Honeywell International Inc.

Timothy Griffith

Qixiang Li

Sapan Nitin Shah

Dinkar A Mylaraswamy

Megan L Hawley

Ananda Vel Murugan Chandra Mohan

Cavene Robinson

Sapan N Shah

Methods and systems are provided for predictive maintenance of a vehicle component. One method involves mapping a current instance of a component of a vehicle to one of plurality of degradation groups of prior lifecycles for other instances of the component based on a relationship between performance measurement data for the current instance and historical performance measurement data associated with that respective degradation group, obtaining contextual data associated with operation of the vehicle, and determining a maintenance recommendation for the current instance of the component based on the contextual data using a predictive maintenance model associated with the mapped degradation group.

Maintenance recommendations using lifecycle clustering

Disclosed are systems, methods, and non-transitory computer-readable medium for generating maintenance recommendation for an auxiliary power unit (APU). One system may include collecting APU data, applying an algorithm that may analyze the APU data using one or more tuning parameters to capture elements of the APU data that indicate an APU health concern, and transmitting the captured elements indicating the APU health concern to a cloud-based health monitoring system.

Internet connected auxiliary power unit airline maintenance system

A processor implemented method in an aircraft-mounted edge computing device configured for real-time monitoring and analysis of APU health information onboard an aircraft is provided. The method comprises: receiving raw APU data from an electronic control unit associated with an APU; downloading one or more tuning parameters from a cloud-based health monitoring system, wherein the one or more tuning parameters includes a threshold level that is adjustable by the cloud-based health monitoring system based on analyzed APU health information; analyzing the raw APU data using the one or more tuning parameters to capture elements of the raw APU data that indicate an APU health concern; reducing the amount of raw APU data in the captured elements by removing raw APU data indicating intermittent or false-positive APU health concerns; and transmitting the captured elements indicating an APU heath concern using an Internet of things protocol to the cloud-based health monitoring system.

Edge computing based airplane auxiliary power unit health monitoring system

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Morris Plains, NJ, United States of America

Timothy Griffith