Patent Publication Number: US-2007118502-A1

Title: Methods and systems for managing a fleet of assets

Description:
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT  
      The U.S. Government may have certain rights in this invention pursuant to contract number F33615-03-2-6300. 
    
    
     BACKGROUND OF THE INVENTION  
      This application relates generally to managing assets and, more particularly, to methods and systems for optimizing lifetime costs of a fleet of assets.  
      The total cost of ownership of a fleet includes purchase, operating, and service costs. Given that purchase price is fixed, daily decisions can be made which affect the operating and service costs of assets. One such decision is the assignment of missions (routes) to each asset to alter the fuel consumption profile, the repair capacity required, and asset maintenance costs.  
      On-condition maintenance of equipment results in process variability that requires excess capability to insure availability. Whether such excess capability is in the form of assets or repair capacity, additional maintenance costs are incurred. One solution is to simply remove engines early, sacrificing life to reduce variability. However, modifying the mission (route) to facilitate reducing variability does not sacrifice the operational life left in the asset.  
      Assets, such as turbine engines, are subject to failure by two types of causes. Wear, which is highly correlated to operating hours (engine flight hours), and thermal shock, which is highly correlated to start events (engine cycles). A determination to assess the mode more probable to cause failure of the asset can be made and the mission (hours per cycle or flight leg for an aircraft engine) can be selected to balance the failure modes, increasing the utilization of the life built into the engine. The ability to make this selection is facilitated when there are multiple assets that can be selected for different missions to optimize operational life for the fleet.  
      Individual assets, such as turbine engines can differ in their fuel efficiency due to configuration, age, and environment. Aircraft routes (missions) differ in fuel requirements based on for example, but not limited to, load (passengers and cargo), altitude, temperature, and humidity. Fuel consumption varies for each engine on each route. An optimal assignment of missions for each aircraft may be made to minimize total daily fuel cost for the fleet.  
      However, because of labor and cost constraints such matching techniques may be impracticable in some instances.  
     BRIEF DESCRIPTION OF THE INVENTION  
      In one embodiment, a mission planning system for a fleet of assets includes a centralized database for storing information, a computer system configured to be coupled to the database wherein the computer system includes a mission model configured to analyze a plurality of missions to be executed by the fleet of assets, the missions being defined by at least one task sequence and a set of environmental parameters associated with each task sequence, an asset model configured to determine the capability of each asset to perform the task sequence associated with at least one mission using the historical data, the threshold data, and the mission analysis, and a selection model configured to match at least one asset to each mission based on the determined asset capability, such that the capability of all assets is facilitated being optimized.  
      In another embodiment, a method of managing life cycle costs associated with maintaining and operating a fleet of assets includes storing historical data relating to missions executed by each asset in a database wherein the historical data includes asset performance data and environmental data for each mission. The method also includes storing threshold data for each asset in the database wherein the threshold data relates to endurance limits for each asset. A plurality of missions to be executed is analyzed wherein the missions are defined by at least one task sequence and a set of environmental parameters associated with each task sequence. The method includes determining the capability of each asset to perform the task sequence associated with at least one mission using the historical data, the threshold data, and the mission analysis, and matching at least one asset to each mission based on the determined asset capability, such that the capability of all assets is facilitated being optimized, the matching is performed using a computer.  
      In yet another embodiment, a computer program for managing life cycle costs associated with maintaining and operating a fleet of assets is provided. The program is embodied on a computer readable medium and includes at least one code segment that stores historical data relating to missions executed by each asset in a database wherein the historical data includes asset performance data and environmental data for each mission, stores threshold data for each asset in the database wherein the threshold data relates to endurance limits for each asset, analyzes a plurality of missions to be executed wherein the missions are defined by at least one task sequence and a set of environmental parameters associated with each task sequence. The at least one code segment also determines the capability of each asset to perform the task sequence associated with at least one mission using the historical data, the threshold data, and the mission analysis, and matches at least one asset to each mission based on the determined asset capability, such that the capability of all assets is facilitated being optimized. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       FIG. 1  is a schematic diagram of a general-purpose computer system in which a system for fleet mission planning operates;  
       FIG. 2  is a schematic diagram of an exemplary mission chart that may be used in a fleet mission planning system;  
       FIG. 3  is a schematic data flow diagram of an exemplary fleet asset management system that may be used with the fleet mission planning computer system shown in  FIG. 1 ;  
       FIG. 4  is a flow chart of an exemplary method of managing life cycle costs associated with maintaining and operating a fleet of assets; and  
       FIG. 5  is a data flow diagram of an exemplary embodiment of the fleet mission planning system shown in  FIG. 2 . 
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
       FIG. 1  is a schematic diagram of a general-purpose computer system  10  in which a system for fleet mission planning operates. The computer system  10  generally comprises a processor  12 , a memory  14 , input/output devices, and data pathways (e.g., buses)  16  connecting the processor, memory and input/output devices. The processor  12  accepts instructions and data from the memory  14  and performs various calculations. The processor  12  includes an arithmetic logic unit (ALU) that performs arithmetic and logical operations and a control unit that extracts instructions from memory  14  and decodes and executes them, calling on the ALU when necessary. Memory  14  generally includes a random-access memory (RAM) and a read-only memory (ROM), however, there may be other types of memory such as programmable read-only memory (PROM), erasable programmable read-only memory (EPROM) and electrically erasable programmable read-only memory (EEPROM). Also, the memory  14  preferably contains an operating system, which executes on the processor  12 . The operating system performs basic tasks that include recognizing input, sending output to output devices, keeping track of files and directories and controlling various peripheral devices.  
      The input/output devices comprise a keyboard  18  and a mouse  20  that enter data and instructions into the computer system  10 . A display  22  allows a user to see what the computer has accomplished. Other output devices could include a printer, plotter, synthesizer and speakers. A modem or network card  24  enables the computer system  10  to access other computers and resources on a network. A mass storage device  26  allows the computer system  10  to permanently retain large amounts of data. Mass storage device  26  may include all types of disk drives such as floppy disks, hard disks and optical disks, as well as tape drives that can read and write data onto a tape that could include digital audio tapes (DAT), digital linear tapes (DLT), or other magnetically coded media. The above-described computer system  10  can take the form of a hand-held digital computer, personal digital assistant computer, personal computer, workstation, mini-computer, mainframe computer, and supercomputer. In the exemplary embodiment, a database  28  for storing information is embodied on mass storage device  26 . At certain times during operation of system  10 , database  28  is embodied in memory  14 .  
       FIG. 2  is a schematic diagram of an exemplary mission chart  50  that may be used in a fleet mission planning system. In the exemplary embodiment, mission chart  50  includes a hub or origination point  52  of a plurality of missions. In an alternative embodiment, mission chart  50  includes a plurality of origination points  52 . Each origination point  52  includes a plurality of associated destination points, such as destination points A-F. In various alternative embodiments, origination point  52  may include any number of associated destination points. Each destination point includes an associated duration associated with a traversal from origination point  52  to a respective destination point. As illustrated in  FIG. 2  the durations may be expressed in units of hours, such as operating hours of an asset traversing a route from origination point  52  to a respective destination point. The durations may also be expressed in other units, such as distance. Each traversal between origination point  52  and a destination point is a mission. Each mission includes one or more tasks. In an embodiment wherein the asset comprises an aircraft, the mission may include a takeoff task  54 , a climbout task  56 , a cruise task  58 , a descent task  60 , an approach task  62 , and a landing task  64 . Each task is performed in an environment that is unique to that task and the environment may affect the performance of the asset performing that task, and each asset may also perform differently from other assets performing the same task. Accordingly, matching certain assets to missions may yield a different level of performance and cost for the fleet of assets than the assets matched to different missions.  
       FIG. 3  is a schematic data flow diagram of an exemplary fleet mission planning system  200  that may be used with the general purpose computer system  10  (shown in  FIG. 1 ). Fleet mission planning system  200  includes database  28  that is configured to receive and store historical data  202  from the fleet of assets. Database  28  is also configured to receive and store threshold data  204  wherein at least some of threshold data  204  relates to historical data  202 . Threshold data  204  may be, but not limited to, operational limits, performance parameters that trigger maintenance actions, or inspection tolerances that are used to determine wear and remaining life of an asset. Threshold data  204  may be received from original equipment manufacturer&#39;s (OEM) literature, including for example, notices, bulletins, technical and operational manuals, regulatory notices, or may be developed in-house or by third party engineers based on preferred operational strategies.  
      In the exemplary embodiment, database  28  is comprised in a single database located in a single location. In an alternative embodiment, database  28  is comprised of various databases located on several computers or servers, and may be located remotely with respect to each other and may be communicatively coupled through a network.  
      A mission model  206  is configured to receive historical data  202  and threshold data  204  from database  28  to determine the set of missions to be executed and the environment and requirements of each mission. In an alternative embodiment, mission model  206  receives mission data  208  from a database  210  that is maintained separately from database  28 . Database  210  may be developed, owned, and maintained by a different business entity than database  28 . Mission model  206  may receive mission data  208  only from database  210  and may not receive any data from database  28 . In the exemplary embodiment, mission model  206  includes a route model  212  configured to determine task sequences to be executed wherein the task sequences include a travel leg length, an asset loading wherein the asset loading includes at least one of passengers, cargo, and fuel, and profiles of an altitude, an ambient temperature, a wind speed, a current speed, an ambient humidity along the travel leg. In an embodiment of the invention the profiles include at least one of an average profile value for the travel leg, a transfer function relating travel conditions to profile values, and a transfer function relating travel conditions to profile values that includes a profile value uncertainty distribution.  
      An asset model  214  describes the capabilities of each asset with respect to the environment specified by mission model  206 . In the exemplary embodiment, asset model  214  is configured to determine the capability of each asset to perform the task sequence associated with at least one mission using the historical data, the threshold data, and the mission analysis. In various embodiments, asset model  214  includes a fuel consumption model  216  configured to determine a current and projected fuel consumption profile for each asset. In various other embodiments, asset model  214  includes a life estimation model  218  configured to estimate the life of the asset. Life estimation model  218  determines life remaining in the asset given the historical data received corresponding to the asset. Life estimation model  218  is also used to determine initial lifespan of new assets that have no historical data recorded.  
      A route selector  220  is configured to match at least one asset to a mission such that at least one of the total fleet fuel consumption is facilitated being minimized, an average time to failure is facilitated being maximized, and an average time to required maintenance is facilitated being maximized. Route selector  220  includes at least one of an exhaustive algorithm, a non-exhaustive search method, and an evolutionary algorithm wherein the exhaustive algorithm selects each combination of asset and mission possible to evaluate effects of each pairing. The evolutionary algorithm learns from past evaluations to anticipate which of the historical data affects the outcome of the pairings to the greatest extent. Other evolutionary algorithms use other methods to use past evaluations to improve the determination of pairings of assets and missions that facilitate optimizing the total costs to the fleet of assets.  
      A forecast module  222  estimates the effects of a mission-asset matching. A set of mission-asset matches is used for trade-off or what-if analysis. The analyzed mission-asset match  224  that facilitates optimizing the costs of the fleet of assets can then be selected from the set of mission-asset matches.  
       FIG. 4  is a flow chart of an exemplary method  300  of managing life cycle costs associated with maintaining and operating a fleet of assets. Method  300  includes storing  302  historical data relating to missions executed by each asset in a database, such as database  28  embodied on mass storage device  26 . At certain times database  28  may be embodied in memory  14 . The historical data generally includes asset performance data and environmental data for each mission. The asset may be any vehicle especially a vehicle used in a fleet of vehicles that are substantially interchangeable with each other to perform a series of tasks that make up a mission. For example, the assets are a fleet of delivery trucks, taxi cabs, ships, aircraft, or trains. The performance data relates to parameters that can be used to determine a maintenance or operational state of the asset, or to parameters that can be used to predict the performance of an asset in certain environmental conditions, such as an altitude, an ambient temperature, a wind speed and direction, an ambient humidity level, and a load wherein the load includes at least one of fuel, passengers, and cargo. Asset fuel efficiency is also used to track the performance of an asset and fuel use parameters relative to each task of a task sequence are also stored in the historical data. Data associated with a predetermined failure mechanism of the asset and asset operating hours and asset start events are also stored in database  28 .  
      The method also includes storing  304  threshold data for each asset in the database wherein the threshold data relates to endurance limits for each asset. For example, manufacturers or regulatory bodies have requirements that dictate maintenance or inspections actions are to be performed within predetermined hours of operation of the asset, or other milestones. Requirements may also dictate maintenance or inspection actions when a measurable parameter associated with the asset attains a predetermined level, for example, a pressure exceeded a pressure limit, or a vibration level exceeding a predetermined threshold. The parameters may also be trends of measured or inferred values.  
      A plurality of missions to be executed is analyzed  306  wherein the missions are defined by at least one task sequence and a set of environmental parameters associated with each task sequence. For example, a mission may be an airline flight from an origin A to a destination B. Tasks that are performed during the airline flight typically are defined as takeoff, cruise, and landing. Each task may have different environmental considerations and requirements. An elevation, ambient temperature, air salinity, and ambient humidity at an airport at origin A may limit the aircraft, aircraft loading, or aircraft performance capability during takeoff. Weather, air currents aloft, a temperature may affect aircraft performance during cruise. Environmental and other missions related parameters may affect the aircraft ability to land at destination B.  
      The capability of each asset to perform the task sequence associated with at least one mission is determined  308  using the historical data, the threshold data, and the mission analysis. For example, individual gas turbine engines can differ in their fuel efficiency due to configuration, age, and environment. Aircraft routes or missions differ in fuel requirements based on load (passengers, cargo, and fuel carried), altitude, temperature, and humidity. A projected fuel consumption profile for each asset is determined for use in determined an asset capability to perform during a mission. Fuel consumption varies for each engine on each mission. An optimal assignment of missions for each aircraft to minimize total daily fuel cost for the fleet is determined by matching  310  at least one asset to each mission based on the determined asset capability, such that the capability of all assets is facilitated being optimized and/or the total fuel consumption of the fleet of assets is facilitated being minimized. Additionally, the historical asset operating hours data and asset start events data are compared to associated asset operating hours threshold data and asset start event data to determine is the asset is nearing a mandated maintenance or inspection action. For example, an asset with a relatively large differential between historical operating hours and threshold operating hours is generally matched  310  to a mission that includes a relatively large operating hour requirement and an asset with a relatively large differential between historical asset start events and threshold asset start events is generally matched  310  to a mission that includes a relatively large asset start events requirement. Such matching  310  may be overridden by parameters and thresholds that have greater importance for safety reasons or may be overridden due to impacts to the whole fleet that make the entire fleet more optimally matched.  
       FIG. 5  is a data flow diagram  400  of an exemplary embodiment of the fleet mission planning system  200  (shown in  FIG. 2 ). In the exemplary embodiment, fleet mission planning system  200  includes a multi-objective optimizer  400  that receives metrics determined from a plurality performance, maintenance, operations, and business constraints. Multi-objective optimizer  400  determines a matching of assets to missions that facilitates optimizing the costs of operating and maintaining the fleet of assets being managed. For example, a particular capability of an asset may not be optimized for a particular asset by multi-objective optimizer  400  because multi-objective optimizer  400  tends to optimize the capabilities of the whole fleet of assets. Accordingly, tradeoffs may be made to select a matching of assets to missions that is not the optimal match for a particular asset but, facilitates optimally matching of the fleet assets to the missions to be executed.  
      Multi-objective optimizer  400  receives a fuel cost metric  402  from a fleet fuel consumption module  404 . The fuel cost metric is determined using an asset fuel consumption profile  406  for each asset from data received from performance monitoring equipment located on-board each asset in a fuel utilization baselining process  408 . Such data may be determined manually, if the asset lacks the performance monitoring equipment. A route fuel requirement profile  410  is also used to determine fuel cost metric  402  using route specific planning data predetermined by the asset owner using business considerations and regulatory constraints. For example, a takeoff portion of a mission may be required by local restrictions to include a steep climbout for noise mitigation in the vicinity of the airport. A steeper climbout translates into a greater fuel consumption requirement during takeoff. Additionally, a short runway also dictates a fuel consumption requirement during takeoff. Route fuel requirement profile  410  also uses real-time weather and atmospheric data to determine conditions that affect the fuel requirement profile for the route to be traversed. Accordingly, fuel cost metric  402  incorporates data relating to the fuel utilization performance of each asset, and the fuel utilization implications of the routes to be traversed and the weather and/or environmental conditions of the route in real-time. Fuel cost metric  416  provides an input to multi-objective optimizer  400  that tends to cause the asset to be matched with missions that can best utilize the assets fuel efficiency capabilities to yield the lowest total fleet fuel consumption.  
      Multi-objective optimizer  400  also receives a shop load metric  412  from a fleet repair schedule estimation  414 . A goal of multi-objective optimizer  400  is to facilitate reducing variation of the workload of repair and maintenance shops that perform maintenance and repair work on the assets of the fleet. Matching assets to missions optimally considers evening the work load of the shops to facilitate reducing servicing costs associated with the fleet of assets. For example, having service triggers for a plurality of assets expiring at substantially the same time would introduce inefficiencies into the servicing of the assets due to labor overtime costs, storage and handling of the assets, and the unavailability of the assets for assignment to missions. A removal from service date is predicted using asset fuel consumption profile  406 , trends of deteriorating performance determined by monitoring and diagnostic systems associated with the asset, current remaining life estimations using assessments of the life remaining of asset component parts and the trends of deteriorating performance, and inspections of the asset. A removal from service date is predicted and fleet repair schedule estimate  414  determines how much shop time the maintenance and/or repair required will need for completion. Shop load metric  412  is used to determines dates and shop resources that will be required to perform the required maintenance/repair.  
      A repair cost metric  416  provides another input to multi-objective optimizer  400  that is indicative of the life remaining in an asset based on asset fuel consumption profile  406 , trends of deteriorating performance determined by monitoring and diagnostic systems associated with the asset, current remaining life estimations using assessments of the life remaining of asset component parts and the trends of deteriorating performance, and inspections of the asset. Repair cost metric  416  provides an input to multi-objective optimizer  400  that tends to cause the life of all components of the asset to be used before the asset is removed from service. For example, an asset may have a first component that has a failure rate influenced by operating hours of the asset and a second component that has a failure rate influenced by the number of start and stop cycles the asset experiences. If, during operation, operating hours of the asset were nearing a value wherein the first component would be expected to fail and the number of starts experienced by the asset were relatively few compared to the value wherein the second component would be expected to fail, repair cost metric  416  would tend to influence multi-objective optimizer  400  to match the asset with a mission that required relatively few operating hours with respect to the number of starts required. In the case wherein the asset was an aircraft engine, repair cost metric  416  would tend to influence multi-objective optimizer  400  to match the aircraft to a mission including short haul routes that would tend to even the asset start life with the asset operating hours life such that when removal from service was dictated, both the asset start life and the asset operating hours life would be substantially expended.  
      It will be appreciated that a technical effect of the configurations of the present invention described herein is the automatic optimization of the management of a fleet of assets.  
      The above-described embodiments of a fleet management system provide a cost-effective and reliable means for matching assets to missions such that the capabilities of the assets are matched to each mission to facilitate optimizing the performance of the entire fleet of assets. More specifically, the missions are analyzed to determine the environmental and performance demands of the missions and the assets are analyzed to determine the capability of the assets. The assets and missions are then matched to generate asset/mission pairs that reduce fleet operating, maintenance, and service costs. As a result, the methods and systems described herein facilitate operating a fleet of assets in a cost-effective and reliable manner  
      Exemplary embodiments of fleet asset management systems are described above in detail. The systems are not limited to the specific embodiments described herein, but rather, components of each system may be utilized independently and separately from other components described herein. Each system component can also be used in combination with other system components.  
      While the invention has been described in terms of various specific embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the claims.