Stephen Charles Slaughter

US Patent:

20200110181, Apr 9, 2020

Filed:

Oct 4, 2018

Appl. No.:

16/151499

Inventors:

- Chicago IL, US
Charles E. Martin - Thousand Oaks CA, US
Stephen C. Slaughter - Scottsdale AZ, US
Richard Patrick - Irvine CA, US

International Classification:

G01S 19/20
G05B 23/02

Abstract:

An apparatus for detecting a fault state of an aircraft is provided. The apparatus accesses a training set of flight data for the aircraft. The training set includes observations of the flight data, each observation of the flight data includes measurements of properties selected and transformed into a set of features. The apparatus builds a generative adversarial network including a generative model and a discriminative model using the training set and the set of features, and builds an anomaly detection model to predict the fault state of the aircraft. The anomaly detection model is trained using the training set of flight data, simulated flight data generated by the generative model, and a subset of features from the set of features. The apparatus deploys the anomaly detection model to predict the fault state of the aircraft using additional observations of the flight data.

US Patent:

20160314632, Oct 27, 2016

Filed:

Apr 24, 2015

Appl. No.:

14/695081

Inventors:

- Chicago IL, US
Charles E. MARTIN - Westlake Village CA, US
Alice A. MURPHY - Mesa AZ, US
Stephen C. SLAUGHTER - Scottsdale AZ, US
Christopher R. WEZDENKO - Mesa AZ, US

International Classification:

G07C 5/08
G07C 5/02

Abstract:

A method for detecting vehicle system faults includes receiving, with a processor, a plurality of sensor signals from one or more sensors; thresholding, with the processor, the plurality of sensor signals for each respective sensor substantially in real time; and generating, with the processor, abnormal derivative frequency values for each of the plurality of thresholded sensor signals in real time and determining an operational status of at least each of the one or more sensors based on the abnormal derivative frequency values.