US 12,333,865 B2
Automated prediction of repair based on sensor data
Rashmi Sundareswara, Topanga, CA (US); Franz D. Betz, Renton, WA (US); and Tsai-Ching Lu, Thousand Oaks, CA (US)
Assigned to The Boeing Company, Arlington, VA (US)
Filed by THE BOEING COMPANY, Chicago, IL (US)
Filed on Aug. 30, 2021, as Appl. No. 17/461,306.
Claims priority of provisional application 63/072,292, filed on Aug. 31, 2020.
Prior Publication US 2022/0068042 A1, Mar. 3, 2022
Int. Cl. G07C 5/00 (2006.01); G06F 18/23213 (2023.01); G06N 5/02 (2023.01); G06N 5/04 (2023.01); G07C 5/08 (2006.01)
CPC G07C 5/006 (2013.01) [G06F 18/23213 (2023.01); G06N 5/02 (2013.01); G06N 5/04 (2013.01); G07C 5/0808 (2013.01); G07C 5/0825 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus for automating prediction of a repair for an aircraft, the apparatus comprising:
a communications device mounted to the aircraft and configured to communicate with an aircraft sensor device monitoring an electrical, hydraulic, or propulsion aircraft operating system of the aircraft;
a memory device mounted to the aircraft and configured to store computer-readable program code for a knowledge-based system including an inference engine and a knowledge base; and
a processing circuit mounted to the aircraft and configured to access the memory device and execute the computer-readable program code to cause the inference engine and thereby the apparatus to:
receive, via the communications device from the aircraft sensor device monitoring the aircraft operating system, a time series of measurements of a plurality of real-time operating conditions of the aircraft recorded during a flight operation of the aircraft;
cluster the time series of measurements into respective clusters of measurements;
identify a pattern across the clusters of measurements, including:
compressing sensor data points in the time series of measurements to form compressed data, wherein the compression is based on:
computing a distance between a data point of the sensor data points and a cluster state that represents a mean value of data points in a cluster of a plurality of clusters of the clusters of measurements, and
assigning the cluster state to the data point based on determining that the computed distance between the data point and the state is the shortest from a set of distances computed between the data point and cluster states of the plurality of clusters, and
sequencing latent states for the compressed data computed for n seconds in a sliding window;
define a current state of the aircraft that includes the pattern across the clusters of measurements;
access the knowledge base including a set of historical cases describing historical problem states of the aircraft and respective solutions, the historical problem states of the aircraft including patterns across clusters of measurements of the plurality of operating conditions of the aircraft recorded during previous instances of operation of the aircraft during which failure modes of the aircraft operating systems of the aircraft occurred, and the respective solutions including repair actions performed to address respective ones of the failure modes;
identify, by searching the knowledge base, a historical case describing a respective solution to a historical problem state of the historical problem states similar to the current state, the respective solution being identified as a candidate solution to the current state, the respective solution to the historical problem state including a repair action; and
generate an output display indicating the repair action to address the current state.