The Boeing Company

James Schimert

Timothy Joseph Wilmering

Omid B Nakhjavani

Liessman E Sturlaugson

Franz David Betz

Seema Chopra

Shahriar Alam

Jun Yuan

James M Ethington

Scott C Farner

Daniel D Gilbertson

Sabyasachi Basu

Balaje T Thumati

Greg Jackson

Andrey Aleksandrovich Zaikin

Darren Puigh

Sean Patrick Warlick

Steven C Runo

Art Wineland

Ryan K Coleman

Sudheer Palyam

Arthur Ray Wineland

Andrew J Parker

A diagnostic system for a closed fluid system is provided. The closed fluid system includes a first fluid subsystem and a second fluid subsystem connected using a valve or valve/actuator. The diagnostic system includes a first sensor disposed in the first fluid subsystem, a second sensor disposed in the second fluid subsystem and a computer. The computer receives first time-ordered fluid level measurements and second time-ordered fluid level measurements from respectively the first sensor and the second sensor and performs a comparison of first fluid level of the first fluid subsystem and second fluid level of the second fluid subsystem. The computer produces a notification indicating a predicted transfer of hydraulic fluid when the first time-ordered fluid level measurements and the second time-ordered fluid level measurements have at least a threshold divergence and outputs the notification for display to a user.

Diagnostic system for a closed fluid system

Systems and methods for indicating the health of brakes on an aircraft or a ground vehicle. One method for monitoring brake health calculates the ratio of input energy to output energy during braking. The input energy is a function of commanded brake force and speed. The output energy may be measured by measuring the increase in temperature or the actual brake force over time during brake application. When using brake temperatures, the temperatures in a pair of brakes may optionally be compared. If the respective measured temperatures of the two brakes differ by an amount that exceeds a specified threshold, the system produces an indication that one brake may require repair or replacement. Variability due to operational and environmental factors that affect measured temperature can be reduced by comparing the measured brake temperatures for one brake with selected brake temperature data collected from the same brake during other landings under similar conditions.

Brake health indicator systems using input and output energy

An example method includes receiving a plurality of data streams acquired for a respective parameter of a plurality of parameters indicating an operating condition of the aircraft, selecting at least one data stream corresponding to at least one parameter of the respective plurality of parameters, selecting a portion of data from the at least one data stream, comparing the portion of data to a model determined for the at least one parameter based on historical data, determining that a failure has occurred or is likely to occur during operation of the aircraft based on the comparing, and transmitting aircraft health monitoring information indicative of occurrence or likelihood of occurrence of the failure.

Real time streaming analytics for flight data processing

Predictive aircraft maintenance systems and methods are disclosed. Predictive maintenance methods may include extracting feature data from flight data collected during a flight of the aircraft, applying an ensemble of related classifiers to produce a classifier indicator for each classifier of the ensemble of classifiers, aggregating the classifier indicators to produce an aggregate indicator indicating an aggregate category of a selected component for a threshold number of future flights, and determining the performance status of the selected component based on the aggregate indicator. The classifiers are each configured to indicate a category of the selected component within a given number of flights. The given number of flights for each classifier is different. The threshold number of future flights is greater than or equal to the maximum of the given numbers of the classifiers. Predictive maintenance systems may include modules configured to extract feature data, classify feature data, and aggregate classifications.

Predictive aircraft maintenance systems and methods incorporating classifier ensembles

Mechanical systems wear or change over time. Data collected over a system's life can be input to statistical learning models to predict this wear/change. Previous work by the inventors trained a flexible empirical regression model at a fixed point of wear, and then applied it independently at time points over the life of an engine to predict wear. The embodiment disclosed herein relates those wear predictions over time using a time evolution model. The time evolution model is sequentially updated with new data, and effectively tunes the empirical model for each engine. The combined model predicts wear with dramatically reduced variability. The benefit of reduced variability is that engine wear is more evident, and it is possible to detect operational anomalies more quickly. In addition to tracking wear, the model is also used as the basis for a Bayesian approach to monitor for sudden changes and reject outliers, and adapt the model after these events.

Coupling time evolution model with empirical regression model to estimate mechanical wear

A method for data-driven anomaly detection may include monitoring a plurality of parameters associated with a plurality of subsystems of a system. The method may also include collecting data corresponding to each of the plurality of parameters from the plurality of subsystems and generating monitoring quantities based on the data. The method may also include determining if any quantities in the monitoring quantities exceed a predetermined limit. A contribution plot may be generated corresponding to each of the parameters in response to any of the quantities exceeding the predetermined limit. The method may further include determining which parameter is likely to cause an effect based on the contribution plot.

Data-driven anomaly detection to anticipate flight deck effects

There is provided an automated data driven method for predicting one or more operational states, such as wear or degradation, of a mechanical system over time. The method has the steps of collecting data on the mechanical system from a data recording device, preprocessing the collected data, selecting a training data set that represents a base condition for statistical comparison, fitting a statistical model to the training data set to relate a predicted response to nuisance variables at the base condition, and using an output model to predict what an observed response would have been at the base condition and calculating the difference between the observed response and the predicted response to predict the one or more operational states of the mechanical system.

Data driven method and system for predicting operational states of mechanical systems

Growing community of inventors

Seabeck, WA, United States of America

James Schimert