not fail, specifically: P ω (n,x,Π)=C P +V ω (0,0,P) C F +V ω (0,0,Π) represents the cost that the equipment must bear if it fails at the detection point.

10. The reliability assessment and maintenance optimization method of adaptively coupling time-varying environmental information according to claim 1 is characterized by: In step 6, since the cost function calculation is too complicated, a value iteration optimization algorithm is designed to find the optimal cost function under a fixed detection interval δ. The value iteration optimization algorithm is: 6.1 Input Device Degradation Process Time parameter ωδ, cost parameter C I , C F , C P , C down and allowable error ε; 6.2 Initialize the cost function, let any (n, x, Π) have 6.3 Initialize the number of iterations i = 0; 6.4 Calculate the reliability function R(ωδ|nωδ,x,Π) and the transition probability density function g(x′|ωδ,nωδ,x,Π) corresponding to (n,x,Π); 6.5 Determine whether x<L is true. If not, then V ω (n,x,Π)=C F +V ω (0,0,Π), go to step 6.8, if yes, go to step 6.6; 6.6 According to x′∈[0,L] and C ω (n,x,Π) calculation 6.7 Judgment Is it established? If it is established, If not established 6.8 Judgment Is it true? If it is true, go to step 6.

9. If it is not true, then i=i+1 and return to step 6.

6. 6.9 Output Through the value iteration optimization algorithm, the optimal cost function of the device in the state (n, x, Π) at a fixed detection interval is calculated, the optimal action in any state is obtained, and the optimal control strategy at a fixed detection interval is formed.

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