US 12,322,225 B2
Aircraft maintenance of line replaceable units
Kang-Yu Ni, Calabasas, CA (US); Tsai-Ching Lu, Thousand Oaks, CA (US); Alexander Norman Waagen, Sunnyvale, CA (US); Aruna Rani Jammalamadaka, Camarillo, CA (US); Charles Eugene Martin, Santa Monica, CA (US); Alice Ann Murphy, Mesa, AZ (US); Derek Samuel Fok, Mesa, AZ (US); Kirby Joe Keller, Chesterfield, MO (US); and Douglas Peter Knapp, West Chester, PA (US)
Assigned to The Boeing Company, Chicago, IL (US)
Filed by The Boeing Company, Chicago, IL (US)
Filed on Mar. 9, 2022, as Appl. No. 17/654,199.
Claims priority of provisional application 63/202,441, filed on Jun. 11, 2021.
Prior Publication US 2022/0398550 A1, Dec. 15, 2022
Int. Cl. G06Q 10/00 (2023.01); G05B 23/02 (2006.01); G06Q 10/20 (2023.01); G07C 5/00 (2006.01); G07C 5/08 (2006.01)
CPC G07C 5/0816 (2013.01) [G05B 23/0283 (2013.01); G06Q 10/20 (2013.01); G07C 5/006 (2013.01)] 28 Claims
OG exemplary drawing
 
1. A method for managing a platform, the method comprising:
receiving, into a platform manager in a computer system, historical sensor information indicating a historical platform health and historical context information corresponding to the historical sensor information in which the historical context information is for a set of operating conditions and comprises timescales;
forming, by the platform manager, context profiles in a training dataset from the historical context information;
using historical sensor information and a desired level of performance for a component of the platform in a machine learning algorithm for transforming the context profiles into coefficient matrices while training a time-varying multi-task machine learning model for survival analysis to: handle time-varying feature vectors, obtain a sparse coefficient matrix, and derive a remaining useful life for a component of the platform;
receiving, by the platform manager, from a sensor system for the platform, sensor information comprising context information and platform health information for a platform health of the platform and forming a context profile;
sending, by the computer system, the sensor information for the platform health of the platform and the context profile into a trained time-varying multi-task machine learning model for survival analysis;
transforming, in the trained time-varying multi-task machine learning model for survival analysis, the context profile into coefficient matrices and therefrom deriving a remaining useful life vector for the component of the platform; and
procuring, using the remaining useful life vector, resources comprising components for service and repair of the component in the platform.