A Method for Optimization and Reliability Assessment of a Two-Unit Co-load System

Abstract

This invention relates to a method for optimizing and evaluating the reliability of a two-unit co-load system. Based on the Weibull distribution of lifetime, the failure distribution function is obtained for any load-bearing unit. The average lifetime function of the load-bearing unit is obtained based on the description of the scale parameters of the load-bearing unit and the load size in the failure distribution function using an inverse power-law model. Based on the average lifetime function, the allocation weight is calculated with the goal of making the average lifetimes of the first and second load-bearing units equal. Based on the allocation weight, the load is redistributed between the first and second load-bearing units, thereby improving the lifetime and reliability of the co-load system after load redistribution. The entire co-load system is optimized, and the method is simple to calculate, easy to implement, and convenient for engineers to master and use.

Description

Technical Field

[0001] This invention relates to the field of reliability optimization of co-loaded systems, and specifically to a method for optimizing and evaluating the reliability of a two-unit co-loaded system. Background Technology

[0002] When a system consists of two units, and the total load is shared by the two units according to a certain rule, and the other normally functioning unit bears the entire load after the failure of one unit, such a system is called a shared-load system. Please refer to the appendix. Figure 1 The total load on the system is L, and the loads borne by the two units are L1 = αL and L2 = (1-α)L, respectively, where 0 ≤ α ≤ 1 is called the load distribution weight of the two-unit co-load system. Co-load systems are a special type of redundant system and an important technical means to improve system reliability, and are widely used in power transmission and advanced manufacturing.

[0003] In a shared load system, the load borne by each unit inevitably affects its reliability evolution, and the unit load is determined by the system's load distribution mechanism, i.e., the value of the load distribution factor. Currently, the most commonly used load distribution mechanism is average distribution, which assumes that all units bear equal loads, α = 0.5.

[0004] In reality, due to the heterogeneity of unit types, individual attributes, and operating environments, the reliability evolution during operation is inevitably affected by random uncertainties, resulting in different reliability levels for units after the same amount of time of operation. Therefore, the system operation strategy under load-average distribution cannot effectively balance the unit reliability evolution process under random uncertainties, and the reliability of the co-loaded system needs further improvement. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide a method for optimizing and evaluating the reliability of a two-unit co-loaded system, which has the characteristic of higher reliability of the co-loaded system.

[0006] In one aspect, an embodiment provides an optimization method for a two-unit co-load system. The two-unit co-load system includes a first load-bearing unit and a second load-bearing unit, with a total load of L. At the start of operation, the load borne by the first load-bearing unit is L1 = αL, and the load borne by the second load-bearing unit is L2 = (1-α)L, where α is the weighting of the load distribution between the two load-bearing units, 0 ≤ α ≤ 1. The optimization method includes:

[0007] Based on the lifetime-based Weibull distribution, the failure distribution function of any load-bearing unit is obtained;

[0008] Based on the description of the inverse power law model of the scale parameters of the bearing unit and the load size in the failure distribution function, the average lifetime function of the bearing unit is obtained.

[0009] Based on the average lifetime function, the allocation weight is calculated with the goal of making the average lifetimes of the first bearing unit and the second bearing unit equal;

[0010] Based on the allocation weights, load is allocated to the first and second bearer units.

[0011] In one embodiment, the lifetime-based Weibull distribution, for any bearer unit, obtains its failure distribution function, including:

[0012]

[0013] Where, m i η is a shape parameter that characterizes the product's failure modes and mechanisms, and is independent of the load applied to the unit; i The scale parameter reflects the environmental stress and working conditions experienced by the product and is related to the load size of the unit; i equals 1 or 2 and is the index of the load-bearing unit; t is the working time of the load-bearing unit.

[0014] In one embodiment, the average lifetime function E of the load-bearing unit is obtained by describing the inverse power-law model based on the scale parameters of the load-bearing unit and the load it bears in the failure distribution function. i (T), including:

[0015]

[0016] in, a i >0,b i >0 is a constant, estimated based on the actual lifespan data and failure time points of the bearing unit. u is the integral variable, x is the independent variable of the function, and e is the exponential constant.

[0017] In one embodiment, calculating the allocation weight based on the average lifetime function, with the objective of making the average lifetimes of the first and second bearing units equal, includes:

[0018] Based on the average life function and the first weighting of the loads borne by the first and second load-bearing units, with the objective of equalizing the average lifespans of the first and second load-bearing units (E1(T) = E2(T)), the solution is obtained...

[0019]

[0020] The load distribution weight α of the first and second bearing units is calculated.

[0021] Secondly, one embodiment provides a reliability assessment method for a two-unit co-loaded system, the two-unit co-loaded system comprising a first load-bearing unit and a second load-bearing unit, the reliability including reliability and / or average lifetime, and the assessment method comprising:

[0022] Based on the lifetime-based Weibull distribution, the failure distribution function of any load-bearing unit is obtained;

[0023] Based on the description of the scale parameters of the bearing unit and the load size in the failure distribution function, the reliability function of the bearing unit when the working time reaches t is obtained.

[0024] Based on the reliability function, the allocation weights of the two bearing units are obtained, and the allocation weights are obtained based on any of the above optimization methods;

[0025] Based on the reliability function and the assigned weights, the lifetime distribution function and fault density function of the first and second bearing units when they work simultaneously are obtained.

[0026] The lifetime distribution function of the first and second load-bearing units when they are working independently;

[0027] Based on the reliability function, the probability of two bearing units not failing after a working time of t is obtained as the first probability of occurrence.

[0028] Based on the failure density function of the first bearing unit when the two bearing units work together, the lifetime distribution function of the second bearing unit, and the lifetime distribution function of the first bearing unit when it works alone, the probability of the first bearing unit failing while the second bearing unit does not fail when the working time of the co-loaded unit reaches t is obtained as the second probability of occurrence.

[0029] Based on the failure density function of the second bearing unit when the two bearing units work together, the lifetime distribution function of the first bearing unit, and the lifetime distribution function of the second bearing unit when they work alone, the probability of the first bearing unit failing while the second bearing unit fails when the co-load system has been in operation for t is obtained as the third probability of occurrence.

[0030] Based on the first occurrence probability, the second occurrence probability, and the third occurrence probability, a reliability evaluation function for the shared-load system is obtained when the operating time reaches t, and the reliability of the shared-load system is evaluated.

[0031] In one embodiment, the lifetime-based Weibull distribution, for any bearer unit, obtains its failure distribution function, including:

[0032]

[0033] Where, m i η is a shape parameter that characterizes the product's failure modes and mechanisms, and is independent of the load applied to the unit; i The scale parameter reflects the environmental stress and working conditions experienced by the product, and is related to the load size of the unit; i equals 1 or 2, which is the index of the load-bearing unit; t is the working time of the load-bearing unit.

[0034] The description of the inverse power-law model based on the scale parameters of the bearing unit and the load size in the failure distribution function, obtaining the reliability function for the bearing unit's operating time reaching t, includes:

[0035]

[0036] in, a i >0,b i >0 is a constant, estimated based on the actual lifespan data and failure time points of the bearing unit.

[0037] In one embodiment, obtaining the lifetime distribution function and fault density function of the first and second bearer units when they operate simultaneously, based on the reliability function and the assigned weights, includes:

[0038] When the first load-bearing unit and the second load-bearing unit operate simultaneously, the lifetime distribution function of the first unit includes:

[0039]

[0040] Where α is the allocation weight, satisfying L1=αL, L2=(1-α)L, 0≤α≤1, where L is the total load, L1 is the load of the first bearing unit, and L2 is the load of the second bearing unit;

[0041] When the first load-bearing unit and the second load-bearing unit operate simultaneously, the fault density function of the first unit includes:

[0042]

[0043] When the first and second load-bearing units operate simultaneously, the lifetime distribution function of the second unit includes:

[0044]

[0045] When the first and second load-bearing units operate simultaneously, the fault density function of the second unit includes:

[0046]

[0047] The obtained lifetime distribution functions for the first and second bearing units when they operate independently include:

[0048] When the first load-bearing unit fails and the second load-bearing unit continues to operate, the lifetime distribution function of the second load-bearing unit includes:

[0049]

[0050] When the second load-bearing unit fails and the first load-bearing unit continues to operate, the lifetime distribution function of the first load-bearing unit includes:

[0051]

[0052] In one embodiment, obtaining the probability of both bearer units not failing when the operating time reaches t, based on the reliability function, as the first probability of occurrence includes:

[0053]

[0054] Where T1 is the lifespan of the first bearing unit and T2 is the lifespan of the second bearing unit;

[0055] The second probability of occurrence is obtained by using the failure density function of the first bearing unit when the two bearing units work together, the lifetime distribution function of the second bearing unit, and the lifetime distribution function of the first bearing unit working alone, to obtain the probability that the first bearing unit fails while the second bearing unit does not fail when the co-load system has been in operation for a duration of t. This includes:

[0056]

[0057] Where s is the working time of the first bearing unit;

[0058] The third probability of occurrence, based on the failure density function of the second bearing unit when both bearing units work together, the lifetime distribution function of the first bearing unit, and the lifetime distribution function of the second bearing unit working alone, is obtained as follows:

[0059]

[0060] In one embodiment, the method of obtaining a reliability evaluation function for the shared-load system based on the first occurrence probability, the second occurrence probability, and the third occurrence probability to evaluate the reliability of the shared-load system includes:

[0061] Based on the first occurrence probability, the second occurrence probability, and the third occurrence probability, the reliability evaluation function of the co-operation system when the operating time reaches t is obtained:

[0062] R S (t)=P1(t)+P2(t)+P3(t) (14)

[0063] And / or,

[0064] Based on the first occurrence probability, the second occurrence probability, and the third occurrence probability, the average lifetime assessment function of the co-loaded system is obtained:

[0065]

[0066] The reliability of the co-loaded system is evaluated based on the obtained reliability evaluation function and / or mean lifetime evaluation function when the operating time reaches t.

[0067] Thirdly, in one embodiment, a computer-readable storage medium is provided, the storage medium storing a program that can be loaded by a processor to execute any of the above-described optimization methods and / or reliability assessment methods.

[0068] The beneficial effects of this invention are:

[0069] Based on the lifetime-based Weibull distribution, the failure distribution function is obtained for any load-bearing unit. The average lifetime function of the load-bearing unit is obtained based on the description of the scale parameters and the load size of the load-bearing unit in the failure distribution function using an inverse power-law model. Based on the average lifetime function, the allocation weight is calculated with the goal of equalizing the average lifetimes of the first and second load-bearing units. Based on the allocation weight, the load is redistributed between the first and second load-bearing units, thereby improving the lifetime and reliability of the co-loaded system after load redistribution. The entire co-loaded system is optimized, and the calculation is simple, easy to implement, and convenient for engineers to master and use. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of load distribution in a two-unit co-loading system according to one embodiment;

[0071] Figure 2 This is a flowchart of an optimization method for a two-unit co-loading system according to an embodiment of this application;

[0072] Figure 3 This is a flowchart of the reliability assessment of a two-unit co-loaded system according to an embodiment of this application;

[0073] Figure 4 This is a reliability curve obtained from one embodiment of this application. Detailed Implementation

[0074] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0075] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0076] The serial numbers assigned to components in this article, such as "first" and "second", are used only to distinguish the objects being described and have no sequential or technical meaning.

[0077] To facilitate the explanation of the inventive concept of this application, the reliability technology of the co-loading system is briefly described below.

[0078] In a shared-load system, the load borne by each unit inevitably affects its reliability evolution, and the unit load is determined by the system's load distribution mechanism, i.e., the value of the load distribution factor. Currently, the most commonly used load distribution mechanism is average distribution, which assumes that all units bear equal loads, α = 0.5. However, in reality, due to the heterogeneity of unit types, individual attributes, and operating environments, the reliability evolution during operation is inevitably affected by random uncertainties, resulting in different reliability levels for units after the same amount of time. Therefore, the system operation strategy under average load distribution cannot effectively balance the unit reliability evolution process under random uncertainties, and the reliability of the resulting shared-load system needs further improvement.

[0079] Researchers discovered that for a co-loaded system composed of two heterogeneous load-bearing units, a new allocation weight for the load-bearing units can be obtained based on the Weibull distribution characteristics obeyed by each load-bearing unit, with the goal of balancing the randomness of the reliability evolution process of the two heterogeneous load-bearing units. This can improve the lifespan of the co-loaded system and thus optimize the working system.

[0080] Based on the above, please refer to Figure 2 This application provides an optimization method for a two-unit co-load system in one embodiment. The two-unit co-load system includes a first load-bearing unit and a second load-bearing unit, with a total load of L. At startup, the first load-bearing unit bears a load of L1 = αL, and the second load-bearing unit bears a load of L2 = (1-α)L, where α is the weighting of the load distribution between the two load-bearing units, 0 ≤ α ≤ 1. If either load-bearing unit fails, the other load-bearing unit continues to operate while bearing the entire load L. The following description uses a wind turbine generator as an example to illustrate the optimization method, which includes:

[0081] Step S01: Based on the lifetime Weibull distribution, obtain the failure distribution function for any load-bearing unit.

[0082] In one specific embodiment, it is assumed that the lifetime T of any carrier unit is... i It follows a Weibull distribution, and its failure distribution function can be obtained.

[0083]

[0084] Where, m i η is a shape parameter that characterizes the product's failure modes and mechanisms, and is independent of the load applied to the unit; i The scale parameter reflects the environmental stress and working conditions experienced by the product and is related to the load size of the unit; i equals 1 or 2 and is the index of the load-bearing unit; t is the working time of the load-bearing unit.

[0085] Taking a wind turbine generator set as an example, the wind turbine generator set consists of two wind turbines that jointly undertake the power generation task, with a total power generation task L = 1. Assume that the lifespan of both wind turbines follows a Weibull distribution, and the distribution parameters are shown in Table 1.

[0086] Table 1 Unit life distribution parameters of a certain wind turbine generator set

[0087] parameter <![CDATA[a1]]> <![CDATA[b1]]> <![CDATA[m1]]> <![CDATA[a2]]> <![CDATA[b2]]> <![CDATA[m2]]> Value 1 1 1.5 1.2 0.5 2.5

[0088] Step S02: Based on the description of the inverse power law model of the scale parameters of the bearing unit and the load size in the failure distribution function, the average lifetime function of the bearing unit is obtained.

[0089] In one specific embodiment, it is assumed that the scale parameter η of any carrier unit is... i and the size of the load L i This can be described using an inverse power-law model, i.e. The average lifetime function of any number of bearing units can be obtained.

[0090]

[0091] in, a i >0,b i >0 is a constant, estimated based on the actual lifespan data and failure time points of the bearing unit. u is the integral variable, x is the independent variable of the function, and e is the exponential constant.

[0092] Based on formula (1) and the unit lifetime distribution parameters in Table 1, the average lifetime function of generator 1 can be obtained as follows:

[0093]

[0094] The average life function of generator 2 is

[0095]

[0096] Step S03: Based on the average lifetime function, calculate the allocation weight with the goal of making the average lifetimes of the first bearing unit and the second bearing unit equal.

[0097] In one embodiment of this application, the objective is to ensure that the average lifespans of the two bearing units are equal, E1(T) = E2(T), by solving...

[0098]

[0099] The assigned weight α is calculated.

[0100] Formula (3) is a transcendental equation, which can be solved by the bisection method to obtain the weight α that satisfies the equation.

[0101] In a certain wind turbine generator set, the goal is to make equations (16) and (17) equal, and the bisection method is used to solve the equations. The assigned weight α = 0.5615. It can be seen that α ≠ 0.5, meaning this load distribution method differs from the traditional average distribution.

[0102] Step S04: Based on the allocation weight, load is allocated to the first bearing unit and the second bearing unit.

[0103] Traditional load balancing mechanisms assume that the two units bear equal loads, i.e., the load balancing weight is 0.5. Therefore, for the first and second load-bearing units with different distributed parameter values, their lifetime distribution patterns and average lifetime values ​​are random, resulting in an imbalance in their reliability evolution patterns.

[0104] In this application, a new allocation weight is obtained based on the reliability evolution law of the two components of the balanced load system. Based on the new allocation weight, the load is allocated to the first load-bearing unit and the second load-bearing unit, so that the reliability evolution law of the two components of the balanced load system is more balanced, improving the overall life and reliability of the balanced load system. This achieves the optimization of the balanced load system, and the calculation is simple, easy to implement, and convenient for engineering technicians to master and use.

[0105] To demonstrate that after allocating the load to the two load-bearing units of the shared-load system based on the allocation weights obtained in the above optimization method, the reliability of the shared-load system is better than that of the shared-load system with average load distribution, a reliability assessment method for two-unit shared-load systems can be used. This reliability includes reliability and / or mean lifetime. Existing assessment methods can be used. A new assessment method is provided in one specific embodiment of this application; please refer to [reference needed]. Figure 3 The evaluation method includes:

[0106] Step S11: Based on the lifetime Weibull distribution, obtain the failure distribution function for any load-bearing unit.

[0107] Step S12: Based on the description of the scale parameters of the bearing unit and the load size in the failure distribution function using an inverse power law model, obtain the reliability function when the working time of the bearing unit reaches t.

[0108] In one embodiment, based on the description of the inverse power law model of the load-bearing unit and the load size in the failure distribution function of formula (1), the reliability function of the load-bearing unit when its working time reaches t is obtained.

[0109]

[0110] in, a i >0,b i >0 is a constant, estimated based on the actual lifespan data and failure time points of the bearing unit.

[0111] Step S13: Obtain the allocation weights of the two bearer units.

[0112] The following explanation uses the allocation weight α = 0.5615 of a certain wind turbine generator set as an example.

[0113] Step S14: Based on the reliability function and the assigned weights, obtain the lifetime distribution function and fault density function of the first bearing unit and the second bearing unit when they work simultaneously.

[0114] Step S15: Based on the reliability function, obtain the lifetime distribution function when the first bearing unit and the second bearing unit work independently.

[0115] After calculating the assigned weights, reliability models for the two load-bearing units under partial and full load conditions can be established.

[0116] When both load-bearing units operate simultaneously, the first load-bearing unit bears a partial load of L1 = αL, and its lifetime distribution function includes:

[0117]

[0118] Where α is the weighting, satisfying L1=αL, L2=(1-α)L, 0≤α≤1, where L is the total load, L1 is the load of the first bearing unit, and L2 is the load of the second bearing unit.

[0119] At this point, the fault density function of the first bearing unit includes:

[0120]

[0121] When both load-bearing units operate simultaneously, the second load-bearing unit bears a portion of the load as L2 = (1-α)L, and the lifetime distribution function of the second load-bearing unit includes:

[0122]

[0123] At this point, the fault density function of the second bearing unit includes:

[0124]

[0125] If the first load-bearing unit fails, the second load-bearing unit continues to operate under full load from the point of failure. Under this condition, the lifetime distribution function of the second load-bearing unit includes:

[0126]

[0127] If the second load-bearing unit fails, the first load-bearing unit continues to operate under full load from the point of failure of the second load-bearing unit. Under this condition, the lifetime distribution function of the first load-bearing unit includes:

[0128]

[0129] In a certain wind turbine generator set, based on Table 1 and formulas (5), (6), (7), (8), (9) and (10), substituting the allocation weight of 0.5615, we can obtain:

[0130] When both motors are working simultaneously, the load on motor 1 is 0.5615, and the life distribution function of motor 1 is F1(t)=1-exp[-(0.5615t) 1.5 The fault density function is:

[0131] When both motors are working simultaneously, the load on motor 2 is 0.4385, and the life distribution function of motor 2 is F2(t)=1-exp[-(0.5518t) 2.5 The fault density function is f2(t) = 0.5655t. 1.5 exp[-(0.5518t) 2.5 ].

[0132] If motor 1 fails, then motor 2 will continue to operate under full load. The life storm function of motor 2 is: F 12 (t)=1-exp[-(0.8333t) 2.5 ].

[0133] If motor 2 fails, then motor 1 will continue to operate under full load. The life storm function of motor 1 is: F 21 (t)=1-exp(-t) 1.5 ).

[0134] To ensure the reliability of the shared-load system after operating time t, at least one unit must not fail after operating time t. This includes three scenarios: neither unit fails, only the first load-bearing unit fails, and only the first load-bearing unit fails. Therefore, to derive the lifetime distribution and reliability function of the shared-load system after operating time t, it is necessary to calculate based on these three scenarios.

[0135] Step S16: Based on the reliability function, obtain the probability that neither of the two bearing units will fail after a working time of t as the first probability of occurrence.

[0136] Before either of the two load-bearing units fails, both operate under partial loads, with loads of L1 = αL and L2 = (1-α)L respectively. Therefore, the probability of this situation occurring is taken as the first probability of occurrence:

[0137]

[0138] Where T1 is the lifespan of the first bearing unit and T2 is the lifespan of the second bearing unit. Therefore, we can obtain the first occurrence probability of the above generator set as: P1(t)=exp{-(0.5615t) 1.5 -(0.5518t) 2.5}

[0139] Step S17: Based on the failure density function of the first bearing unit when the two bearing units work together, the lifetime distribution function of the second bearing unit, and the lifetime distribution function of the first bearing unit when it works alone, obtain the probability of the first bearing unit failing while the second bearing unit does not fail when the working time of the co-loaded unit reaches t, as the second probability of occurrence.

[0140] The first load-bearing unit fails before the shared load system reaches operating time t, while the second load-bearing unit does not fail before operating time t. In this case, before the first load-bearing unit fails, both load-bearing units operate under partial load, with loads of L1 = αL and L2 = (1-α)L respectively. After the first load-bearing unit fails, the second load-bearing unit operates under full load, with a load of L. Since the failure time of the first load-bearing unit can be any time in the interval [0, t], the probability of this situation occurring can be calculated using the law of total probability:

[0141]

[0142] Where s is the working time of the first bearing unit, t2 = ts represents the working time of the second bearing unit under full load, and t p2 The equivalent operating time s of the second load-bearing unit under partial load can be converted to the equivalent operating time under full load by solving equation F. 12 (t p2 ) = F2(s) is obtained, that is:

[0143]

[0144] Solving

[0145] Therefore, the probability of this situation occurring can be used to derive the second probability of occurrence as follows:

[0146]

[0147] Therefore, we can obtain the second probability of occurrence of the above generator set as:

[0148] Step S18: Based on the failure density function of the second bearing unit when the two bearing units work together, the lifetime distribution function of the first bearing unit, and the lifetime distribution function of the second bearing unit when working alone, obtain the probability of the first bearing unit not failing but the second bearing unit failing when the co-load system has been in operation for t as the third probability of occurrence.

[0149] The second load-bearing unit fails before the shared load system reaches operating time t, while the first load-bearing unit does not fail before operating time t. In this case, before the second load-bearing unit fails, both load-bearing units operate under partial load, with loads of L1 = αL and L2 = (1-α)L respectively. After the second load-bearing unit fails, the first load-bearing unit operates under full load, with a load of L. Since the failure time of the second load-bearing unit can be any time in the interval [0, t], the probability of this situation occurring can be calculated using the law of total probability:

[0150]

[0151] Where t1 = ts represents the operating time of the first bearing unit under full load, t p1 The equivalent operating time s of the first load-bearing unit under partial load can be converted to the equivalent operating time under full load by solving equation F. 21 (t p1 ) = F1(s) is obtained, that is:

[0152]

[0153] Solving

[0154] Therefore, the probability of this situation occurring can be used to derive the third probability of occurrence as follows:

[0155]

[0156] Therefore, we can obtain the third probability of occurrence of the above generator set as:

[0157] Step S19: Based on the first occurrence probability, the second occurrence probability, and the third occurrence probability, obtain the reliability evaluation function of the co-load system when the working time reaches t, and evaluate the reliability of the co-load system.

[0158] In one specific embodiment, the reliability evaluation function of the co-loaded system is obtained when the operating time reaches t:

[0159] R S (t)=P1(t)+P2(t)+P3(t) (14)

[0160] And / or,

[0161] Obtain the mean lifetime assessment function for the co-loaded system:

[0162]

[0163] The reliability of the co-loaded system is evaluated based on the obtained reliability evaluation function and / or mean lifetime evaluation function when the operating time reaches t.

[0164] We can then obtain the reliability curve of the generator set in the [0, 300] unit time when the weight allocation is 0.5615, as shown below. Figure 4 As shown.

[0165] We can obtain the average lifespan of the generator set with an allocation weight of 0.5615.

[0166] Compared with the system reliability under the load sharing method, the average lifespan of the generator set when the distribution weight is 0.5 is calculated as follows:

[0167] E0 = 0.9783

[0168] It can be seen that, compared with the load equalization method, the load weighted allocation method based on the allocation weights obtained by the optimization method proposed in this application improves the average lifespan of the generator set by a certain percentage.

[0169]

[0170] One embodiment of this application provides a computer-readable storage medium storing a program that can be loaded by a processor to execute any of the above-described optimization methods and / or reliability assessment methods.

[0171] Those skilled in the art will understand that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to achieve the above functions. For example, the program can be stored in the memory of a device, and when the program in the memory is executed by the processor, all or part of the above functions can be achieved. In addition, when all or part of the functions in the above embodiments are implemented by computer programs, the program can also be stored in a server, another computer, disk, optical disk, flash drive, or external hard drive, etc., and can be downloaded or copied to the memory of a local device, or the system of the local device can be updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be achieved.

[0172] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. An optimization method for a two-unit co-loaded system, characterized in that, The two-unit shared system comprises a first bearing unit and a second bearing unit, the total load borne is L, when starting to work, the load borne by the first bearing unit is , the load borne by the second bearing unit is , is the distribution weight of the load of the two bearing units, ; the optimization method comprises: Based on the lifetime-based Weibull distribution, the failure distribution function of any load-bearing unit is obtained; Based on the description of the inverse power law model of the scale parameters of the bearing unit and the load size in the failure distribution function, the average lifetime function of the bearing unit is obtained. Based on the average lifetime function, the allocation weight is calculated with the goal of making the average lifetimes of the first bearing unit and the second bearing unit equal; Based on the allocation weights, load is allocated to the first and second bearing units; The description of the inverse power-law model based on the scale parameters of the load-bearing unit and the load size in the failure distribution function yields the average lifetime function of the load-bearing unit. ,include: （2） in, , It is a constant, estimated based on the actual lifespan data and failure time points of the bearing unit. u is the integral variable, x is the independent variable of the function, and e is the exponential constant; The shape parameter represents the product's failure mode and failure mechanism. It is independent of the load size of the unit. i is equal to 1 or 2 and is the index of the load-bearing unit. The calculation of the allocation weight based on the average lifetime function, with the objective of making the average lifetimes of the first and second bearing units equal, includes: Based on the average lifetime function, the average lifetimes of the first bearing unit and the second bearing unit are equal. With the goal of solving （3） The assigned weights are calculated. .

2. The optimization method as described in claim 1, characterized in that, The lifetime-based Weibull distribution, for any load-bearing unit, obtains its failure distribution function, including: （1） in, t is a dimensional parameter that reflects the environmental stress and working conditions experienced by the product and is related to the load on the unit; t is the working time of the load-bearing unit.

3. A reliability assessment method for a two-unit co-loaded system, characterized in that, The two-unit co-load system includes a first load-bearing unit and a second load-bearing unit. The reliability includes reliability and / or average lifespan. The evaluation method includes: Based on the lifetime-based Weibull distribution, the failure distribution function of any load-bearing unit is obtained; Based on the description of the scale parameters of the bearing unit and the load size in the failure distribution function, the reliability function of the bearing unit when the working time reaches t is obtained. The allocation weights of the two bearing units are obtained, and the allocation weights are obtained based on the optimization method described in claim 1 or 2; Based on the reliability function and the assigned weights, the lifetime distribution function and fault density function of the first and second bearing units when they work simultaneously are obtained. Based on the reliability function, the lifetime distribution function of the first bearing unit and the second bearing unit when they work independently is obtained; Based on the reliability function, the probability of two bearing units not failing after a working time of t is obtained as the first probability of occurrence. Based on the failure density function of the first bearing unit when the two bearing units work together, the lifetime distribution function of the second bearing unit, and the lifetime distribution function of the first bearing unit when it works alone, the probability of the first bearing unit failing while the second bearing unit does not fail when the working time of the co-loaded unit reaches t is obtained as the second probability of occurrence. Based on the failure density function of the second bearing unit when the two bearing units work together, the lifetime distribution function of the first bearing unit, and the lifetime distribution function of the second bearing unit when they work alone, the probability of the first bearing unit failing while the second bearing unit fails when the co-load system has been in operation for t is obtained as the third probability of occurrence. Based on the first occurrence probability, the second occurrence probability, and the third occurrence probability, a reliability evaluation function for the shared-load system is obtained when the operating time reaches t, and the reliability of the shared-load system is evaluated.

4. The evaluation method as described in claim 3, characterized in that, The lifetime-based Weibull distribution, for any load-bearing unit, obtains its failure distribution function, including: （1） in, These are shape parameters that characterize the product's failure modes and failure mechanisms, and are independent of the load applied to the unit. The scale parameter reflects the environmental stress and working conditions experienced by the product, and is related to the load size of the unit; i equals 1 or 2, which is the index of the load-bearing unit; t is the working time of the load-bearing unit. The description of the inverse power-law model based on the scale parameters of the bearing unit and the load size in the failure distribution function, to obtain the reliability function of the bearing unit at time t, includes: （4） in, , It is a constant, estimated based on the actual lifespan data and failure time points of the bearing unit.

5. The evaluation method as described in claim 4, characterized in that, The method of obtaining the lifetime distribution function and failure density function of the first and second load-bearing units when they operate simultaneously, based on the reliability function and the assigned weights, includes: When the first load-bearing unit and the second load-bearing unit operate simultaneously, the lifetime distribution function of the first unit includes: （5） in, To assign weights, satisfy , , Where L is the total load, L1 is the load of the first bearing unit, and L2 is the load of the second bearing unit; When the first load-bearing unit and the second load-bearing unit operate simultaneously, the fault density function of the first unit includes: （6） When the first and second load-bearing units operate simultaneously, the lifetime distribution function of the second unit includes: （7） When the first and second load-bearing units operate simultaneously, the fault density function of the second unit includes: （8） The obtained lifetime distribution functions for the first and second bearing units when they operate independently include: When the first load-bearing unit fails and the second load-bearing unit continues to operate, the lifetime distribution function of the second load-bearing unit includes: （9） When the second load-bearing unit fails and the first load-bearing unit continues to operate, the lifetime distribution function of the first load-bearing unit includes: （10）。 6. The evaluation method as described in claim 5, characterized in that, The method of obtaining the probability of both bearer units not failing when the operating time reaches t, based on the reliability function, includes: （11） Where T1 is the lifespan of the first bearing unit and T2 is the lifespan of the second bearing unit; The second probability of occurrence is obtained by using the failure density function of the first bearing unit when the two bearing units work together, the lifetime distribution function of the second bearing unit, and the lifetime distribution function of the first bearing unit working alone, to obtain the probability that the first bearing unit fails while the second bearing unit does not fail when the co-load system has been in operation for a duration of t. This includes: （12） Where s is the working time of the first bearing unit; The third probability of occurrence, based on the failure density function of the second bearing unit when both bearing units work together, the lifetime distribution function of the first bearing unit, and the lifetime distribution function of the second bearing unit working alone, is obtained as follows: （13）。 7. The evaluation method as described in claim 6, characterized in that, The method of evaluating the reliability of the shared-load system by obtaining a reliability assessment function based on the first occurrence probability, the second occurrence probability, and the third occurrence probability when the operating time reaches t includes: Based on the first occurrence probability, the second occurrence probability, and the third occurrence probability, the reliability evaluation function of the co-operation system when the operating time reaches t is obtained: （14） And / or, Based on the first occurrence probability, the second occurrence probability, and the third occurrence probability, the average lifetime assessment function of the co-loaded system is obtained: （15） The reliability of the co-loaded system is evaluated based on the obtained reliability evaluation function and / or mean lifetime evaluation function when the operating time reaches t.

8. A computer-readable storage medium, characterized in that, The storage medium stores a program that can be loaded by a processor to perform the method as described in any one of claims 1 to 2, or 3 to 7.

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