The Boeing Company

Robab Safa-Bakhsh

Patrick Harris

Jun Yuan

Ali Bahrami

Patrick Neal Harris

Paul Clark Parks

Bruce D Harmon

An off-board apparatus is provided for reducing nuisance fault indications from a vehicle. The apparatus is communicably coupled to a vehicle health monitoring (VHM) system, or one or more vehicle systems for at least collecting and communicating data thereto such as data that indicates fault generated by a line replaceable unit of the vehicle in response to a built-in test. The apparatus is also coupleable with a computerized maintenance management system configured to store data that describes a historical condition and maintenance of the vehicle. The apparatus is programmed to determine a probability of validity or invalidity of the fault indication, determine a condition indicator for the signal path based on the LRUs current and historical operational environment, and generate a notification, or output the fault indication for generation of a notification, only in an instance in which the fault indication, with a high probability is valid.

Reducing nuisance fault indications from a vehicle using physics based and data driven models

Method, system and computer program product for providing a predicted vehicle track and for providing alerts when two predicted vehicle tracks are closer than a threshold amount. A vehicle intent prediction model is generated based on past instance of tracks for a vehicle operation, known vehicle intent data for the past instances, and contextual factors, such as weather, airline operator, air vehicle type or configuration, day of the week, etc. for the past instances. The vehicle intent prediction model can be generated using one or more machine learning algorithms. A future vehicle trajectory for a current vehicle operation can be output by the vehicle intent prediction model using the current track and existing contextual factors for the current vehicle operation. In the event that two vehicles following their respective predicted vehicle future trajectories would be closer than a threshold distance, an alert can be provided.

Data driven airplane intent inferencing

An electrical power generation and distribution health monitoring system includes a network of a plurality of nodes included in an electrical system of a vehicle. Each node of the plurality of nodes is synchronized with other nodes of the plurality of nodes. The network is configured to communicate electrical system health data associated with the electrical system to at least one vehicle health reporting system.

Electrical power health monitoring system

A platform health monitoring system including an associative memory. The associative memory stores a plurality of known health profiles for a platform. The platform health monitoring system also includes an input device in communication with the associative memory. The input device is configured to receive a current health profile of the platform. The platform health monitoring system also may include a comparator configured to compare, in conjunction with the associative memory, the current health profile with the plurality of known health profile to generate a comparison. The comparator is further configured to generate, based on the comparison and in conjunction with the associative memory, a report on a current health of the platform.

Platform health monitoring system

A monitoring system, method and computer program product are provided for monitoring the health of a monitored system. The monitoring system may include a receiver module to receive a plurality of sensor data relating to the monitored system and to receive textual data, such as free text, relating to the monitored system. The monitoring system may also include an associative memory to store and process the sensor data and the textual data and to establish associations between at least some of the sensor data and the textual data. Further, the monitoring system may include a dynamic change identification module to identify an instance in which the sensor data represents a dynamic change in condition and a correlation module to identify an instance in which textual data is associated with the sensor data proximate the dynamic change in condition so as to correlate the sensor data and the textual data.

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Ambler, PA, United States of America

Robab Safa-Bakhsh