Granted Patent B2

US 12,469,012 · App. 18/157,620 · Granted Nov 11, 2025

Inventors: Ranjan K. Paul (Sammamish, WA); Jan Irvahn (Richland, WA); Christopher D. Deits (Renton, WA); Liessman E. Sturlaugson (St. Louis, MO); Ameya Deepak Kamat (Bengaluru, IN)

Assignee: The Boeing Company

G06Q10/20

Abstract

Claims (64)

1 . A method performed by a computing system for determining a maintenance interval for a subject aircraft configuration, the method comprising:

obtaining sensor data obtained by a set of sensors and reported by an electronic system of each aircraft of a population of multiple aircraft of the subject aircraft configuration;

obtaining a failure mode definition that identifies a set of failure modes involving a component of the subject aircraft configuration;

obtaining a model definition that identifies, for each failure mode of a set of two or more failure modes involving the component, one or more predictive models to be implemented by the computing system for that failure mode from among a set of predictive models;

for a first failure mode of the set of failure modes involving the component, selecting a first predictive model of the set of predictive models based on the model definition;

implementing the first predictive model at the computing system to determine a first lifetime-probability distribution of the first failure mode involving the component based, at least in part, on the sensor data;

for a second failure mode of the set of failure modes involving the component, selecting a second predictive model of the set of predictive models based on the model definition that differs from the first predictive model;

implementing the second predictive model at the computing system that differs from the first predictive model to determine a second lifetime-probability distribution of the second failure mode involving the component based, at least in part, on the sensor data;

determining a maintenance interval for the component based, at least in part, on the first lifetime-probability distribution and the second lifetime-probability distribution; and

outputting the maintenance interval;

wherein the set of predictive models includes two or more of:

a minor-evident model that considers a magnitude of a failure of the component categorized as either (1) loss of function or (2) no loss of function, and by which a lifetime probability distribution determined for a failure mode is based on sensor data involving the loss of function and is not based on sensor data involving no loss of function,

a condition-based model that considers whether a condition has been met on a per-aircraft basis based on the sensor data obtained from the aircraft as a prerequisite to utilizing the sensor data in determining a lifetime probability distribution of a failure mode,

a risk-equivalent model that considers in-service risk by combining (1) a measure of scheduled maintenance with risk with (2) a measure of predictive maintenance with precision in determining a lifetime probability distribution of a failure mode based on sensor data.

2 . The method of claim 1 , further comprising:

implementing the first predictive model at the computing system to determine a third lifetime-probability distribution of a third failure mode of the set of failure modes involving the component based, at least in part, on the sensor data;

wherein the third failure mode differs from the first failure mode;

wherein determining the maintenance interval for the component is further based, at least in part, on the third lifetime-probability distribution.

3 . The method of claim 1 , further comprising:

implementing a third predictive model at the computing system to determine a third lifetime-probability distribution of a failure mode of the set of failure modes involving the component based, at least in part, on the sensor data;

wherein the third predictive model differs from the first predictive model and the second predictive model.

4 . The method of claim 1 , wherein the set of failure modes involving the component of the subject aircraft configuration includes a multi-component failure mode involving the component and one or more other components of the subject aircraft configuration; and

wherein the maintenance interval that is output is for a maintenance subtask for the component and the one or more other components of the multi-component failure mode.

5 . The method of claim 1 , further comprising:

obtaining a maintenance task definition that identifies a maintenance task for the subject aircraft configuration having a plurality of maintenance subtasks, and one or more components of the subject aircraft configuration for each maintenance subtask; and

associating the maintenance interval that is output with a maintenance subtask of the plurality of maintenance subtasks involving the component as identified by the maintenance task definition.

6 . The method of claim 1 , wherein the maintenance interval for the component is based, at least in part, on a combination of the first lifetime-probability distribution and the second lifetime-probability distribution.

7 . The method of claim 6 , wherein the maintenance interval for the component is further based, at least in part, on a threshold risk level applied to the combination of the first lifetime-probability distribution and to the second lifetime-probability distribution.

8 . The method of claim 1 , wherein the computing system includes a model definition interface by which data of the model definition is provided to the computing system as input data.

9 . The method of claim 1 , wherein the computing system includes a model definition interface by which data of the model definition is provided by the computing system as output data.

10 . The method of claim 1 , wherein the computing system includes a model definition interface by which data of the model definition is modified by users.

11 . A computing system of one or more computing devices, comprising:

a logic machine; and

a storage machine having instructions stored thereon executable by the logic machine to:

obtain sensor data reported by an electronic system of each aircraft of a population of multiple aircraft of the subject aircraft configuration;

obtain a failure mode definition that identifies a set of failure modes involving a component of the subject aircraft configuration;

obtain a model definition that identifies, for each failure mode of a set of two or more failure modes involving the component, one or more predictive models to be implemented by the computing system for that failure mode from among a set of predictive models;

for a failure mode of the set of failure modes involving the component, select a first predictive model of the one or more predictive models based on the model definition;

implement the first predictive model at the computing system to determine a first lifetime-probability distribution of the first failure mode involving the component based, at least in part, on the sensor data;

for a second failure mode of the set of failure modes involving the component, select a second predictive model based on the model definition that differs from the first predictive model;

implement the second predictive model at the computing system that differs from the first predictive model to determine a second lifetime-probability distribution of the second failure mode involving the component based, at least in part, on the sensor data;

determine a maintenance interval for the component based, at least in part, on the first lifetime-probability distribution and the second lifetime-probability distribution; and

output the maintenance interval;

wherein the set of predictive models includes two or more of:

a minor-evident model that considers a magnitude of a failure of the component categorized as either (1) loss of function or (2) no loss of function, and by which a lifetime probability distribution determined for a failure mode is based on sensor data involving the loss of function and is not based on sensor data involving no loss of function,

a condition-based model that considers whether a condition has been met on a per-aircraft basis based on the sensor data obtained from the aircraft as a prerequisite to utilizing the sensor data in determining a lifetime probability distribution of a failure mode,

a risk-equivalent model that considers in-service risk by combining a measure of (1) scheduled maintenance with risk with (2) a measure of predictive maintenance with precision in determining a lifetime probability distribution of a failure mode based on sensor data.

12 . The computing system of claim 11 , wherein the maintenance interval for the component is based, at least in part, on a combination of the first lifetime-probability distribution and the second lifetime-probability distribution.

13 . The computing system of claim 12 , wherein the maintenance interval for the component is further based, at least in part, on a threshold risk level applied to the combination of the first lifetime-probability distribution and the second lifetime-probability distribution.

14 . The computing system of claim 11 , wherein the instructions are further executable to provide a model definition interface by which data of the model definition is provided to the computing system as input data.

15 . The computing system of claim 11 , wherein the instructions are further executable by the logic machine to:

obtain a maintenance task definition that identifies a maintenance task for the subject aircraft configuration having a plurality of maintenance subtasks, and one or more components of the subject aircraft configuration for each maintenance subtask; and

associate the maintenance interval that is output with a maintenance subtask of the plurality of maintenance subtasks involving the component as identified by the maintenance task definition.

16 . The computing system of claim 11 , wherein the set of failure modes involving the component of the subject aircraft configuration includes a multi-component failure mode involving the component and one or more other components of the subject aircraft configuration; and

wherein the maintenance interval that is output is for a maintenance subtask for the component and the one or more other components of the multi-component failure mode.

17 . The computing system of claim 11 , wherein the instructions are further executable by the logic machine to:

implement the first predictive model at the computing system to determine a third lifetime-probability distribution of a third failure mode of the set of failure modes involving the component based, at least in part, on the sensor data;

wherein the third failure mode differs from the first failure mode;

wherein determining the maintenance interval for the component is further based, at least in part, on the third lifetime-probability distribution.

18 . The computing system of claim 11 , wherein the instructions are further executable by the logic machine to:

implement a third predictive model at the computing system to determine a third lifetime-probability distribution of a failure mode of the set of failure modes involving the component based, at least in part, on the sensor data;

wherein the third predictive model differs from the first predictive model and the second predictive model.

19 . The computing system of claim 11 , wherein the instructions are further executable to provide a model definition interface by which data of the model definition is provided by the computing system as output data.

20 . The computing system of claim 11 , wherein the instructions are further executable to provide a model definition interface by which data of the model definition is modified by users.

Assignments (1)

Continuity (1)

References Cited (13)