Multi-fault time limit dispatch analysis method and system based on Monte Carlo algorithm

Abstract

The invention discloses a multi-fault time limit dispatch analysis method and system based on a Monte Carlo algorithm, and solves the problem of reasonable distribution of time limit dispatch when a redundant structure has multiple faults. The method comprises the following steps: setting Monte Carlo algorithm simulation parameters, and entering a simulation major cycle; judging whether simulation is finished or not according to the maximum long-time fault release time and the long-time fault release time, if yes, finishing the simulation, and otherwise, initializing a global variable; judging the running state of the Monte Carlo algorithm simulation program, if the program is terminated, updating the fault-free working time and the long-time fault release time, and then carrying out a new round of simulation again, otherwise, judging the state of a fault component at the current moment, if the component state value is 0, carrying out maintenance treatment, if the component state value is 1, judging whether an LOTC event occurs or not, and if the LOTC event occurs, carrying out maintenance treatment; if yes, the fault-free working time is recorded, the state value is updated, Monte Carlo algorithm simulation is carried out again, and if not, dispatching processing is carried out.

Description

Technical Field

[0001] This application relates to the field of time-constrained dispatch technology, and more specifically, to a multi-fault time-constrained dispatch analysis method and system based on the Monte Carlo algorithm. Background Technology

[0002] The Full Authority Digital Engine Control System (FADEC) of an aircraft serves as the middleware connecting the aircraft's control system and power system. As the central hub of the power system, it is increasingly important for modern engine fault monitoring, isolation, and health management. For aircraft FADEC systems, Time Limited Dispatch (TLD) analysis is a crucial step in system airworthiness certification and an important component of aircraft engine system safety analysis. To improve reliability, FADEC's electronic control components, power supplies, and sensors typically employ redundancy configurations. TLD refers to the ability of a system to operate with a fault for a predetermined period of time without immediate repair when a redundant unit fails. This allows for maintenance of onboard redundant units during planned maintenance intervals, leveraging the advantage of "any redundancy available." It allows airlines to schedule maintenance tasks within a specific timeframe, rather than potentially causing flight delays or cancellations due to all faults requiring pre-flight handling, further improving aircraft dispatchability.

[0003] Existing TLD analysis methods mainly include the time-weighted average method, non-approximate Markov algorithm, approximate Markov algorithm, and Monte Carlo algorithm. Among them, the time-weighted average method uses several past observations of the same variable arranged in chronological order, weighted by the frequency of the variable's occurrence, to calculate a weighted arithmetic mean. This mean is then used as a trend prediction method to forecast the variable's future value. This is a single-fault analysis method, considering a single fault directly leading to Loss of Thrust Control (LOTC), a dispatchable single fault, and a single secondary fault leading to LOTC when dispatched under single-fault conditions. It is not suitable for TLD analysis under multi-fault dispatchable conditions. For Markov algorithms, in the course of an event, if each state transition is only related to the state at the previous moment and not to past states, or in other words, the state transition process has no aftereffects, the transition rate from one state to another in the Markov model includes the failure rate and the repair rate. Individual mechanical faults and uncovered faults should also be considered in the LOTC rate of each fault. These faults can also cause the system to transition from other states to the LOTC state. Although theoretically it can handle multi-fault TLD problems, the number of system states increases exponentially with the increase of system components, leading to an extremely complex modeling process. The Monte Carlo algorithm performs multi-fault TLD analysis through numerical statistical simulation. The system state is determined by the unit state, and the failure and repair of a unit will change the unit state. In TLD analysis, when the change of unit state causes the system to change from the working state to the LOTC state, the FADEC system will stop working. This time is the time T before the LOTC state of the FADEC system. LOTC Currently, most Monte Carlo algorithms only propose general methods without reasonable and detailed simulation steps, and do not provide the handling process for different dispatch and maintenance when there are multiple faults. At the same time, they have the problem of inconsistent starting points compared with time-weighted average method and Markov algorithm. Summary of the Invention

[0004] This application provides a method and system for analyzing time-constrained dispatching under multiple faults based on the Monte Carlo algorithm, which solves the problem of reasonable allocation of time-constrained dispatching under multiple faults in redundant structures, and gives a maintenance strategy under multiple fault dispatching conditions.

[0005] The specific technical solution is as follows:

[0006] In a first aspect, embodiments of this application provide a multi-failure time-constrained dispatch analysis method based on the Monte Carlo algorithm, the multi-failure time-constrained dispatch analysis method comprising:

[0007] S1. Set the simulation parameters of the Monte Carlo algorithm and enter the simulation loop; wherein, the simulation parameters of the Monte Carlo algorithm include the maximum long-term fault release time, time step, short-term fault release time, maximum number of simulations, and convergence threshold;

[0008] S2. Obtain the current long-term fault release time, and determine whether the simulation has ended based on the maximum long-term fault release time and the long-term fault release time. If yes, the simulation ends; otherwise, proceed to step S3.

[0009] S3. Initialize global variables; where the initial value of the number of simulations is 1, the initial value of the fault-free working time is 0, the initial failure rate of each component is the original failure rate, all elements of the initial value of the state vector are 1, and all elements of the initial value of the time vector are exponentially distributed random numbers with one-third of the failure rate of each component as the parameter.

[0010] S4. Obtain the absolute value of the difference between the current number of simulations and the mean time between failures (MTBF) between the two current adjacent simulations, and determine the running status of the Monte Carlo algorithm simulation program based on the maximum number of simulations, the current number of simulations, the convergence threshold, and the absolute value of the difference between the two current adjacent simulations. If the program terminates, proceed to step S5; otherwise, proceed to step S6.

[0011] S5. Update the fault-free operating time according to the current mean time between failures (MTBF) and update the long-term fault release time in a progressive manner according to the time step. Return to step S2 and start a new round of simulation. In the simulation loop, the long-term fault release time starts from 0 and is updated progressively according to the time step.

[0012] S6. Obtain the number of the currently faulty component and the current time of the current fault occurrence of the currently faulty component, and determine whether the component status value of the currently faulty component was 1 before the current time of the current fault occurrence. If yes, proceed to step S7; otherwise, proceed to step S10.

[0013] S7. Set the component state value of the currently faulty component to 0, and the failure rate of the currently faulty component to 1. Calculate the instantaneous LOTC rate of the system based on the minimum cut set data, and determine whether the LOTC event has occurred based on the instantaneous LOTC rate of the system. If yes, proceed to step S8; otherwise, proceed to step S9.

[0014] S8. Obtain and update the fault-free working time based on the current fault occurrence time, and initialize the failure rate of each component to the original failure rate. All elements of the state vector are 1, and all elements of the time vector are exponentially distributed random numbers with one-third of the failure rate of each component as the parameter. At the same time, increment the simulation count by 1, return to step S4, and repeat the Monte Carlo algorithm simulation.

[0015] S9. Determine the dispatch type based on the instantaneous LOTC rate of the system for dispatch processing, and return to step S6 after the dispatch processing is completed; wherein, the dispatch type includes non-dispatch type, short-term dispatch type and long-term dispatch type;

[0016] S10. Perform maintenance processing and update the component status value of the component that has been repaired. After the maintenance processing is completed, return to step S6. When performing unified maintenance processing on all components that have been released due to failure in the system, synchronize the time of all components that have been released due to failure to the current time of failure, and update their fault-free working time respectively.

[0017] In some embodiments of this application, determining whether the simulation has ended based on the maximum long-term fault release time and the long-term fault release time specifically includes:

[0018] Determine whether the long-term fault release time is not less than the maximum long-term fault release time. If it is greater, the simulation is determined to have ended; otherwise, the simulation is determined not to have ended.

[0019] In some embodiments of this application, determining the running status of the Monte Carlo algorithm simulation program based on the maximum number of simulations, the current number of simulations, the convergence threshold, and the absolute value of the difference between the mean time between failures (MTBF) of the two consecutive simulations specifically includes:

[0020] Determine whether the absolute value of the difference between the mean time between failures (MTBF) of the two current consecutive simulations is less than the convergence threshold. At the same time, determine whether the current number of simulations is greater than the maximum number of simulations. If the absolute value of the difference between the mean time between failures (MTBF) of the two current consecutive simulations is not less than the convergence threshold and the current number of simulations is not greater than the maximum number of simulations, then the program is determined not to have terminated; otherwise, the program is determined to have terminated.

[0021] In some embodiments of this application, determining whether an LOTC event has occurred based on the system's instantaneous LOTC rate specifically includes:

[0022] Determine whether the instantaneous LOTC rate of the system is less than 1. If it is, determine that the LOTC event has not occurred; otherwise, determine that the LOTC event has occurred.

[0023] The determination of the dispatch type based on the system's instantaneous LOTC rate specifically includes:

[0024] If the instantaneous LOTC rate of the system is greater than the first preset LOTC rate threshold, it is determined to be a non-dispatch type; if the instantaneous LOTC rate of the system is greater than the second preset LOTC rate threshold but not greater than the first preset LOTC rate threshold, it is determined to be a short-term dispatch type; if the instantaneous LOTC rate of the system is greater than the third preset LOTC rate threshold but not greater than the second preset LOTC rate threshold, it is determined to be a long-term dispatch type.

[0025] In some embodiments of this application, the dispatch handling method for the long-term dispatch type fault is any one of long-term dispatch handling, short-term dispatch handling, or non-dispatch handling; the dispatch handling method for the short-term dispatch type fault is short-term dispatch handling; and the dispatch handling method for the non-dispatch type fault is non-dispatch handling.

[0026] In some embodiments of this application, the dispatching process of step S9 specifically includes:

[0027] Dispatch the currently faulty component;

[0028] Re-dispatch all long-term dispatch type components that have failed in the system;

[0029] Re-dispatch all short-term dispatch type components that have failed in the system.

[0030] In some embodiments of this application, the dispatching process for the currently faulty component specifically includes:

[0031] Determine whether the current long-term fault release time of the currently faulty component is 0. If so, perform immediate repair on the currently faulty component. Otherwise, determine whether there are two or more other faulty components besides the currently faulty component, and determine the dispatch type of all faulty component combinations.

[0032] If such a combination exists and all faulty components are of the non-dispatch type, then all faulty components shall be repaired immediately; otherwise, each faulty component shall be handled individually according to its dispatch type.

[0033] When the dispatch type of the currently faulty component is non-dispatch type, immediate repair processing is performed on the currently faulty component. The component state value of the currently faulty component is set to 1, the failure rate of the currently faulty component is initialized to the original failure rate, and the state vector is updated. m =t m +Trand m , where t mTrand is the time when the fault occurs or is repaired in the currently faulty component m. m The lifetime value is a value randomly generated according to an exponential distribution for the currently faulty component m;

[0034] When the dispatch type of the currently faulty component is short-term dispatch, the currently faulty component is temporarily allowed to proceed, and the state vector is updated. m =t m +T_ST, where t m T_ST is the time when the fault occurs or is repaired in the currently faulty component m, and T_ST is the short-term fault release time.

[0035] When the dispatch type of the currently faulty component is a long-term dispatch type, the currently faulty component is allowed to proceed for a long time, and the state vector is updated. m =t m +T_LT, where t m T_LT is the time when the fault occurs or is repaired in the currently faulty component m, and T_LT is the long-term fault release time.

[0036] In some embodiments of this application, the re-dispatch processing of all faulty long-term dispatch type components in the system specifically includes:

[0037] If the combined dispatch type of the currently faulty component m and the long-term dispatch type component k is a non-dispatch type, then the currently faulty component m and the long-term dispatch type component k shall be repaired immediately.

[0038] If the current faulty component m and the long-term dispatch type component k are combined into a short-term dispatch type, then update the state vector t. k =t k -T_LT+T_ST, and determine the updated t k If the value is less than the minimum value, then the long-term dispatch type component k is immediately repaired; otherwise, the updated value is used. k Continue to allow the long-term dispatch type component k to pass. Simultaneously, continue to allow the currently faulty component m to pass according to the release time of the combined fault of the currently faulty component m and the long-term dispatch type component k, where t... k T_ST is the fault occurrence or repair time of the long-term dispatch type component k, T_LT is the short-term fault release time, T_LT is the long-term fault release time, and minvalue is the current system time.

[0039] If the current faulty component m and the long-term dispatch type component k are combined and the dispatch type is a long-term dispatch type, then determine t. kIf the value is less than the minimum value, then the long-term dispatch type component k should be repaired immediately; otherwise, proceed according to t. k Continue to allow the long-term dispatch type component k to pass. Simultaneously, continue to allow the currently faulty component m to pass according to the release time of the combined fault of the currently faulty component m and the long-term dispatch type component k, where t... k The time when the fault occurs or is repaired for the long-term dispatch type component k is given, and minvalue is the current time of the system.

[0040] In some embodiments of this application, the re-dispatch processing of all faulty short-term dispatch type components in the system specifically includes:

[0041] If the combined dispatch type of the currently faulty component m and the short-term dispatch type component x is non-dispatch type, then the currently faulty component m and the short-term dispatch type component x are immediately repaired; otherwise, the condition is determined by t. x Is it less than the minimum value, where t x The time of failure or repair of the short-term dispatch type component x is given, and minvalue is the current system time.

[0042] If so, the short-term dispatch type component x shall be repaired immediately; otherwise, the short-term dispatch type component x shall continue to be released. At the same time, the current faulty component m shall continue to be released according to the release time of the combined fault of the current faulty component m and the short-term dispatch type component x.

[0043] Secondly, embodiments of this application provide a multi-failure time-constrained dispatch analysis system based on the Monte Carlo algorithm, the multi-failure time-constrained dispatch analysis system comprising:

[0044] The first control module is used to set the simulation parameters of the Monte Carlo algorithm and enter the simulation loop; wherein, the Monte Carlo algorithm simulation parameters include the maximum long-term fault release time, time step, short-term fault release time, maximum number of simulations, and convergence threshold;

[0045] The first judgment module is used to obtain the current long-term fault release time and determine whether the simulation has ended based on the maximum long-term fault release time and the long-term fault release time. If so, the simulation ends; otherwise, the second control module is triggered.

[0046] The second control module is used to initialize global variables; wherein, the initial value of the number of simulations is set to 1, the initial value of the fault-free working time is set to 0, the failure rate of each component is initialized to the original failure rate, all elements of the initial value of the state vector are 1, and all elements of the initial value of the time vector are exponentially distributed random numbers with one-third of the failure rate of each component as the parameter.

[0047] The second judgment module is used to obtain the absolute value of the difference between the current number of simulations and the average fault-free operating time of the two current adjacent simulations, and to judge the running status of the Monte Carlo algorithm simulation program based on the maximum number of simulations, the current number of simulations, the convergence threshold, and the absolute value of the difference between the average fault-free operating time of the two current adjacent simulations. If the program terminates, the third control module is triggered; otherwise, the third judgment module is triggered.

[0048] The third control module is used to update the fault-free operating time according to the current mean time between failures (MTBF) and update the long-term fault release time in a progressive manner according to the time step. Then, it returns to the first judgment module and performs a new round of simulation. In the simulation loop, the long-term fault release time starts from 0 and is updated progressively according to the time step.

[0049] The third judgment module is used to obtain the number of the current faulty component and the current fault occurrence time of the current faulty component, and to determine whether the component status value of the current faulty component was 1 before the current fault occurrence time. If so, the fourth judgment module is triggered; otherwise, the sixth control module is triggered.

[0050] The fourth judgment module is used to set the component state value of the current faulty component to 0, the failure rate of the current faulty component to 1, calculate the instantaneous LOTC rate of the system based on the minimum cut set data, and determine whether the LOTC event has occurred based on the instantaneous LOTC rate of the system. If it has, the fourth control module is triggered; otherwise, the fifth control module is triggered.

[0051] The fourth control module is used to obtain and update the fault-free working time according to the current fault occurrence time, and initialize the failure rate of each component to the original failure rate. All elements of the state vector are 1, and all elements of the time vector are exponentially distributed random numbers with one-third of the failure rate of each component as the parameter. At the same time, the simulation count is incremented by 1, and the module returns to the second judgment module to re-perform the Monte Carlo algorithm simulation.

[0052] The fifth control module is used to determine the dispatch type based on the instantaneous LOTC rate of the system for dispatch processing, and return to the third judgment module after the dispatch processing is completed; wherein, the dispatch type includes non-dispatch type, short-term dispatch type and long-term dispatch type;

[0053] The sixth control module is used for maintenance processing and updating the component status value of the components that have completed maintenance. After the maintenance processing is completed, it returns to the third judgment module. When performing unified maintenance processing on all components that have been released due to failure in the system, the time of all components that have been released due to failure is synchronized to the current time of failure, and their fault-free working time is updated respectively.

[0054] Thirdly, embodiments of this application provide a multi-fault time-constrained dispatch analysis apparatus based on the Monte Carlo algorithm, comprising: a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it executes the multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm as described in the first aspect.

[0055] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm as described in the first aspect.

[0056] The beneficial effects of the embodiments of this application are as follows:

[0057] This multi-fault time-constrained dispatch analysis method solves the problem of rational allocation of time-constrained dispatch in redundant structures with multiple faults. It provides clear guidance on maintenance and dispatch strategies, adopts maintenance strategies that are easy to operate and require minimal workload, and is more practical and operable. It can be applied to the fields of multi-fault TLD analysis, redundancy maintenance decision-making, and reliability analysis of aircraft. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 A flowchart illustrating a multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm provided in this application embodiment;

[0060] Figure 2This is a schematic diagram of the components of a multi-fault time-constrained dispatch analysis system based on the Monte Carlo algorithm, provided in an embodiment of this application. Detailed Implementation

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0062] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The terms "comprising" and "having," and any variations thereof, in the embodiments and drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0063] This application discloses a multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm, which can meet the TLD analysis requirements of multi-fault FADEC systems, and provides maintenance strategies under multi-fault dispatch conditions. These are described in detail below.

[0064] Figure 1 This paper illustrates a multi-failure time-constrained dispatch analysis method based on the Monte Carlo algorithm, according to an embodiment of this application. Figure 1 As shown, this multi-failure time-constrained dispatch analysis method includes the following steps:

[0065] Step S101: Set the simulation parameters for the Monte Carlo algorithm and enter the simulation loop.

[0066] In this embodiment of the application, the Monte Carlo algorithm simulation parameters include, but are not limited to, the maximum long-term fault release time T_LT. max Time step T step Short-term fault release time T_ST, maximum number of simulations N iter And the convergence threshold ∈. In addition, entering the simulation loop refers to the long-term fault release time T_LT starting from 0 and increasing according to the time step.

[0067] Step S102: Obtain the current long-term fault release time, and determine whether the simulation has ended based on the maximum long-term fault release time and the long-term fault release time.

[0068] In this embodiment of the application, simulation judgment is performed after the simulation parameters are set.

[0069] Specifically, this is determined by checking whether the current long-term fault release time T_LT is not less than the maximum long-term fault release time T_LT. max That is, to determine whether the long-term fault release time T_LT has ended in a loop and reached the maximum long-term fault release time T_LT. max If T_LT≥T_LT max If the simulation ends, proceed to step S103.

[0070] Step S103: Initialize global variables.

[0071] Specifically, let the number of simulations N Sim The initial value is 1, and the initial value of the fault-free operating time is T. LOTC Set the initial failure rate λ of each component to 0. i The original failure rate is represented by the state vector S = [s1 s2 … s]. n All elements s with initial values j All are 1, and the time vector is T = [t1 t2 … t n All elements t with initial values i Each component's failure rate is one-tenth. Let be an exponentially distributed random number for the parameter. Where s j (j = 1, 2, ..., n) represents the component state of the currently faulty component j, including both faulty and operational states, represented by 0 and 1 respectively; t i (i = 1, 2, ..., n) indicates that the state of the current faulty component i changes at this moment, which is the time when the fault occurs or is repaired. It also corresponds to the start or end time of the fault dispatch interval.

[0072] In each Monte Carlo algorithm simulation, it is checked whether the LOTC event has occurred. If the LOTC event occurs, it is checked whether the simulation has ended and whether the program can terminate in step S104. If the program does not terminate, the next Monte Carlo algorithm simulation is started. During this period, T LOTC The value will be updated based on whether the faulty component causes a LOTC event. If it does, the lifespan of that component is the system's fault-free operating time T. LOTC Additionally, at the start of each Monte Carlo algorithm simulation, all components are repaired, and all elements s of the initial value of the state vector S are initialized. j All values ​​are 1. During the simulation, the status value of components that have failed but have not yet been repaired will be updated to 0.

[0073] Step S104: Obtain the absolute value of the difference between the current number of simulations and the mean time between failures (MTBF) between the two adjacent simulations, and determine whether the Monte Carlo algorithm simulation program should be terminated.

[0074] In this embodiment of the application, based on the maximum number of simulations N iter Current number of simulations N Sim The convergence threshold is ∈ , and the absolute value of the difference between the mean fault-free uptime of the two consecutive simulations is . The running status of this Monte Carlo algorithm simulation program is assessed to determine whether the program has terminated. The specific calculation formula is as follows:

[0075]

[0076] In the above formula, For the Nth Sim The mean time between failures (MTBF) of the system simulated using the Monte Carlo algorithm. For the Nth Sim -1 Monte Carlo simulation of the system mean time between failures.

[0077] Specifically, determine the absolute value of the difference between the mean time between failures (MTBF) in two consecutive simulations. Is it less than the convergence threshold ∈? Simultaneously, determine the current simulation count N. Sim Is it greater than the maximum number of simulations N? iter ,like If convergence is achieved, the Monte Carlo algorithm simulation program terminates, and step S105 is executed; otherwise, step S106 is executed. This is to ensure that the first Monte Carlo algorithm simulation is not terminated.

[0078] Step S105: Update the fault-free operating time based on the current mean time between failures (MTBF) and update the long-term fault release time in a progressive manner according to the time step.

[0079] When the program terminates, i.e., the program that has been looping for the current long-term fault release time T_LT terminates, the process proceeds from step S104 to step S105, utilizing the current mean time between failures (MTBF). Update the fault-free operating time T LOTC ,Right now Simultaneously update the long-term fault release time T_LT, return to step S102, and start a new round of simulation.

[0080] Step S106: Obtain the number of the current faulty component and the current fault occurrence time of the current faulty component, and determine whether the component status value of the current faulty component was 1 before the current fault occurrence time.

[0081] In this embodiment, the current fault occurrence time and its number [minvalue, m] = min(T) of the currently faulty component are obtained. That is, the current fault occurrence time is the minimum value t in the time vector T. m (m = 1, 2, ..., n), that is:

[0082] t m =min t i

[0083] i = 1, 2, ..., n

[0084] Determine t m Previously, the component status value s of the currently faulty component m was... m Is it 1?

[0085] If s m If the value is 1, then the currently faulty component m is at time t. m In the event of a time-lapse, the program enters and executes step S107 to determine whether the LOTC event has occurred.

[0086] If s m If the value is 0, it indicates that the fault release time of the current faulty component m has expired and maintenance is required. The program then proceeds to step S110 for maintenance processing.

[0087] Step S107: Set the component state value of the current faulty component to 0 and the failure rate of the current faulty component to 1. Calculate the instantaneous LOTC rate of the system based on the minimum cut set data, and determine whether the LOTC event has occurred based on the instantaneous LOTC rate of the system.

[0088] If the currently faulty component m is in t m If a fault occurs at any time, then let s m =0,λ m =1, calculate the instantaneous LOTC rate λ of the system based on the minimum cut set data. LOTC And determine the instantaneous LOTC rate λ of the system. LOTC Is it less than 1? If λ LOTC If the result is ≥1, then the LOTC event is determined to have occurred, and the program proceeds to step S108; otherwise, step S109 is executed.

[0089] In a specific embodiment, this multi-fault time-constrained dispatch analysis method employs a fault tree data structure. By parsing the input minimal cutset data, the corresponding LOTC event fault structure function is obtained, and then the instantaneous LOTC rate of the system is further calculated. The following section uses the minimal cutset data for all single fault states E, A, B, C, D, F, and G as an example to provide a detailed introduction to this fault tree data structure.

[0090] The corresponding LOTC event fault structure function can be expressed as:

[0091] LOTC=A·B·(C+D)+E+F·G

[0092] =A·B·C+A·B·D+E+F·G

[0093] In the above formula, "+" represents "or" in Boolean operations, and "·" represents "and" in Boolean operations.

[0094] Here, E represents a mechanical-hydraulic fault. A fault with only one component in the minimal cut set is a mechanical-hydraulic fault. Based on the single fault states and the minimal cut set data analysis, single faults are combined in pairs to obtain all double fault states. Double faults do not include mechanical faults. In this example, the identified double faults are: AB, AC, AD, AF, AG, BC, BD, BF, BG, CD, CF, CG, DF, DG, FG.

[0095] If we want to calculate the instantaneous LOTC rate of the system caused by the failure of component B, then let B in the above formula be true, that is:

[0096] LOTC B =A·(C+D)+E+F·G

[0097] =A·C+A·D+E+F·G

[0098] The instantaneous LOTC rate λ of the system caused by the failure of component B LOTC / B This can be expressed as:

[0099] λ LOTC / B =λ A ·λ C +λ A ·λ D +λ E +λ F ·λ G

[0100] In the above formula, λ A , λ C , λ D , λ E , λ F , λ G Failure rate for each component.

[0101] When the instantaneous LOTC rate of the system is greater than or equal to 1, the LOTC event occurs; otherwise, it does not. Since the minimum cut set corresponding to a mechanical fault is a single component, when calculating the instantaneous LOTC rate of a mechanical fault, as in the example above, if E is set to true, the result obtained by substituting the failure rate into 1 will definitely be greater than or equal to 1, and the LOTC event will occur. The calculation of the instantaneous LOTC rate of a system caused by a dual fault is similar to that of a single fault. Based on the above steps, the instantaneous LOTC rates of all systems caused by single faults and dual faults can be obtained.

[0102] Step S108: Obtain and update the fault-free working time based on the current fault occurrence time, and initialize the failure rate of each component to the original failure rate. All elements of the state vector are 1, and all elements of the time vector are exponentially distributed random numbers with one-third of the failure rate of each component as the parameter. At the same time, increment the simulation count by 1.

[0103] When a LOTC event occurs, record the fault-free operating time, update the status, return to step S104, and repeat the Monte Carlo algorithm simulation. Specifically, obtain the fault-free operating time obtained from the current Monte Carlo algorithm simulation, i.e.:

[0104]

[0105] In the above formula, Let t be the fault-free uptime obtained from the Nth Monte Carlo simulation. m This represents the current time when the fault occurred.

[0106] At the same time, initialize the failure rate λ of each component. i Given the original failure rate, reinitialize the state vector S = [s1 s2 ... s... n All elements s in ] j All t values ​​are 1, initialize the time variable T = [t1 t2 … t n All elements t in ] i All are random numbers that follow an exponential distribution, and the parameter for taking values ​​in the exponential distribution is... After that, N Sim =N Sim +1, return to step S104, and repeat the Nth iteration. Sim +1 Monte Carlo algorithm simulation.

[0107] Step S109: Determine the dispatch type based on the system's instantaneous LOTC rate for dispatch processing.

[0108] In this embodiment, the dispatch type for engine control system faults is divided into three categories: non-dispatch type, short-time dispatch type, and long-time dispatch type. Specifically, the non-dispatch type (ND) is not allowed when system performance does not meet model design approval, the system loses critical functions, or the system's instantaneous LOTC rate exceeds a preset threshold; the fault must be repaired immediately. The short-time dispatch type (ST) is allowed when system redundancy is severely lost or the system's instantaneous LOTC rate is within a first preset threshold range; the fault can be dispatched for a short time interval T_ST, and the fault must be repaired within T_ST. The long-time dispatch type (LT) is allowed when the system's instantaneous LOTC rate is within a second preset threshold range; the fault can be dispatched for a longer time interval T_LT (T_LT > T_ST), and the fault must be repaired within T_LT.

[0109] The dispatch type for single (or dual) faults is determined by the resulting instantaneous LOTC rate of the system. In some embodiments, the specific determination method for the dispatch type is as follows: if the instantaneous LOTC rate of the system is greater than a first preset LOTC rate threshold, it is determined to be a non-dispatch type; if the instantaneous LOTC rate of the system is greater than a second preset LOTC rate threshold but not greater than the first preset LOTC rate threshold, it is determined to be a short-term dispatch type; if the instantaneous LOTC rate of the system is greater than a third preset LOTC rate threshold but not greater than the second preset LOTC rate threshold, it is determined to be a long-term dispatch type. The first, second, and third preset LOTC rate thresholds are flexibly adjustable and can be set according to specific circumstances. Furthermore, the dispatch handling method for long-term dispatch type faults is any one of long-term dispatch handling, short-term dispatch handling, or non-dispatch handling; the dispatch handling method for short-term dispatch type faults is short-term dispatch handling; and the dispatch handling method for non-dispatch type faults is non-dispatch handling. That is to say, short-term dispatch or no dispatch is generally allowed for long-term dispatch faults, but not allowed otherwise.

[0110] In this embodiment, the dispatch processing of the multi-failure time-limited dispatch analysis method specifically includes: dispatching the currently faulty component; re-dispatching all faulty long-term dispatch type components in the system; and re-dispatching all faulty short-term dispatch type components in the system. After the dispatch processing is completed, the process returns to step S106 to re-determine whether the current time is the time when the current faulty component failed.

[0111] In some specific embodiments, dispatching the currently faulty component includes:

[0112] Determine if the current long-term fault release time T_LT of the faulty component m is 0. If it is, i.e. the release time is 0, it means that the component cannot continue to be released, and the faulty component m is immediately repaired; otherwise, proceed with the following steps:

[0113] Determine whether there are two or more other faulty components besides the currently faulty component m, and determine the dispatch type of all faulty component combinations. If the currently faulty component m is faulty and there are two or more other faulty components, then all faulty component combinations are classified as non-dispatch type, and all faulty components are immediately repaired. Otherwise, based on the dispatch type of the currently faulty component m, it is handled individually.

[0114] If the dispatch type of the currently faulty component m is non-dispatch type, perform immediate repair on the currently faulty component m, and set the component status value s of the currently faulty component. m Set the initial failure rate λ of the currently faulty component m to 1. m To update the state vector t, we get the original failure rate. m =t m +Trand m , where t m Trand represents the time of failure or repair of the currently faulty component m. m The lifetime value of the currently faulty component m is randomly generated according to an exponential distribution, and the lifetime is accumulated and updated, thereby accumulating the fault-free working time of the system.

[0115] If the dispatch type of the currently faulty component m is short-term dispatch, then temporarily allow the current faulty component m to proceed, and update the state vector, t. m =t m +T_ST, where t m T_ST represents the time when the fault occurs or is repaired in the currently faulty component m, and T_ST represents the short-term fault release time.

[0116] If the dispatch type of the currently faulty component m is long-term dispatch, allow the current faulty component m to proceed for a long time, and update the state vector, t. m =t m +T_LT, where t m T_LT represents the time when the fault occurs or is repaired in the current faulty component m, and T_LT represents the long-term fault release time.

[0117] In other specific embodiments, all faulty long-term dispatch type components in the system are re-dispatched, specifically including:

[0118] If the current faulty component m and the long-term dispatch type component k are combined and the dispatch type is non-dispatch type, then the current faulty component m and the long-term dispatch type component k are immediately repaired.

[0119] If the current faulty component m and the long-term dispatch type component k are combined into a short-term dispatch type, then update the state vector t. k =t k -T_LT+T_ST, and determine the updated t k If the timeout is less than the minimum value, meaning the release time is less than the current system time, indicating that maintenance was needed much earlier, then the long-term dispatch type component k should be immediately maintained; otherwise, it should be maintained according to the updated time. k Continue to allow long-term dispatch type component k to proceed. Simultaneously, continue to allow the currently faulty component m to proceed according to the release time of the combined fault of the currently faulty component m and the long-term dispatch type component k, where t... k T_ST represents the time when the fault occurs or is repaired for component k of the long-term dispatch type, T_LT represents the short-term fault release time, T_LT represents the long-term fault release time, and minvalue represents the current system time.

[0120] If the current faulty component m and the long-term dispatch type component k are combined and the dispatch type is long-term dispatch type, then determine t. k If the timeout is less than the minimum value, meaning the release time is less than the current system time, indicating that maintenance was needed much earlier, then the long-term dispatch type component k should be immediately maintained; otherwise, it should be maintained according to time t. k Continue to allow long-term dispatch type component k to proceed. Simultaneously, continue to allow the currently faulty component m to proceed according to the release time of the combined fault of the currently faulty component m and the long-term dispatch type component k, where t... k The time of failure or repair for long-term dispatch type component k is given, and minvalue is the current system time.

[0121] In other specific embodiments, all faulty short-term dispatch type components in the system are re-dispatched, specifically including:

[0122] If the combined dispatch type of the currently faulty component m and the short-term dispatch type component x is non-dispatch type, then perform immediate repair on the currently faulty component m and the short-term dispatch type component x; otherwise, determine t. x Is it less than the minimum value, where t x The minimum value represents the time when the fault occurs or is repaired for short-term dispatch type component x, and the minimum value represents the current system time.

[0123] If so, then the short-term dispatch type component x will be repaired immediately; otherwise, the short-term dispatch type component x will continue to be released. At the same time, the release time of the combined fault of the current faulty component m and the short-term dispatch type component x will be used to continue to release the current faulty component m.

[0124] Step S110: Perform repair processing and update the component status value of the repaired component.

[0125] In this embodiment, the repair process includes incomplete repair and complete repair. Incomplete repair refers to repairing only the currently faulty component m and updating the component status value of the currently faulty component m; complete repair refers to repairing all faulty components that have been released (i.e., s...). k All components with a timeout value of 0 are subjected to unified maintenance. The time of all faulty components that have been released is synchronized to the current time of the fault occurrence, and their fault-free operating time is updated respectively, i.e., their lifespan value is added to each component. Then, the component status value of all related components is updated. After the maintenance is completed, return to step S106 to re-determine whether the current time is the time of the fault occurrence of the current faulty component.

[0126] Corresponding to the above method embodiments, this application also provides a multi-failure time-constrained dispatch analysis system based on the Monte Carlo algorithm, used to execute the steps of the multi-failure time-constrained dispatch analysis method based on the Monte Carlo algorithm in the above embodiments. Figure 2 As shown, the multi-fault time-limited dispatch analysis system includes: a first control module 201, a first judgment module 202, a second control module 203, a second judgment module 204, a third control module 205, a third judgment module 206, a fourth judgment module 207, a fourth control module 208, a fifth control module 209, and a sixth control module 210.

[0127] Specifically, the first control module 201 is used to set the simulation parameters of the Monte Carlo algorithm and enter the simulation loop. The Monte Carlo algorithm simulation parameters include the maximum long-term fault release time, time step, short-term fault release time, maximum number of simulations, and convergence threshold.

[0128] The first judgment module 202 is used to obtain the current long-term fault release time and, based on the maximum long-term fault release time and the long-term fault release time, determine whether the simulation has ended. If so, the simulation ends; otherwise, the second control module 203 is triggered.

[0129] The second control module 203 is used to initialize global variables. Specifically, the initial value of the number of simulations is set to 1, the initial value of the fault-free operating time is set to 0, the initial failure rate of each component is set to the original failure rate, all elements of the initial value of the state vector are set to 1, and all elements of the initial value of the time vector are exponentially distributed random numbers with a parameter of one-failure rate of each component.

[0130] The second judgment module 204 is used to obtain the absolute value of the difference between the current number of simulations and the mean time between failures (MTBF) of the two adjacent simulations, and to judge the running status of the Monte Carlo algorithm simulation program based on the maximum number of simulations, the current number of simulations, the convergence threshold, and the absolute value of the difference between the two adjacent simulations. If the program terminates, the third control module 205 is triggered; otherwise, the third judgment module 206 is triggered.

[0131] The third control module 205 updates the fault-free operating time based on the current mean time between failures (MTBF) and updates the long-term fault release time in a progressive manner according to the time step. It then returns to the first judgment module 202 to begin a new round of simulation. In the simulation loop, the long-term fault release time starts from 0 and is updated progressively according to the time step.

[0132] The third judgment module 206 is used to obtain the number of the current faulty component and the current fault occurrence time of the current faulty component, and to determine whether the component status value of the current faulty component was 1 before the current fault occurrence time. If so, the fourth judgment module 207 is triggered; otherwise, the sixth control module 210 is triggered.

[0133] The fourth judgment module 207 is used to set the component status value of the current faulty component to 0 and the failure rate of the current faulty component to 1. It calculates the instantaneous LOTC rate of the system based on the minimum cut set data, and judges whether the LOTC event has occurred based on the instantaneous LOTC rate of the system. If it has, the fourth control module 208 is triggered; otherwise, the fifth control module 209 is triggered.

[0134] The fourth control module 208 is used to obtain and update the fault-free working time based on the current fault occurrence time, initialize the failure rate of each component to the original failure rate, set all elements of the state vector to 1, set all elements of the time vector to an exponentially distributed random number with a parameter of one-third of the failure rate of each component, increment the simulation count by 1, return to the second judgment module 204, and repeat the Monte Carlo algorithm simulation.

[0135] The fifth control module 209 is used to determine the dispatch type based on the system's instantaneous LOTC rate for dispatch processing. After the dispatch processing is completed, it returns to the third judgment module 206. The dispatch types include non-dispatch type, short-term dispatch type, and long-term dispatch type.

[0136] The sixth control module 210 is used for maintenance processing and updates the component status value of the components that have completed maintenance. After the maintenance processing is completed, it returns to the third judgment module 206. Among them, when performing unified maintenance processing on all components that have been released due to failure in the system, the time of all components that have been released due to failure is synchronized to the current time of failure, and their fault-free working time is updated respectively.

[0137] It should be noted that the multi-fault time-constrained dispatch analysis system based on the Monte Carlo algorithm provided in this application is based on the same concept as the multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm in this application, and its technical effects are the same as those of the multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm in this application. For details, please refer to the description in the multi-fault time-constrained dispatch analysis method embodiment based on the Monte Carlo algorithm in this application, which will not be repeated here.

[0138] This application also provides a multi-failure time-constrained dispatch analysis device based on the Monte Carlo algorithm, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the multi-failure time-constrained dispatch analysis method embodiments based on the Monte Carlo algorithm described above. Alternatively, when the processor executes the computer program, it implements the functions of each module in the multi-failure time-constrained dispatch analysis system embodiments based on the Monte Carlo algorithm described above.

[0139] In one specific embodiment, the computer program can be divided into one or more modules, which are stored in memory and executed by a processor to complete the embodiments of this application. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, describing the execution process of the computer program in the Monte Carlo algorithm-based multi-failure time-constrained dispatch analysis device. For example, the computer program can be divided into a first control module, a first judgment module, a second control module, a second judgment module, a third control module, a third judgment module, a fourth judgment module, a fourth control module, a fifth control module, and a sixth control module, with the specific functions of each module as follows:

[0140] The first control module is used to set the simulation parameters of the Monte Carlo algorithm and enter the simulation loop. The Monte Carlo algorithm simulation parameters include the maximum long-term fault release time, time step, short-term fault release time, maximum number of simulations, and convergence threshold.

[0141] The first judgment module is used to obtain the current long-term fault release time and, based on the maximum long-term fault release time and the long-term fault release time, determine whether the simulation has ended. If so, the simulation ends; otherwise, the second control module is triggered.

[0142] The second control module is used to initialize global variables. Specifically, the initial value of the number of simulations is set to 1, the initial value of the fault-free operating time is set to 0, the initial failure rate of each component is set to the original failure rate, all elements of the initial value of the state vector are set to 1, and all elements of the initial value of the time vector are exponentially distributed random numbers with a parameter of one-failure rate of each component.

[0143] The second judgment module is used to obtain the absolute value of the difference between the current number of simulations and the mean time between failures (MTBF) of the two adjacent simulations. Based on the maximum number of simulations, the current number of simulations, the convergence threshold, and the absolute value of the difference between the two adjacent simulations, the module judges the running status of the Monte Carlo algorithm simulation program. If the program terminates, the third control module is triggered; otherwise, the third judgment module is triggered.

[0144] The third control module updates the fault-free operating time based on the current mean time between failures (MTBF) and updates the long-term fault release time in a progressive manner according to the time step. It then returns to the first judgment module to start a new round of simulation. In the simulation loop, the long-term fault release time starts from 0 and is updated progressively according to the time step.

[0145] The third judgment module is used to obtain the number of the current faulty component and the current fault occurrence time of the current faulty component, and to determine whether the component status value of the current faulty component was 1 before the current fault occurrence time. If so, the fourth judgment module is triggered; otherwise, the sixth control module is triggered.

[0146] The fourth judgment module is used to set the component status value of the current faulty component to 0 and the failure rate of the current faulty component to 1. It calculates the instantaneous LOTC rate of the system based on the minimum cut set data, and judges whether the LOTC event has occurred based on the instantaneous LOTC rate of the system. If it has, the fourth control module is triggered; otherwise, the fifth control module is triggered.

[0147] The fourth control module is used to obtain and update the fault-free working time based on the current fault occurrence time, initialize the failure rate of each component to the original failure rate, set all elements of the state vector to 1, set all elements of the time vector to an exponentially distributed random number with a parameter of one-failure rate of each component, increment the simulation count by 1, return to the second judgment module, and repeat the Monte Carlo algorithm simulation.

[0148] The fifth control module determines the dispatch type based on the system's instantaneous LOTC rate for dispatch processing. After dispatch processing is complete, it returns to the third judgment module. The dispatch types include non-dispatch type, short-term dispatch type, and long-term dispatch type.

[0149] The sixth control module is used for maintenance processing and updates the component status value of the components that have completed maintenance. After the maintenance processing is completed, it returns to the third judgment module. Specifically, when performing unified maintenance processing on all components that have been released after failure in the system, the time of all components that have been released after failure is synchronized to the current time of failure, and their fault-free operating time is updated respectively.

[0150] The Monte Carlo algorithm-based multi-failure time-constrained dispatch analysis device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud management server. Those skilled in the art will understand that the Monte Carlo algorithm-based multi-failure time-constrained dispatch analysis device may include, but is not limited to, a processor and memory, and may also include more or fewer components, or combinations of certain components, or different components. For example, the Monte Carlo algorithm-based multi-failure time-constrained dispatch analysis device may also include input / output devices, network access devices, buses, etc.

[0151] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor, or it can be any conventional processor.

[0152] The memory can be an internal storage unit of the Monte Carlo algorithm-based multi-failure time-constrained dispatch analysis device, such as a hard disk or RAM. Alternatively, it can be an external storage device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital Card (SD card), or Flash Card. Furthermore, the memory can include both internal and external storage units. This memory stores computer programs and other programs or data required by the Monte Carlo algorithm-based multi-failure time-constrained dispatch analysis device. It can also temporarily store data that has been output or will be output.

[0153] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0154] Those skilled in the art will recognize that the modules and algorithm steps of the various embodiments described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0155] Furthermore, this application embodiment also provides a computer-readable storage medium storing a computer program that can be executed by a processor to perform the multi-failure time-constrained dispatch analysis method based on the Monte Carlo algorithm as described in the above embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), etc.

[0156] In summary, this application discloses a multi-fault time-constrained dispatch analysis method and system based on the Monte Carlo algorithm, which solves the problem of reasonable allocation of time-constrained dispatch in redundant structures with multiple faults. It provides clear guidance on maintenance and dispatch strategies, adopts maintenance strategies that are easy to operate and require minimal workload, and is more practical and operable. It can be applied to the fields of multi-fault TLD analysis, redundancy maintenance decision-making, and reliability analysis of aircraft.

[0157] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0158] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm, characterized in that, The multi-fault time-constrained dispatch analysis method includes: S1. Set the simulation parameters of the Monte Carlo algorithm and enter the simulation loop; wherein, the simulation parameters of the Monte Carlo algorithm include the maximum long-term fault release time, time step, short-term fault release time, maximum number of simulations, and convergence threshold; S2. Obtain the current long-term fault release time, and determine whether the simulation has ended based on the maximum long-term fault release time and the long-term fault release time. If yes, the simulation ends; otherwise, proceed to step S3. S3. Initialize global variables; where the initial value of the number of simulations is 1, the initial value of the fault-free working time is 0, the initial failure rate of each component is the original failure rate, all elements of the initial value of the state vector are 1, and all elements of the initial value of the time vector are exponentially distributed random numbers with one-third of the failure rate of each component as the parameter. S4. Obtain the absolute value of the difference between the current number of simulations and the mean time between failures (MTBF) between the two current adjacent simulations, and determine the running status of the Monte Carlo algorithm simulation program based on the maximum number of simulations, the current number of simulations, the convergence threshold, and the absolute value of the difference between the two current adjacent simulations. If the program terminates, proceed to step S5; otherwise, proceed to step S6. S5. Update the fault-free operating time according to the current mean time between failures (MTBF) and update the long-term fault release time in a progressive manner according to the time step. Return to step S2 and start a new round of simulation. In the simulation loop, the long-term fault release time starts from 0 and is updated progressively according to the time step. S6. Obtain the number of the currently faulty component and the current time of the current fault occurrence of the currently faulty component, and determine whether the component status value of the currently faulty component was 1 before the current time of the current fault occurrence. If yes, proceed to step S7; otherwise, proceed to step S10. S7. Set the component state value of the currently faulty component to 0, and the failure rate of the currently faulty component to 1. Calculate the instantaneous LOTC rate of the system based on the minimum cut set data, and determine whether the LOTC event has occurred based on the instantaneous LOTC rate of the system. If yes, proceed to step S8; otherwise, proceed to step S9. S8. Obtain and update the fault-free working time based on the current fault occurrence time, and initialize the failure rate of each component to the original failure rate. All elements of the state vector are 1, and all elements of the time vector are exponentially distributed random numbers with one-third of the failure rate of each component as the parameter. At the same time, increment the simulation count by 1, return to step S4, and repeat the Monte Carlo algorithm simulation. S9. Determine the dispatch type based on the instantaneous LOTC rate of the system for dispatch processing, and return to step S6 after the dispatch processing is completed; wherein, the dispatch type includes non-dispatch type, short-term dispatch type and long-term dispatch type; S10. Perform maintenance processing and update the component status value of the component that has been repaired. After the maintenance processing is completed, return to step S6. When performing unified maintenance processing on all components that have been released due to failure in the system, synchronize the time of all components that have been released due to failure to the current time of failure, and update their fault-free working time respectively.

2. The multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm according to claim 1, characterized in that, The step of determining whether the simulation has ended based on the maximum long-term fault release time and the long-term fault release time specifically includes: Determine whether the long-term fault release time is not less than the maximum long-term fault release time. If it is greater, the simulation is determined to have ended; otherwise, the simulation is determined not to have ended.

3. The multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm according to claim 1, characterized in that, The step of determining the running status of the Monte Carlo algorithm simulation program based on the maximum number of simulations, the current number of simulations, the convergence threshold, and the absolute value of the difference between the mean time between failures (MTBF) of the two consecutive simulations includes: Determine whether the absolute value of the difference between the mean time between failures (MTBF) of the two current consecutive simulations is less than the convergence threshold. At the same time, determine whether the current number of simulations is greater than the maximum number of simulations. If the absolute value of the difference between the mean time between failures (MTBF) of the two current consecutive simulations is not less than the convergence threshold and the current number of simulations is not greater than the maximum number of simulations, then the program is determined not to have terminated; otherwise, the program is determined to have terminated.

4. The multi-fault time-constrained dispatch analysis method based on Monte Carlo algorithm according to claim 1, characterized in that, The determination of whether an LOTC event has occurred based on the instantaneous LOTC rate of the system specifically includes: Determine whether the instantaneous LOTC rate of the system is less than 1. If it is, determine that the LOTC event has not occurred; otherwise, determine that the LOTC event has occurred. The determination of the dispatch type based on the system's instantaneous LOTC rate specifically includes: If the instantaneous LOTC rate of the system is greater than the first preset LOTC rate threshold, it is determined to be a non-dispatch type; if the instantaneous LOTC rate of the system is greater than the second preset LOTC rate threshold but not greater than the first preset LOTC rate threshold, it is determined to be a short-term dispatch type; if the instantaneous LOTC rate of the system is greater than the third preset LOTC rate threshold but not greater than the second preset LOTC rate threshold, it is determined to be a long-term dispatch type.

5. The multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm according to claim 4, characterized in that, The dispatch handling method for the long-term dispatch type fault is any one of long-term dispatch handling, short-term dispatch handling, or non-dispatch handling; the dispatch handling method for the short-term dispatch type fault is short-term dispatch handling; and the dispatch handling method for the non-dispatch type fault is non-dispatch handling.

6. The multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm according to claim 1, characterized in that, The dispatching process in step S9 specifically includes: Dispatch the currently faulty component; Re-dispatch all long-term dispatch type components that have failed in the system; Re-dispatch all short-term dispatch type components that have failed in the system.

7. The multi-failure time-constrained dispatch analysis method based on the Monte Carlo algorithm according to claim 6, characterized in that, The dispatching process for the currently faulty component specifically includes: Determine whether the current long-term fault release time of the currently faulty component is 0. If so, perform immediate repair on the currently faulty component. Otherwise, determine whether there are two or more other faulty components besides the currently faulty component, and determine the dispatch type of all faulty component combinations. If such a combination exists and all faulty components are of the non-dispatch type, then all faulty components shall be repaired immediately; otherwise, each faulty component shall be handled individually according to its dispatch type. When the dispatch type of the currently faulty component is non-dispatch type, immediate repair processing is performed on the currently faulty component. The component state value of the currently faulty component is set to 1, the failure rate of the currently faulty component is initialized to the original failure rate, and the state vector is updated. m =t m +Trand m , where t m Trand is the time when the fault occurs or is repaired in the currently faulty component m. m The lifetime value is a value randomly generated according to an exponential distribution for the currently faulty component m; When the dispatch type of the currently faulty component is short-term dispatch, the currently faulty component is temporarily allowed to proceed, and the state vector is updated. m =t m +T_ST, where t m T_ST is the time when the fault occurs or is repaired in the currently faulty component m, and T_ST is the short-term fault release time. When the dispatch type of the currently faulty component is a long-term dispatch type, the currently faulty component is allowed to proceed for a long time, and the state vector is updated. m =t m +T_LT, where t m T_LT is the time when the fault occurs or is repaired in the currently faulty component m, and T_LT is the long-term fault release time.

8. The multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm according to claim 6, characterized in that, The re-dispatch process for all faulty long-term dispatch type components in the system specifically includes: If the combined dispatch type of the currently faulty component m and the long-term dispatch type component k is a non-dispatch type, then the currently faulty component m and the long-term dispatch type component k shall be repaired immediately. If the current faulty component m and the long-term dispatch type component k are combined into a short-term dispatch type, then update the state vector t. k =t k -T_LT+T_ST, and determine the updated t k If the value is less than the minimum value, then the long-term dispatch type component k is immediately repaired; otherwise, the updated value is used. k Continue to allow the long-term dispatch type component k to pass. Simultaneously, continue to allow the currently faulty component m to pass according to the release time of the combined fault of the currently faulty component m and the long-term dispatch type component k, where t... k T_ST is the fault occurrence or repair time of the long-term dispatch type component k, T_LT is the short-term fault release time, T_LT is the long-term fault release time, and minvalue is the current system time. If the current faulty component m and the long-term dispatch type component k are combined and the dispatch type is a long-term dispatch type, then determine t. k If the value is less than the minimum value, then the long-term dispatch type component k should be repaired immediately; otherwise, proceed according to t. k Continue to allow the long-term dispatch type component k to pass. Simultaneously, continue to allow the currently faulty component m to pass according to the release time of the combined fault of the currently faulty component m and the long-term dispatch type component k, where t... k The time when the fault occurs or is repaired for the long-term dispatch type component k is given, and minvalue is the current time of the system.

9. The multi-fault time-constrained dispatch analysis method based on the Monte Carlo algorithm according to claim 6, characterized in that, The re-dispatch process for all faulty short-term dispatch type components in the system specifically includes: If the combined dispatch type of the currently faulty component m and the short-term dispatch type component x is non-dispatch type, then the currently faulty component m and the short-term dispatch type component x are immediately repaired; otherwise, the condition is determined by t. x Is it less than the minimum value, where t x The time of failure or repair of the short-term dispatch type component x is given, and minvalue is the current system time. If so, the short-term dispatch type component x shall be repaired immediately; otherwise, the short-term dispatch type component x shall continue to be released. At the same time, the current faulty component m shall continue to be released according to the release time of the combined fault of the current faulty component m and the short-term dispatch type component x.

10. A multi-fault time-constrained dispatch analysis system based on the Monte Carlo algorithm, characterized in that, The multi-failure time-limited dispatch analysis system includes: The first control module is used to set the simulation parameters of the Monte Carlo algorithm and enter the simulation loop; wherein, the Monte Carlo algorithm simulation parameters include the maximum long-term fault release time, time step, short-term fault release time, maximum number of simulations, and convergence threshold; The first judgment module is used to obtain the current long-term fault release time and determine whether the simulation has ended based on the maximum long-term fault release time and the long-term fault release time. If so, the simulation ends; otherwise, the second control module is triggered. The second control module is used to initialize global variables; wherein, the initial value of the number of simulations is set to 1, the initial value of the fault-free working time is set to 0, the failure rate of each component is initialized to the original failure rate, all elements of the initial value of the state vector are 1, and all elements of the initial value of the time vector are exponentially distributed random numbers with one-third of the failure rate of each component as the parameter. The second judgment module is used to obtain the absolute value of the difference between the current number of simulations and the average fault-free operating time of the two current adjacent simulations, and to judge the running status of the Monte Carlo algorithm simulation program based on the maximum number of simulations, the current number of simulations, the convergence threshold, and the absolute value of the difference between the average fault-free operating time of the two current adjacent simulations. If the program terminates, the third control module is triggered; otherwise, the third judgment module is triggered. The third control module is used to update the fault-free operating time according to the current mean time between failures (MTBF) and update the long-term fault release time in a progressive manner according to the time step. Then, it returns to the first judgment module and performs a new round of simulation. In the simulation loop, the long-term fault release time starts from 0 and is updated progressively according to the time step. The third judgment module is used to obtain the number of the current faulty component and the current fault occurrence time of the current faulty component, and to determine whether the component status value of the current faulty component was 1 before the current fault occurrence time. If so, the fourth judgment module is triggered; otherwise, the sixth control module is triggered. The fourth judgment module is used to set the component state value of the current faulty component to 0, the failure rate of the current faulty component to 1, calculate the instantaneous LOTC rate of the system based on the minimum cut set data, and determine whether the LOTC event has occurred based on the instantaneous LOTC rate of the system. If it has, the fourth control module is triggered; otherwise, the fifth control module is triggered. The fourth control module is used to obtain and update the fault-free working time according to the current fault occurrence time, and initialize the failure rate of each component to the original failure rate. All elements of the state vector are 1, and all elements of the time vector are exponentially distributed random numbers with one-third of the failure rate of each component as the parameter. At the same time, the simulation count is incremented by 1, and the module returns to the second judgment module to re-perform the Monte Carlo algorithm simulation. The fifth control module is used to determine the dispatch type based on the instantaneous LOTC rate of the system for dispatch processing, and return to the third judgment module after the dispatch processing is completed; wherein, the dispatch type includes non-dispatch type, short-term dispatch type and long-term dispatch type; The sixth control module is used for maintenance processing and updating the component status value of the components that have completed maintenance. After the maintenance processing is completed, it returns to the third judgment module. When performing unified maintenance processing on all components that have been released due to failure in the system, the time of all components that have been released due to failure is synchronized to the current time of failure, and their fault-free working time is updated respectively.

Images