Event-driven equipment life-cycle operation and maintenance cost evaluation method and system

Abstract

The application discloses an equipment full-life operation and maintenance cost evaluation method and system based on event driving, and belongs to the technical field of equipment operation and maintenance management and cost analysis. The application firstly establishes an equipment hierarchical structure model containing sites, modules, systems, devices, replaceable units and life parts, constructs a discrete event-driven state evolution engine, and processes events in time sequence to promote equipment state updating; in the event processing process, operation and maintenance cost real-time collection is triggered synchronously, and each cost is bound with corresponding hierarchical objects and triggering events. The application realizes joint evaluation of equipment full-life operation and maintenance cost, support frequency and availability level, solves the problems of disconnection of cost and state evolution, poor process interpretability and non-uniform hierarchical expression in the prior art, can be adapted to various equipment scenes, and provides support for operation and maintenance scheme comparison and selection.

Description

Technical Field

[0001] This invention relates to the field of equipment operation and maintenance management and cost analysis technology, and in particular to an event-driven method and system for evaluating the operation and maintenance costs of equipment throughout its entire life cycle. Background Technology

[0002] Existing equipment operation and maintenance cost analysis techniques can be broadly categorized into three types. The first type is the static cost estimation method, which typically provides an estimate of the total annual cost or life-cycle cost based on empirical quotas, average annual maintenance frequency, average spare parts consumption, and annual budget targets. This method is simple to calculate and convenient for budget preparation, but it often compresses dynamic processes such as failures, maintenance, waiting, procurement, and support activities into average values, making it difficult to reflect the true cost fluctuations of complex equipment at different operating stages, under different task densities, and under different support conditions.

[0003] The second category is analytical calculation methods based on reliability or maintainability. These methods typically begin by establishing mathematical models for failure rates, mean time to repair (MTBT), and lifetime distribution, and then derive system availability, spare parts requirements, or lifecycle costs. These methods are suitable for problems with clear structures and strong assumptions. However, for scenarios involving multi-level equipment, multiple maintenance strategies, and various resource constraints, analytical expressions often require significant simplification and struggle to fully represent the coupling effects of discrete events such as corrective maintenance, preventative maintenance, inventory shortages, and external support tasks.

[0004] The third category is simulation-driven operation and maintenance assessment methods. These methods typically utilize discrete event simulation, Monte Carlo simulation, or agent modeling techniques to dynamically extrapolate changes in equipment status, maintenance activities, resource utilization, and cost accumulation. Compared to the first two categories, simulation methods can more realistically reproduce the entire process of equipment operation, failure, maintenance, recovery, and restart. Therefore, they are increasingly being used for support verification and scheme comparison for complex equipment.

[0005] However, existing simulation evaluation techniques still generally have the following shortcomings: First, cost statistics are disconnected from state evolution. Many models simply summarize costs after the simulation ends, only calculating costs after the fact based on the number of repairs or the quantity of spare parts. They fail to correlate costs with factors such as the event trigger time, waiting time, on-site operation duration, and procurement delays, resulting in a lack of process interpretability in the cost results and making it difficult to support solution optimization.

[0006] Secondly, there is a lack of a unified hierarchical object representation. Complex equipment typically presents a multi-layered nested relationship of objects such as sites, modules, systems, equipment, replaceable units, and life-cycle components. However, existing methods often model with a single device or component as the core, lacking an integrated representation from the structural hierarchy to the cost aggregation hierarchy. This makes it impossible to clearly define the hierarchical source of costs, the fault triggering chain, and the impact on system functionality.

[0007] Third, the planned window and corrective response are disconnected. In reality, equipment maintenance does not always occur immediately. Some failures trigger emergency responses, while others are delayed until a monthly window, planned window, major overhaul window, intermediate overhaul window, or minor overhaul window for unified execution. Existing models often model preventive maintenance and corrective maintenance separately, failing to incorporate them into the same event-driven framework. This results in key parameters such as window frequency being unable to be analyzed within the same model.

[0008] Fourth, the description of spare parts delivery delays and out-of-stock downtime mechanisms is insufficient. For replaceable units that cannot be repaired on-site or must be replaced, once the inventory is zero, the equipment will often remain down until the spare parts arrive. This process will simultaneously prolong downtime and increase peak costs. However, in existing methods, spare parts are either assumed to always be available on time or inventory requirements are only estimated at an average level, without incorporating the entire chain of procurement ordering, lead time, delivery recovery, and downtime continuation into the simulation's main loop.

[0009] Fifth, there is a lack of a comprehensive indicator system for selecting solutions. In engineering applications, the goal is often not simply to minimize costs, but to make trade-offs between availability, downtime, maintenance frequency, and inventory levels. Existing technologies often only output annual costs or average failure rates, which are difficult to directly apply to the engineering process of "solution comparison—constraint screening—recommendation decision".

[0010] In summary, existing technologies generally suffer from the following shortcomings: some methods focus on static analysis, making it difficult to effectively characterize state evolution and event propagation relationships during operation and maintenance; some methods, while able to describe the operation and maintenance process, lack sufficient support for cost formation mechanisms, cost composition decomposition, and unified indicator output; and some methods are built for specific application scenarios, with a narrow scope of application and limited universality and transferability. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of the prior art and provide an event-driven method and system for evaluating the full life cycle maintenance costs of equipment. This invention addresses the problems in the prior art, such as the disconnect between cost statistics and state evolution, inconsistent hierarchical object representation, separation between planned maintenance and restorative maintenance, insufficient modeling of spare parts shortage mechanisms, and lack of comprehensive indicators for scheme selection. It enables accurate calculation and clear explanation of the formation mechanism, time distribution, and scheme differences of the full life cycle maintenance costs of equipment, while taking into account the universality, process, interpretability, and scalability of the method.

[0012] The core technical concept of this invention is as follows: using a unified equipment hierarchical structure model as the object carrier and a discrete event-driven state evolution engine as the core driver, faults, lifespan, maintenance, inventory, support, and costs are integrated into the same timeline for unified advancement. Through the synchronous triggering of event processing and cost collection, a strong binding between costs and events and hierarchical objects is achieved. At the same time, it is equipped with a hierarchical maintenance response, inventory procurement linkage, multi-dimensional indicator output, and multi-solution comparison mechanism. The various technical features work together to jointly solve the core defects of existing technologies and achieve a joint assessment of equipment life-cycle operation and maintenance costs, support frequency, and availability level.

[0013] The first aspect: an event-driven method for assessing the full lifecycle maintenance costs of equipment.

[0014] An event-driven method for evaluating the lifecycle maintenance costs of equipment includes the following steps: Establish an equipment hierarchical structure model, which represents the hierarchical relationship between stations, modules, systems, equipment, replaceable units and life-use parts, as well as the operating status, fault attributes, maintenance attributes, inventory attributes and cost parameters of each level object, and establish a mapping relationship for the transmission of the status changes of lower level objects to upper level objects; A discrete event-driven state evolution engine is constructed, a set of discrete events is defined, and discrete events are processed sequentially in chronological order based on a future event table to drive the state update of each level object in the equipment hierarchical structure model. During the processing of the discrete events, the corresponding operation and maintenance costs are synchronously collected in real time, and each collected cost is bound to the corresponding level of object and the discrete event that triggered the cost.

[0015] Therefore, by using a unified equipment hierarchical structure model, an integrated representation from the bottom-level components to the upper-level system is achieved, solving the problem of inconsistent hierarchical expression of existing technical objects; by using a discrete event-driven state evolution engine, all kinds of dynamic processes in the entire equipment operation process are incorporated into the same timeline, fully reproducing the real evolution process of equipment operation and maintenance; by synchronously triggering event processing and cost collection, a strong binding between costs and events and hierarchical objects is achieved, solving the core problems of delayed cost statistics and unexplainable processes in existing technologies, and providing a foundation for accurate assessment and mechanism analysis of full life cycle costs.

[0016] This includes establishing a tiered maintenance response mechanism, which determines the handling path of a fault based on its impact on equipment operation and the criticality of the fault-causing object: whether to immediately trigger emergency maintenance, incorporate it into a pre-set planned maintenance window, or postpone it to a subsequent maintenance window.

[0017] Therefore, by adopting a graded maintenance response mechanism, different levels of faults are assigned different processing paths, which realizes unified management of emergency maintenance and planned maintenance. This solves the problem of the separation between the planning window and the corrective response in the existing technology and can adapt to the maintenance decision-making needs under different fault scenarios.

[0018] When the preset planned maintenance window arrives, the system scans the currently pending faults, components nearing their lifespan thresholds, and mergeable work items, combines them according to preset rules to form a maintenance package within the window, and performs merged maintenance.

[0019] Therefore, by combining and merging maintenance packages when the planned window arrives, the additional costs caused by repeated disassembly, reorganization, and dispatch are reduced. At the same time, preventive maintenance and restorative maintenance are incorporated into the same event-driven framework, enabling unified analysis and optimization of parameters such as maintenance window frequency.

[0020] This also includes establishing an inventory and procurement linkage mechanism, performing spare parts inventory checks based on maintenance needs, issuing spare parts when inventory is sufficient, generating purchase orders and recording delivery lead times when inventory is insufficient, and simultaneously maintaining the shutdown or downgraded operation status of the corresponding equipment until the spare parts arrive and the maintenance process is resumed.

[0021] Therefore, by linking inventory and procurement, the entire chain of inventory inspection, procurement ordering, waiting for delivery, and delivery resumption is incorporated into the simulation main loop. This accurately describes the consequences of long-term downtime caused by insufficient spare parts, solves the problem of insufficient description of spare parts delivery delays and shortage downtime mechanisms in existing technologies, and achieves precise linkage between downtime and cost changes.

[0022] The collection of maintenance costs includes calculating the maintenance task cost based on the resource occupation duration of a single maintenance activity-triggered support task. The support task cost is a separate cost category from preventive maintenance costs and restorative maintenance costs.

[0023] Therefore, by using a task-level cost calculation method, the billing is based on the resource occupation time of the task rather than simply on the number of repairs, which truly reflects the impact of remote support, centralized support, or on-site organization on the total cost. This solves the problem of the difficulty in accurately measuring the deployment cost of support in existing technologies and achieves the accurate collection and independent accounting of support task-related costs.

[0024] The collected maintenance costs include preventive maintenance costs, restorative maintenance costs, support mission costs, replacement unit procurement costs, replacement unit repair costs, life-cycle component replacement costs, and inventory holding costs.

[0025] Therefore, by classifying maintenance costs across all dimensions, the system covers all sources of costs throughout the entire lifecycle of equipment maintenance. Combined with the mechanism of synchronous event collection, it enables the traceability and decomposition of each cost, clearly presenting the formation mechanism and composition distribution of costs.

[0026] This also includes establishing fault and lifespan evolution rules. For the lifespan components, lifespan consumption is measured only when the equipment is in operation, and lifespan consumption is suspended during shutdown and maintenance. For the replaceable units, fault triggering logic is set, and response levels are divided according to fault attributes.

[0027] Therefore, by using differentiated fault and life evolution rules, the life consumption patterns of equipment in actual operation are accurately matched, while providing an input basis for the graded maintenance response mechanism, ensuring the consistency between equipment status evolution and actual operating scenarios, and improving the accuracy of assessment results.

[0028] Based on the cost aggregation results, corresponding operation and maintenance evaluation indicators are output according to the hierarchical objects, time dimensions, and cost triggering reasons of the equipment hierarchical structure model.

[0029] Therefore, by outputting multi-dimensional operation and maintenance evaluation indicators, the cost data can be broken down and analyzed in multiple dimensions. This can clearly answer the questions of "which level the cost comes from, which fault chains trigger it, and which system functions are ultimately affected", providing data support for the refined management of equipment operation and maintenance.

[0030] In this process, by adjusting the values ​​of maintenance window frequency, maintenance intensity, and inventory parameters, multiple sets of operation and maintenance plans are generated. After simulation evaluation, the comparison and selection of multiple plans are completed based on the output operation and maintenance evaluation indicators.

[0031] Therefore, through simulation evaluation and comparative screening of multiple schemes, a comprehensive set of indicators that can be directly used for engineering decision-making has been formed, which solves the problem that existing technologies lack a comprehensive indicator system for scheme screening. It can support users to make trade-offs between availability level, downtime, support frequency and inventory investment, and screen the operation and maintenance scheme that meets the constraints and is economically optimal.

[0032] The second aspect: an event-driven equipment lifecycle maintenance cost assessment system. An event-driven equipment lifecycle maintenance cost assessment system includes an object modeling module, an event scheduling module, and a cost engine module. The object modeling module is used to establish an equipment hierarchical structure model. The equipment hierarchical structure model represents the hierarchical relationship between stations, modules, systems, equipment, replaceable units and life-use parts, as well as the operating status, fault attributes, maintenance attributes, inventory attributes and cost parameters of each level object, and establishes a mapping relationship for the transmission of the status changes of lower-level objects to upper-level objects. The event scheduling module is used to construct a discrete event-driven state evolution engine, define a discrete event set, process discrete events sequentially in chronological order based on a future event table, and drive the state update of objects at each level in the equipment hierarchical structure model. The cost engine module is used to synchronously trigger the real-time aggregation of corresponding operation and maintenance costs during the processing of discrete events, and to bind each aggregated cost to the corresponding level of object and the discrete event that triggered the cost.

[0033] Thus, through a modular system architecture, the integrated execution of equipment hierarchical modeling, event-driven state evolution, and real-time cost collection is achieved. The modules work together to fully realize the evaluation method of this invention, which has good scalability and can be expanded with more functional modules as needed to adapt to the operation and maintenance cost evaluation requirements of different types of equipment. Attached Figure Description

[0034] Figure 1 This is an overall architecture diagram of the event-driven equipment life-cycle maintenance cost assessment system according to an embodiment of the present invention; Figure 2 This is an event-driven state evolution flowchart of the event-driven equipment life-cycle maintenance cost assessment method according to an embodiment of the present invention. Detailed Implementation

[0035] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description, the same reference numerals are used for the same parts, and repeated descriptions are omitted. Furthermore, the drawings are merely schematic diagrams, and the proportions of the parts or the shapes of the parts may differ from the actual figures.

[0036] This invention constructs a comprehensive equipment lifecycle maintenance cost assessment scheme by employing a unified equipment hierarchical structure model, a discrete event-driven state evolution engine, and an event-synchronized real-time cost collection mechanism, combined with supporting mechanisms such as tiered maintenance response, inventory procurement linkage, and multi-solution comparison. This scheme balances versatility, process-orientation, interpretability, and scalability. The various technical features are synergistic and interconnected, jointly addressing the existing technical problems of disconnected cost statistics and state evolution, inconsistent hierarchical representation, poor process interpretability, and difficulty in supporting scheme comparison. Among them, the equipment hierarchical structure model provides a unified object foundation for all state evolution, maintenance decisions, and cost collection; the discrete event-driven state evolution engine provides a timeline and driving core for the dynamic simulation of the entire process, and all maintenance decisions, inventory processing, and cost collection are executed based on event triggers; the real-time cost collection mechanism is the core value of this invention, realizing a strong binding between costs and events and hierarchical objects, and solving the core pain point of the inexplicable cost process in existing technologies; mechanisms such as hierarchical maintenance response, inventory procurement linkage, and fault life evolution provide support for the authenticity of state evolution and the accuracy of cost collection, while expanding the applicable scenarios of the method; the multi-dimensional indicator output and scheme comparison mechanism realizes the engineering implementation of evaluation results and directly supports the selection and decision-making of operation and maintenance schemes.

[0037] In this embodiment, the event-driven equipment lifecycle maintenance cost assessment method provided by the present invention includes three core steps, which are sequentially supported and interconnected, forming the core framework of the method of the present invention.

[0038] The first step is to establish a hierarchical structure model for the equipment. In this implementation, the equipment to be evaluated is abstracted from top to bottom into six levels: site, module, system, equipment, replaceable unit, and life-sustaining component. Each level of object includes identification information, hierarchical relationship, working status, fault status, maintenance attributes, inventory attributes, cost parameters, and time parameters. The site is the highest level object, corresponding to an equipment cluster or fixed site; the module corresponds to the functional unit under the site; the system corresponds to the functional system under the module; the equipment corresponds to the independent physical equipment under the system; the replaceable unit is the smallest unit within the equipment that can be replaced on-site; and the life-sustaining component is a part with a fixed service life that needs to be replaced periodically. Simultaneously, a mapping relationship is established for the transmission of lower-level object status changes to upper-level objects. For example, when a replaceable unit fails, the availability status of its associated equipment, system, module, and even the site will be updated synchronously. This achieves a complete mapping from the status of lower-level components to the functions of upper-level systems and then to the overall availability level, solving the problem of inconsistent object hierarchy representation in existing technologies and providing a unified object carrier for subsequent status evolution, maintenance decisions, and cost collection.

[0039] In this embodiment, equipment objects are modeled hierarchically according to sites, modules, systems, equipment, replaceable units, and life-sustaining components. Sites represent the overall objects of equipment operation or support; modules represent functional sub-items under a site; systems represent functional systems under a module; equipment represents independent equipment under a system; replaceable units represent faulty, repairable, and replaceable components within equipment; and life-sustaining components represent components with a fixed lifespan that require replacement based on a lifespan threshold. This hierarchical relationship is determined by the object number, parent object number, and object type in the basic input data.

[0040] Furthermore, in the simulation embodiment, equipment hierarchy data is provided by the Equipment Hierarchy table, with fields including EQUID, DESCROC, TYPE, PID, KeyEquip, EMC, PMCost, etc. Replaceable unit data is provided by the LRU Repair table, with fields including LRUID, PID, NUM, MTBF, MinWorkQty, PCF, RepT, DiagT, REPABLE, REPCOST, LRUCOST, SPAREQTY, PurchaseQuantity, LeadTime, etc. Life-limited component data is provided by the life-limited component table, with fields including moduleID, systemID, EQUIPID, LlpName, LlpLife, quantity field, and LLP_COST, etc. Maintenance activity rules are provided by the Maintenance Activity table, with fields including PMID, COST, LABORWD, INTERVAL, DURATION, etc.

[0041] Furthermore, state changes of lower-level objects are propagated to upper-level objects. Specifically, when a replaceable unit fails, it is first determined whether its associated equipment is critical, and then whether the failure affects operation. If the number of healthy replaceable units of the same type is lower than the minimum healthy quantity (MinWorkQty) required for equipment operation, the failure is marked as affecting operation and further impacts the equipment's availability. The equipment status is then propagated to its associated system, module, and site, thereby mapping component-level failures or lifespan replacement requirements to upper-level equipment availability.

[0042] The second step is to construct a discrete event-driven state evolution engine. In this implementation, a discrete event set is first defined, which includes at least events such as start of operation, end of operation, fault triggering, lifespan threshold reaching, planned window reaching, purchase order generation, spare parts arrival, and maintenance completion. Simultaneously, a future event table is maintained. The system retrieves events from the future event table in chronological order and executes them sequentially. Each execution of an event synchronously updates the state of the corresponding hierarchical objects in the equipment hierarchy model. For example... Figure 2 The event-driven state evolution flowchart shown illustrates how equipment enters normal operation from the start of operation. When a fault triggers or a lifespan limit event occurs, the equipment enters the maintenance response judgment stage. Based on the judgment result, the corresponding processing procedure is executed. The entire process is driven by discrete events, incorporating the entire process of equipment operation—fault—maintenance—recovery—restart into the same timeline. This fully reproduces the real evolution process of equipment operation and maintenance, solving the problem that static analysis in existing technologies cannot adequately represent dynamic processes.

[0043] In this implementation, state evolution does not employ a fixed-step, uniform refresh of all objects. Instead, it is achieved through agents, state graphs, events, and message triggering mechanisms within the simulation model. The model includes objects such as Main, Site, Module, system, Equipment, LRU, llp, Depository, and Transporter. Main is responsible for global parameters, the maintenance waiting pool, the maintenance window, cost statistics, and result output; Equipment is responsible for determining the status of replaceable units within the equipment and for determining equipment-level availability; LRU is responsible for fault, maintenance waiting, repair, and availability statistics; llp is responsible for lifetime consumption and replacement requirements for life-sustaining parts; Depository is responsible for spare parts quantity, procurement lead time, and inventory costs; and Transporter is used to represent the cost of support or deployment tasks.

[0044] Furthermore, the discrete event set can be described according to the actual triggering process in the model as follows: operation timing events, replaceable unit failure events, lifespan consumption events, lifespan replacement demand events, preventive maintenance window arrival events, minor repair events, medium repair events, major repair events, periodic support window events, repair start events, repair completion events, spare parts inventory check events, procurement ordering events, spare parts arrival events, annual cost statistics events, and monthly indicator statistics events. All of these events revolve around equipment operation, failures, maintenance, inventory, support tasks, and cost statistics.

[0045] Furthermore, in the Main state diagram, it initially enters the "normal operation" state and determines the maintenance window based on the remaining time corresponding to minor, medium, and major repairs. When the "remaining time for major repair" is less than or equal to zero, it enters the "major repair" state; when the "remaining time for medium repair" is less than or equal to zero, it enters the "medium repair" state; and when the "remaining time for minor repair" is less than or equal to zero, it enters the "minor repair" state. After entering the corresponding maintenance window, the model records the corresponding number of times, triggers the support task, and calls the window maintenance execution logic; after the window duration reaches the set value, it returns to "normal operation" and resets the remaining time of the corresponding maintenance cycle.

[0046] The rules for determining preventive maintenance cycles are as follows: if the remaining time for a major overhaul is not greater than zero, then a major overhaul is performed first; otherwise, if the remaining time for a medium overhaul is not greater than zero, then a medium overhaul is performed; otherwise, if the remaining time for a minor overhaul is not greater than zero, then a minor overhaul is performed; if none of the above conditions are met, then normal operation continues. The cycles and durations of minor, medium, and major overhauls are determined by the INTERVAL and DURATION fields in the MaintenanceActivity table, and the model converts them to hours for state diagram triggering.

[0047] The third step involves simultaneously triggering the real-time collection of corresponding operation and maintenance costs during the processing of discrete events. In this implementation, the system registers and collects the corresponding costs while processing each discrete event, rather than summarizing and converting them after the simulation ends. Furthermore, each collected cost is uniquely bound to the corresponding level of equipment object and the discrete event that triggered the cost. For example, if a fault in a replaceable unit triggers a maintenance event, the repair cost generated by this event will be directly bound to the replaceable unit object. Simultaneously, the time and cause of the fault event that triggered the cost are recorded, achieving full-process traceability of costs and solving the core problems of existing technologies where cost statistics are disconnected from state evolution and the process is unexplainable.

[0048] In this embodiment, a graded maintenance response mechanism is also included. This mechanism is based on the equipment hierarchical structure model and state evolution engine, which provides differentiated decision-making paths for fault handling and provides more granular triggering basis for cost collection.

[0049] Furthermore, the tiered maintenance response mechanism is achieved through the replacement unit status, equipment criticality, impact on operation, and a maintenance waiting pool. The model first determines whether the equipment to which the faulty object belongs is critical equipment, and then determines whether the fault prevents the equipment from meeting the minimum health quantity requirements or directly affects operation, thereby forming four maintenance priorities (as shown in the table below).

[0050]

[0051] Furthermore, after the replaceable units awaiting repair are added to the global repair pool, the model sorts them according to the rule of "priority category ascending, waiting time descending, and object number ascending." That is, the lower the priority value, the earlier it is processed; within the same priority, the longer the waiting time, the earlier it is processed; if both priority and waiting time are the same, they are sorted by object number to avoid unstable processing order for the same batch of objects. This rule corresponds to the logic in the model such as "adding to the repair LRU," "obtaining the sorted repair list," and "performing window repair."

[0052] Furthermore, when a repair window arrives, the model first obtains the sorted list of objects to be repaired, and then determines the number of objects that can be repaired this time according to the window capacity. The window capacity is determined by "repair intensity × window duration" and rounded down; the actual number of objects to be repaired this time is the smaller value between the number of candidate objects and the window capacity. This rule can be expressed as: Nw=min(Ncand, floor(r×Tw)), where Ncand is the number of current candidate objects to be repaired, r is the repair intensity, and Tw is the window duration. The model then selects objects sequentially from the front of the sorted queue and sends a "start repair" message.

[0053] In this implementation, based on the degree of impact of the fault on equipment operation and the criticality of the fault object, faults are divided into four response branches: the first category is faults affecting operation and occurring on critical equipment, which immediately trigger emergency support tasks and emergency repairs; the second category is faults affecting operation but not on critical equipment, which are included in the most recent monthly planned maintenance window; the third category is faults of critical components that do not affect operation, which are also included in the most recent monthly planned maintenance window; and the fourth category is faults of non-critical components that do not affect operation, which can be postponed to subsequent minor, medium, and major repair windows for unified processing. Through this hierarchical maintenance response mechanism, emergency repairs and planned maintenance are incorporated into the same event-driven framework, solving the problem of the separation between planned windows and corrective responses in the prior art. At the same time, different processing paths will trigger different cost aggregation rules, ensuring the consistency of cost measurement with the actual maintenance scenario.

[0054] In this embodiment, based on the tiered maintenance response mechanism, a merged maintenance mechanism is defined when a planned maintenance window arrives. This mechanism uses the event-driven engine as the trigger and the tiered maintenance response mechanism as a prerequisite, achieving unified execution of preventative and restorative maintenance while reducing the additional costs associated with redundant maintenance. In this embodiment, when a preset planned maintenance window (including monthly, minor, medium, and major maintenance windows) arrives, the system triggers a planned window arrival event. Based on this event, the system automatically scans all pending faults, components nearing their lifespan thresholds, and mergeable work items, combining them according to a preset maintenance intensity to form a maintenance package within the window, and then performs merged maintenance. For example, when the monthly maintenance window arrives, the system merges all delayed non-emergency faults, replacement jobs for life-limited components nearing their lifespan threshold, and preventative inspection jobs that can be performed simultaneously within the month, and completes these jobs in order of priority. This approach effectively reduces the additional costs associated with repeated disassembly and assembly, repeated maintenance organization, and repeated deployment. At the same time, it enables unified modeling of preventative and restorative maintenance within the same event-driven framework, allowing direct analysis of the impact of parameters such as window frequency and maintenance intensity on total costs and downtime within the same model.

[0055] In this implementation, costs are not simply calculated per simulation iteration after the simulation ends, but are accumulated synchronously during the occurrence of events such as operation, maintenance, procurement, support tasks, and annual statistics. The model sets cost variables such as LRU purchase cost, LRU maintenance cost, preventative maintenance cost, operating energy cost, labor cost, spare parts cost, cost of used parts, maintenance vessel cost, and total cost. Annual costs, cumulative costs, and itemized costs are stored in data tables such as total_cost, total_cost_details, cost_summary, and cost_yearly_snapshot.

[0056] Furthermore, the specific rules for cost allocation include: replacement units are included in LRU purchase costs or spare parts costs when purchased or replaced; repairable units are included in LRU maintenance costs when repaired; planned operations occurring within the planned window, such as minor, medium, and major repairs, are included in preventive maintenance costs; replacement of life-limited parts is included in life-limited part purchase costs; support mission deployment is included in support mission costs based on mission duration or window duration, which in this embodiment is manifested as ship maintenance costs; annual human resources are included in labor costs annually; and operating energy consumption can be included in operating energy consumption costs based on operating time and energy consumption parameters.

[0057] Furthermore, annual statistical events are used to convert cumulative costs into annual increments. Specifically, the cumulative value of the previous year is saved, and during annual statistics, the current cumulative value is subtracted from the previous year's cumulative value to obtain the current year's LRU purchase cost, LRU maintenance cost, preventative maintenance cost, operating energy cost, labor cost, spare parts cost, near-life parts purchase cost, and ship maintenance cost, which are then written into the annual cost snapshot table. At the end of the simulation, all types of cumulative costs are then combined into the total cost.

[0058] This embodiment also includes an inventory and procurement linkage mechanism. Based on the inventory attributes in the equipment hierarchical structure model and using an event-driven engine as the trigger, this mechanism incorporates the entire spare parts procurement chain into the simulation's main loop, accurately depicting the impact of stockouts and downtime on costs and downtime. In this embodiment, when a maintenance event triggers a spare parts replacement requirement, the system first checks the inventory quantity of the corresponding replaceable unit or lifespan part. If the inventory is greater than zero, it performs inventory deduction and spare parts requisition, continuing the subsequent maintenance process. If the inventory is zero, the system immediately generates a purchase order event, recording the order time, lead time, and estimated arrival time. Simultaneously, it sets the status of the corresponding equipment object to "downtime waiting" or "degraded operation," which remains until a spare parts arrival event is triggered, at which point the subsequent maintenance process resumes. This inventory and procurement linkage mechanism fully incorporates the continuous chain process of inventory check, purchase order placement, waiting for delivery, and delivery resumption into the event-driven simulation main loop. It accurately describes the consequences of prolonged downtime caused by insufficient spare parts, and solves the problem of insufficient description of spare parts delivery delays and stockout downtime mechanisms in existing technologies. At the same time, the stockout waiting time will directly affect the guarantee task cost and downtime-related indicators, realizing precise linkage between inventory status, downtime, and cost changes.

[0059] In this implementation, the inventory and procurement linkage mechanism is jointly implemented by the Depository object and the replaceable unit maintenance process. When a replaceable unit cannot be repaired on-site or needs to be replaced, the model checks the corresponding spare parts inventory quantity (SPAREQTY). If the inventory is greater than zero, the inventory is deducted and the replacement or repair completion process begins; if the inventory is insufficient, a procurement demand is generated according to the purchase quantity (PurchaseQuantity), and the arrival waiting time is set according to the leadTime. Before the spare parts arrive, the relevant replaceable unit remains in a pending repair or waiting state, and whether the equipment to which it belongs is shut down is determined by whether the fault affects operation and the MinWorkQty constraint.

[0060] Furthermore, the inventory and cost mechanisms are linked synchronously. When spare parts are requisitioned, replaced, or procured, the model will classify the corresponding costs into categories such as LRU purchase cost, spare parts cost, or near-life parts acquisition cost; inventory holding or inventory occupation can be included in inventory-related costs on a periodic event basis. For repairable and replaceable units, LRU maintenance costs are recorded after repair; for non-repairable items with insufficient inventory, the subsequent replacement process will resume after procurement. Thus, stockouts not only change the repair completion time but also affect the final evaluation results through waiting time, availability, number of maintenance tasks, and procurement costs.

[0061] This implementation clarifies the calculation rules for support mission costs. These rules are based on an event-synchronized cost aggregation mechanism and triggered by a tiered maintenance response mechanism, thus resolving the problem of difficulty in accurately measuring support deployment costs in existing technologies. In this implementation, for each on-site support action triggered by an emergency response, periodic window, or maintenance window, the system automatically generates an independent support mission record. This record includes the triggering reason, start time, end time, number of days occupied, associated maintenance items, resources used, and billing parameters. The support mission cost is calculated as follows: Single support mission cost = Daily occupancy cost × Number of days occupied; the total support mission cost is the sum of the costs of all support missions throughout the entire lifecycle. Furthermore, support mission costs are separate from preventative maintenance costs and restorative maintenance costs, belonging to independent cost categories. Maintenance costs primarily reflect component repair, replacement, labor, and resource inputs, while support mission costs reflect the costs incurred in completing maintenance tasks, such as organization, dispatch, and remote support, including ship fares in maritime support scenarios and transportation and personnel deployment costs in remote site support. This task-level billing mechanism avoids the inaccuracy of amortizing support costs by the number of repairs in existing technologies. It truly reflects the impact of remote support, centralized support, or on-site organization on the total cost, and is especially suitable for special scenarios with long support distances and high costs per support.

[0062] This implementation clearly defines the full composition of operation and maintenance costs. This cost composition provides a clear collection dimension for the event-synchronized cost collection mechanism, achieving comprehensive coverage of all operation and maintenance costs throughout the equipment's entire lifecycle. In this implementation, the collected operation and maintenance costs include seven categories: preventative maintenance costs (costs incurred for preventative inspections, maintenance, and replacements performed within the planned window); repair maintenance costs (costs incurred for unplanned maintenance triggered by faults); support mission costs (organizational dispatch costs calculated according to the aforementioned rules and directly linked to support missions); replaceable unit procurement costs (costs incurred for the procurement and replenishment of replaceable units); replaceable unit repair costs (costs incurred for the return and repair of faulty replaceable units); lifespan component replacement costs (costs incurred for the procurement and replacement of lifespan components upon their expiration); and inventory holding costs (warehousing, management, and capital occupation costs incurred due to spare parts inventory). All seven categories of costs are synchronously registered and collected by the cost engine when the corresponding event occurs, and are simultaneously bound to the corresponding equipment object and triggering event, achieving complete coverage and traceability of costs throughout the entire lifecycle.

[0063] In this embodiment, fault and lifespan evolution rules are established. These rules provide the core state change logic for the state evolution engine and the input basis for the graded maintenance response mechanism, ensuring consistency between equipment state evolution and actual operating scenarios. For lifespan components, lifespan consumption logic is set up, measuring lifespan consumption only when the equipment is in operation; lifespan consumption is suspended during shutdown and maintenance, accurately reflecting the lifespan loss patterns during actual equipment operation. For replaceable units, fault triggering logic is set up, employing methods such as random triggering based on failure rate or degradation triggering based on remaining lifespan. After a fault is triggered, the response level is determined based on the degree of impact of the fault on operation and the criticality of the component, providing a basis for the graded maintenance response mechanism. Through these fault and lifespan evolution rules, accurate modeling of equipment state evolution is achieved, improving the accuracy of subsequent maintenance decisions, cost collection, and indicator output.

[0064] In this implementation, the fault triggering of a replaceable unit is determined based on parameters such as its MTBF, critical fault probability (PCF), repairability (REPABLE), diagnostic time (DiagT), repair time (RepT), repair cost (REPCOST), procurement cost (LRUCOST), and spare parts quantity (SPAREQTY). After a fault occurs, the model determines whether the replaceable unit is a critical fault, whether it affects operation, whether it is repairable, and whether replacement is necessary. Based on this, it enters the tiered maintenance response, maintenance waiting pool, spare parts inspection, or procurement waiting process.

[0065] Furthermore, the lifespan evolution of life-limited components is determined based on the LlpLife and quantity fields. Life-limited components consume their lifespan during equipment operation. When the remaining lifespan reaches the replacement threshold or expires, a replacement requirement is generated and included in the cost of purchasing life-limited components. The difference between life-limited components and ordinary replaceable units is that life-limited components are primarily replaced due to lifespan depletion and do not enter the same repair waiting queue as ordinary fault objects; replaceable units, on the other hand, are primarily replaced due to faults and enter the corresponding maintenance and disposal path based on whether they are repairable, whether inventory is sufficient, and priority.

[0066] Furthermore, regarding state propagation, the model compares the number of healthy replaceable units of the same type within the equipment with MinWorkQty. When the number of healthy units is insufficient, this type of failure is marked as impacting operation; when an operation-impacting failure occurs in critical equipment, the equipment state affects the availability of the system, modules, and sites. This rule ensures that cost assessment does not merely count the number of failures, but reflects the actual impact of failures on equipment operational capabilities.

[0067] This implementation clearly defines the output rules for operation and maintenance (O&M) evaluation indicators. Based on the equipment hierarchical structure model and event-synchronized cost aggregation results, these rules enable multi-dimensional decomposition of evaluation results, providing support for refined equipment O&M management. In this implementation, after simulation, the system outputs O&M evaluation indicators based on the cost aggregation results throughout the entire lifecycle, in three dimensions: First, based on the hierarchical objects of the equipment hierarchical structure model, it outputs indicators such as cost contribution, failure frequency, and downtime for each level (site, module, system, equipment, replaceable unit, and lifespan component), clearly identifying high-cost components and high-risk links throughout the entire lifecycle; second, based on the time dimension, it outputs annual, monthly, and weekly cost sequences, downtime sequences, and support task frequency sequences, clearly reflecting cost fluctuations and O&M pressure at different operational stages; third, based on cost triggering reasons, it outputs cost distributions corresponding to different failure modes, different maintenance types, and different support scenarios, clearly identifying the core driving factors of costs. Through multi-dimensional indicator output, the problem of existing technologies being unable to clearly explain cost sources is solved, providing accurate data support for equipment design improvement, maintenance strategy optimization, and inventory strategy adjustment.

[0068] In this embodiment, a multi-scheme comparison and screening mechanism is established. This mechanism uses the event-driven simulation framework as the execution carrier and the multi-dimensional operation and maintenance evaluation indicators as the decision-making basis, solving the problem that existing technologies lack a comprehensive indicator system for scheme screening and cannot directly support engineering decisions. In this embodiment, by adjusting the values ​​of key parameters such as maintenance window frequency, maintenance intensity, inventory parameters, and support rules, multiple sets of candidate operation and maintenance schemes are generated. For each set of schemes, a full life cycle simulation evaluation is performed, outputting the multi-dimensional operation and maintenance evaluation indicators, including total operation and maintenance cost, cost composition, annual cost sequence, average downtime, total downtime, number of support tasks, number of procurements, and availability level. Based on the above indicators, constraints can be set according to engineering requirements, such as setting minimum availability level and maximum allowable downtime. Among the candidate schemes that meet the constraints, the scheme with the lowest total cost is selected, achieving the scheme optimization goal of "minimizing cost while meeting the service bottom line". Through this multi-option comparison mechanism, the method of the present invention can directly support the entire process of "option comparison - constraint screening - recommendation decision" in engineering applications, avoiding the problem that the existing technology can only output a single cost indicator and cannot support multi-objective compromise decision-making.

[0069] In this embodiment, the complete implementation process is as follows: Step 1: Read input data and establish equipment hierarchy. The system reads input tables such as Equipment Hierarchy, LURepair, life-limited component, and Maintenance Activity, generates site, module, system, equipment, replaceable unit, and life-limited component objects, and establishes parent-child hierarchical relationships.

[0070] Step 2: Initialize global parameters and status. Set the maintenance window cycle, maintenance window duration, maintenance intensity, environmental intensity, initial spare parts quantity, procurement lead time, initial cost variables, and result table. Initialize the remaining time for minor, medium, and major repairs in the Main object, and clear the pending repair pool, indicator set, and database result table.

[0071] Step 3: Track equipment operation and lifespan consumption. The model advances the remaining time for minor, intermediate, and major repairs based on operating time, and tracks the lifespan consumption of life-limited components. If a component reaches the replacement condition, a replacement requirement is generated and processed in the corresponding maintenance window or replacement process.

[0072] Step 4: Trigger a replaceable unit failure and determine its impact. After a replaceable unit fails based on parameters such as MTBF, the model determines whether it is a critical failure, whether it is repairable, and whether it affects operation. It also checks whether the number of healthy replaceable units of the same type in the equipment is lower than MinWorkQty.

[0073] Step 5: Implement tiered maintenance response. If the fault pertains to critical equipment and affects operation, it will be handled with priority; other faults will be placed in the global repair pool and sorted according to priority category, waiting time, and object number.

[0074] Step Six: The maintenance window arrives and merged maintenance is performed. When a minor, medium, major, or periodic maintenance window arrives, the model calculates the window capacity based on the window duration and maintenance intensity, selects objects that meet the rules from the waiting pool, triggers "Start Repair," and completes the maintenance according to the repair time.

[0075] Step 7: Perform inventory check and procurement linkage. When a spare part needs to be replaced for repair, first check the inventory; if the inventory is sufficient, deduct from the inventory and continue the repair; if the inventory is insufficient, generate a procurement request and wait for the LeadTime to arrive. Once the part arrives, resume the replacement or repair process.

[0076] Step 8: Synchronously collect costs and output metrics. The model synchronously accumulates costs during procurement, repair, preventative maintenance, life-cycle component replacement, support mission deployment, and annual statistics, and writes them into the results table by year, cost type, and object level. Output metrics include average LRU availability, final number of items pending repair, average waiting time for repair, total cumulative cost, and breakdown of each cost item.

[0077] In this embodiment, the basic input data structure is as follows:

[0078] Specifically, maintenance window frequencies can be 15, 30, 45, and 60 days; maintenance intensity can be 3, 6, 9, 12, and 15 items / day; and environmental intensity can be 0, 0.25, 0.5, 0.75, and 1.0. Each set of parameters can be repeatedly simulated, and the mean and standard deviation of the results are calculated. The above parameters are used to verify the changes in cost and availability under different maintenance window frequencies, maintenance resource capabilities, and external operational pressures, and do not constitute a limitation on this technical solution.

[0079] In this embodiment, through the above implementation process, the technical solution can achieve the following effects: First, it integrates equipment operation, failure, maintenance, inventory, procurement, support tasks, and cost collection within the same timeline, avoiding a disconnect between cost statistics and status evolution; Second, through the hierarchical relationship of sites, modules, systems, equipment, replaceable units, and lifespan parts, costs can be traced back to specific levels and triggering objects; Third, through a maintenance waiting pool and tiered priority rules, emergency maintenance, window maintenance, and delayed maintenance are unified into the same maintenance response mechanism; Fourth, through the linkage between inventory and procurement, waiting, downtime, or degraded operation caused by insufficient spare parts can affect availability, number of pending repairs, waiting time, and costs; Fifth, through annual cost snapshots and indicator summaries, solution comparisons can simultaneously consider costs, availability levels, and maintenance pressure.

[0080] In some examples, simulations of different combinations of maintenance window frequency, maintenance intensity, and environmental intensity yielded the following quantitative results.

[0081]

[0082] As shown in the table above, when the maintenance window period was extended from 15 days to 60 days, the average LRU availability decreased from approximately 0.644 to approximately 0.518, the final number of pending repairs increased from approximately 471.3 to approximately 538.3, and the cumulative total cost decreased from approximately 95,294.2 to approximately 72,452.8. These results indicate that a shorter maintenance window period can improve availability and reduce the backlog of pending repairs, but it increases support mission costs; a longer maintenance window period can reduce some support costs, but it leads to a higher backlog of pending repairs and a decrease in availability.

[0083]

[0084] As shown in the table above, when the maintenance intensity increased from 3 items / day to 15 items / day, the average LRU availability increased from approximately 0.448 to approximately 0.640, and the final number of items awaiting repair decreased from approximately 634.9 to approximately 467.5, indicating that enhanced maintenance capacity can effectively absorb the backlog of items in the repair pool. However, when the maintenance intensity increases to a higher level, the increase in availability becomes smaller, indicating that under the combined constraints of inventory, window frequency, and failure demand, simply increasing the maintenance intensity exhibits diminishing marginal returns.

[0085]

[0086] As shown in the table above, when the environmental intensity increases from 0 to 1.0, the average LRU availability decreases from approximately 0.640 to approximately 0.522, the final number of units awaiting repair increases from approximately 465.0 to approximately 543.4, the average waiting time for repair increases from approximately 68,550.3 hours to approximately 77,215.5 hours, and the cumulative total cost increases from approximately 71,107.6 to approximately 91,685.2. These results indicate that increased external operating pressure simultaneously increases fault exposure and lifespan depletion, leading to adverse changes in maintenance waiting time, availability, and cost.

[0087] This invention also provides an event-driven equipment lifecycle maintenance cost assessment system to implement the assessment method described above, such as... Figure 1 The system architecture diagram shown illustrates that the system comprises an object modeling module, an event scheduling module, and a cost engine module. These three core modules work together to fully implement the evaluation method of this invention. The object modeling module is the foundational module, used to read equipment hierarchy, fault parameters, lifespan parameters, maintenance parameters, and cost parameters to establish the equipment hierarchy structure model, providing a unified object carrier for the entire system. The event scheduling module is the core driving module, used to maintain a future event table, trigger discrete events in chronological order, construct a discrete event-driven state evolution engine, promote the state update of equipment objects at each level, and simultaneously trigger corresponding operations in other modules. The cost engine module is the core value module, used to simultaneously trigger the real-time collection of corresponding maintenance costs while the event scheduling module processes discrete events. Each collected cost is bound to the corresponding level of object and the discrete event that triggered the cost, achieving full-process traceability of costs.

[0088] Furthermore, the system described in this embodiment may also include a status update module, a maintenance decision module, an inventory procurement module, a support task module, and an indicator statistics module, wherein: the status update module is used to change the operating status, fault status, and maintenance status of the equipment object; the maintenance decision module is used to execute graded maintenance response and merged maintenance decisions; the inventory procurement module is used to execute inventory checks, purchase order generation, and arrival recovery processing; the support task module is used to generate support task records and calculate billing; and the indicator statistics module is used to execute multi-dimensional indicator output and multi-scheme comparison. All of the above modules are executed based on event triggering by the event scheduling module, and work in coordination with the three core modules to fully realize all the functions of the method of this invention.

[0089] Example 1: Lifecycle Maintenance Cost Assessment of General Equipment This embodiment applies the evaluation method of the present invention to fixed site equipment in a general scenario. The specific steps are as follows: Step 1: Prepare basic data. Establish input data to describe equipment hierarchical relationships, replaceable unit maintenance attributes, maintenance activity rules, life-part constraints, and cost parameters, enabling the system to simultaneously characterize equipment object structure, fault repair process, inventory support process, and cost aggregation relationships.

[0090] Step two: Initialize the simulation environment. The system reads input data through the object modeling module, constructs hierarchical objects of sites, modules, systems, equipment, replaceable units, and life-cycle parts, and assigns them initial operating status, maintenance status, inventory status, and cost record containers; at the same time, the event scheduling module generates initial events such as start of operation, arrival of planned window, and triggering of maintenance activities, and writes them into the future event table.

[0091] Step 3: Time Progression and Event Handling. The event scheduling module uses an event-driven approach to advance the simulation time. It processes events such as fault triggering, lifespan limit expiration, window expiration, maintenance completion, and spare parts arrival sequentially through an event queue. Simultaneously, the status update module updates the status of the corresponding equipment objects, ensuring that faults, lifespan, maintenance, inventory, and costs all unfold along a unified timeline. Figure 2 The state evolution process is shown.

[0092] Step 4, Fault Response Determination. When a device or replaceable unit fails, the maintenance decision module determines its handling path based on the degree of its impact on operation, the criticality of the object, and the current support conditions. For faults that need to be restored immediately, an emergency repair or emergency support task is generated; for faults that can be delayed, they are recorded as pending items and uniformly arranged in the subsequent planning window.

[0093] Step 5: Window merging. When a monthly, minor, medium, or major repair window arrives, the maintenance decision module filters the current backlog of faults, components nearing their lifespan threshold, and mergeable work items, combining them into a single maintenance package for execution. This reduces the additional costs associated with repeated disassembly and assembly, repeated downtime, and repeated support organization.

[0094] Step Six: Inventory and Replacement Linkage Processing. When a maintenance task involves replacing replaceable units or lifespan parts, the inventory procurement module first checks if the inventory is sufficient. If the inventory is sufficient, it directly executes the requisition and replacement; if the inventory is insufficient, it generates a purchase order and keeps the relevant objects in a downtime or downgraded state during the waiting period until the spare parts are replenished before resuming operation.

[0095] Step 7: Real-time cost collection. During the processing of each of the above events, the cost engine module synchronously registers the corresponding costs, including preventive maintenance costs, restorative maintenance costs, replaceable unit procurement costs, replaceable unit repair costs, life-cycle component replacement costs, and support mission costs. Costs correspond one by one with the fault, waiting, repair, replacement, and recovery processes.

[0096] Step 8: Indicator Statistics and Result Output. After the simulation, the indicator statistics module outputs results according to object level, time dimension, and cost reasons, generating indicators such as total cost, total downtime, average downtime, number of support tasks, number of procurements, number of maintenance tasks, and availability level. By changing parameters such as window frequency, maintenance intensity, and inventory capacity, multiple schemes are compared to provide a basis for selecting operation and maintenance schemes.

[0097] Example 2: Life-cycle maintenance cost assessment of long-term offshore equipment This embodiment is designed for a scenario involving long-term offshore equipment operation, which is characterized by long support distances, limited on-site conditions, high costs for each support mission, and long spare parts replenishment cycles. This scenario can fully demonstrate the synergistic effect of the various technical features of this invention.

[0098] First, following the hierarchical relationship of sites, modules, systems, equipment, replaceable units, and life-cycle components, an equipment hierarchical structure model is established through the object modeling module. Using the same event-driven framework as in Example 1, the method of this invention does not rely on the limiting condition of "at sea". The sea is only used as a specific case scenario in terms of parameter settings and cost rules, which fully demonstrates the versatility of the method of this invention.

[0099] Secondly, in this embodiment, whenever a fault requires on-site vessel dispatch for support, or when the planning window triggers an on-site maritime maintenance task, the support task module automatically generates a support task record. This record includes the number of days the vessel is dispatched and the corresponding cost parameters. The vessel cost is calculated in the form of "daily cost × number of days the vessel is dispatched" and accumulates linearly with the number of days the vessel is dispatched. The sum of the vessel costs of multiple support tasks constitutes the total support task cost. This separates the vessel cost from the traditional average amortization method and treats it as an independent cost item directly bound to the support task. It is synchronously collected through the cost engine module, which truly reflects the cost characteristics of "few tasks but high cost per task" or "frequent tasks leading to rapid accumulation of vessel costs" in maritime support.

[0100] When a marine equipment malfunctions, the maintenance decision module first determines whether a ship must be dispatched for repair. For malfunctions that affect operations and cannot be delayed, a marine support mission is immediately triggered. The system compares not only the cost of the repair itself, but also the differences in ship costs caused by the number of dispatches and the number of dispatch days under different strategies. For malfunctions that can be delayed, they are merged into subsequent dispatch windows for processing, reducing the number of dispatches and lowering the total support cost.

[0101] Regarding inventory and replenishment, if the on-site inventory at sea is insufficient, the inventory procurement module will keep the relevant equipment in a standby state and include the waiting time in the total downtime statistics. Once the spare parts are replenished, it will be connected with the dispatch mission or maintenance window to complete the recovery. Since the replenishment cycle at sea is usually long, the waiting time due to missing parts will often further amplify the impact of the dispatch strategy on the total cost. This invention uses a unified event-driven framework to simultaneously reflect the linkage between ship fees, maintenance costs, procurement costs and downtime within the same model.

[0102] When comparing different plans, three groups of plans were set up: a denser dispatch window, a medium dispatch window, and a sparser dispatch window. Different maintenance merging intensities were configured for each group. After simulation, the total cost, vessel fees, maintenance costs, downtime, and number of support tasks for each plan were output. If a plan has lower maintenance costs but excessively high total vessel fees due to frequent dispatches, its overall economic efficiency is considered poor. Similarly, if a plan reduces the number of dispatches but causes excessive downtime due to long waiting times, its limitations can also be identified. Finally, acceptable availability level constraints were first set, and then the plan with the lowest total cost was selected from the candidate plans that met the constraints, completing the optimization and screening of the operation and maintenance plan.

[0103] The embodiments described above do not constitute a limitation on the scope of protection of this technical solution. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the above embodiments should be included within the scope of protection of this technical solution.

Claims

1. An event-driven method for evaluating the life-cycle maintenance costs of equipment, characterized in that, Includes the following steps: Establish an equipment hierarchical structure model, which represents the hierarchical relationship between stations, modules, systems, equipment, replaceable units and life-use parts, as well as the operating status, fault attributes, maintenance attributes, inventory attributes and cost parameters of each level object, and establish a mapping relationship for the transmission of the status changes of lower level objects to upper level objects; A discrete event-driven state evolution engine is constructed, a set of discrete events is defined, and discrete events are processed sequentially in chronological order based on a future event table to drive the state update of each level object in the equipment hierarchical structure model. During the processing of the discrete events, the corresponding operation and maintenance costs are synchronously collected in real time, and each collected cost is bound to the corresponding level of object and the discrete event that triggered the cost.

2. The method according to claim 1, characterized in that, It also includes establishing a tiered maintenance response mechanism, which determines the handling path of a fault based on the degree of impact of the fault on equipment operation and the criticality of the fault object, such as immediately triggering emergency maintenance, incorporating it into a preset planned maintenance window, or postponing it to a subsequent maintenance window.

3. The method according to claim 2, characterized in that, When the preset planned maintenance window arrives, the system scans the current pending faults, components nearing their lifespan thresholds, and mergeable work items, combines them according to preset rules to form a maintenance package within the window, and performs merged maintenance.

4. The method according to claim 1, characterized in that, It also includes establishing an inventory and procurement linkage mechanism, performing spare parts inventory checks based on maintenance needs, issuing spare parts when inventory is sufficient, generating purchase orders and recording delivery lead times when inventory is insufficient, and simultaneously maintaining the shutdown or downgraded operation status of the corresponding equipment until the spare parts arrive and the maintenance process is resumed.

5. The method according to claim 1, characterized in that, The collection of maintenance costs includes calculating the maintenance task cost based on the resource occupation duration of the maintenance task for a single maintenance activity-triggered support task. The support task cost belongs to an independent cost category from preventive maintenance costs and restorative maintenance costs.

6. The method according to claim 1, characterized in that, The collected maintenance costs include preventive maintenance costs, restorative maintenance costs, support mission costs, replacement unit procurement costs, replacement unit repair costs, life-cycle component replacement costs, and inventory holding costs.

7. The method according to claim 1, characterized in that, It also includes establishing fault and lifespan evolution rules. For the lifespan components, lifespan consumption is measured only when the equipment is in operation, and lifespan consumption is suspended during shutdown and maintenance. For the replaceable units, fault triggering logic is set, and response levels are divided according to fault attributes.

8. The method according to claim 1, characterized in that, Based on the cost aggregation results, corresponding operation and maintenance evaluation indicators are output according to the hierarchical objects, time dimensions, and cost triggering reasons of the equipment hierarchical structure model.

9. The method according to claim 8, characterized in that, By adjusting the values ​​of maintenance window frequency, maintenance intensity, and inventory parameters, multiple maintenance plans are generated. After simulation evaluation, the multiple plans are compared and selected based on the output maintenance evaluation indicators.

10. An event-driven equipment lifecycle maintenance cost assessment system, characterized in that, It includes an object modeling module, an event scheduling module, and a cost engine module; The object modeling module is used to establish an equipment hierarchical structure model. The equipment hierarchical structure model represents the hierarchical relationship between stations, modules, systems, equipment, replaceable units and life-use parts, as well as the operating status, fault attributes, maintenance attributes, inventory attributes and cost parameters of each level object, and establishes a mapping relationship for the transmission of the status changes of lower-level objects to upper-level objects. The event scheduling module is used to construct a discrete event-driven state evolution engine, define a discrete event set, process discrete events sequentially in chronological order based on a future event table, and drive the state update of objects at each level in the equipment hierarchical structure model. The cost engine module is used to synchronously trigger the real-time aggregation of corresponding operation and maintenance costs during the processing of discrete events, and to bind each aggregated cost to the corresponding level of object and the discrete event that triggered the cost.

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