# US 2022/0276128 A1 · Method for online service policy tracking using optimal asset controller > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0276128 A1 - Document ID: US20220276128A1 - Status: Published application - Kind: A1 - Application number: 17/745,749 - Filed: 2022-05-16 - Published: 2022-09-01 - Earliest claimed: 2017-09-19 - Assignees: Raytheon Technologies Corporation - Applicants: Raytheon Technologies Corporation - Inventors: Thomas A. Frewen; Ozgur Erdinc; Hala Mostafa; Mei Chen; Draguna Vrabie - Web page: https://patentgrep.com/patent/US20220276128A1 ## CPC - G01M 15/14: Testing gas-turbine engines or jet-propulsion engines - G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling - G06Q 10/0875: Itemisation or classification of parts, supplies or services, e.g. bill of materials - G06Q 10/20: Administration of product repair or maintenance - G07C 5/006: {Indicating maintenance} - G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)} - G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE ## Abstract A method and system of providing customizable service for an asset. The method including generating a predictive model for each asset of a fleet, each predictive model based on an operational profile for the asset and including a probability density function associated with the operational durability of the asset, establishing a maintenance strategy associated with the asset, and combining each of the predictive models to generate a compound fleet performance model, the fleet performance model including a combined probability density function. The method also includes collecting actual asset performance and maintenance data to generate actual asset metrics, determining a fleet performance profile based on the actual asset metrics indicative of a health assessment of the fleet, comparing the predicted fleet performance with the actual fleet performance, and ascertaining actionable choices for managing the assets based on a deviation of the predicted and actual fleet performance. ## Claims ### Claim 1 A method of customizable service for an asset, the method comprising: generating a predictive model for each asset of a plurality of assets forming a fleet, each predictive model based on an operational profile for the asset, the predictive model including a probability density function associated with the operational durability of the asset; establishing a maintenance strategy associated with at least one asset associated with the fleet, the maintenance strategy including user defined metrics; combining each of the predictive models to generate a predicted compound fleet performance model, the fleet performance model including a combined probability density function; collecting actual asset performance and maintenance data for the at least one asset of the plurality of assets to generate actual asset metrics; determining, by a computer processor, a fleet performance profile based on the actual asset metrics indicative of a health assessment of the fleet; comparing, by the computer processor, the predicted fleet performance profile with the actual fleet performance profile; and ascertaining actionable choices for managing the assets based on a deviation of the predicted fleet performance profile and the actual fleet performance profile. ### Claim 2 (depends on 1) The method of claim 1, wherein the predictive model is based on a Weibull distribution. ### Claim 3 (depends on 1) The method of claim 1, wherein the predictive model is based on at least one of delivery schedules, repair models, overhaul models, mission data. ### Claim 4 (depends on 1) The method of claim 1, wherein the combining includes an integration of each probability density function associated with the fleet. ### Claim 5 (depends on 1) The method of claim 1, wherein the determining includes updating the predictive models for the assets of the fleet based on the collected performance and maintenance data. ### Claim 6 (depends on 1) The method of claim 1, wherein the actual asset performance and maintenance data includes at least one of actual failure data, actual repair rates associated with selected assets in the fleet. ### Claim 7 (depends on 1) The method of claim 1, wherein the comparing is based on determining if an actual failure rate exceeds an expected or predicted failure rate. ### Claim 8 (depends on 1) The method of claim 1, wherein the actionable choices are based on computing a deviation of fleet level actual failure data from predictions and updating the predictive models based on the deviation. ### Claim 9 (depends on 8) The method of claim 8, wherein the computing is based on a Wasserstein distance. ### Claim 10 (depends on 1) The method of claim 1, further including adjusting mission profiles for at least one asset based on the actual fleet performance deviation from a predicted fleet performance. ### Claim 11 (depends on 1) The method of claim 1, further including adjusting a maintenance strategy for at least one asset based on an actual fleet performance deviation from a predicted fleet performance. ### Claim 12 (depends on 1) The method of claim 1, further including adjusting a maintenance strategy for at least one asset based on at least one of actual fleet performance and actual asset performance. ### Claim 13 (depends on 1) The method of claim 1, further including updating a predictive model associated with at least one asset of the plurality of assets based on at least one of actual fleet performance and actual asset performance. ### Claim 14 (depends on 1) The method of claim 1, further including reiterating at least the generating, combining, collecting, determining, comparing, and ascertaining for a selected duration. ### Claim 15 (depends on 14) The method of claim 14, wherein the selected duration corresponds to a duration of a maintenance contract. ### Claim 16 (depends on 1) The method of claim 1, wherein the predictive model includes a selected look ahead time. ### Claim 17 (depends on 16) The method of claim 16, wherein the selected look ahead time is less than or equal to a contract duration. ### Claim 18 (depends on 1) The method of claim 1, further including generating a report indicative of at least an actual fleet performance deviation from a predicted fleet performance. ### Claim 19 (depends on 1) The method of claim 1, wherein the asset is at least one of a gas turbine engine and a component of a gas turbine engine. ### Claim 20 A maintenance system for providing customizable service for an asset, the system configured to: generate a predictive model for each asset of a plurality of assets forming a fleet, each predictive model based on an operational profile for the asset, the predictive model including a probability density function associated with the operational durability of the asset; establish a maintenance strategy associated with at least one asset associated with the fleet, the maintenance strategy including user defined metrics; combine each of the predictive models to generate a predicted compound fleet performance model, the fleet performance model including a combined probability density function; collect actual asset performance and maintenance data for the at least one asset of the plurality of assets to generate actual asset metrics; determine a fleet performance profile based on the actual asset metrics indicative of a health assessment of the fleet, compare the predicted fleet performance profile with the actual fleet performance profile, and ascertain actionable choices for managing the assets based on a deviation of the predicted fleet performance profile and the actual fleet performance profile.