Honeywell International Inc.

Jan Zirnstein

Chris Hickenbottom

Matthew Damon Emery

Nikhita Sagar

Yuan Qin

Manoj Kumar

Gregory Edward Stewart

Jonathan T Grunow

Marc Light

David Joachim Germann

Vijay Kumar Ravi

Amit Pandey

Milad Makkie

Various embodiments described herein relate to an anomaly detection framework adaptable to different asset types. In this regard, a data stream associated with a first asset is received. The data stream is then processed to generate output data by encoding the data stream based on historical data associated with the first asset, the historical data comprising clustered data representative of fault states and one or more non-fault states. Furthermore, in accordance with a determination that the generated output data is indicative of a potential fault of the first asset, fault data indicative of the potential fault is generated and caused to be transmitted to an administrative device for display.

Asset agnostic anomaly detection using clustering and auto encoder

Methods, apparatuses, and systems for predicting radio altimeter failures are provided. An example method may include determining a first plurality of altitude values associated with a first radio altimeter, determining a second plurality of altitude values associated with a second radio altimeter, calculating a first level feature based at least in part on the first plurality of altitude values and the second plurality of altitude values, and determining a radio altimeter failure indicator based at least in part on the first level feature.

Methods, apparatuses and systems for predicting radio altimeter failure

Systems and methods of providing usage-based maintenance services for a plurality of vehicles arranged in a fleet includes receiving usage data. A usage parameter distribution is generated, and a fleet usage model is generated according to the distribution. The fleet usage model expresses a score as a function of the usage parameter. Also, a score distribution is generated, and a reward model is generated according to the score distribution. The reward model expresses a maintenance reward as a function of the score. Additionally, the score for one of the vehicles is determined using the fleet usage model, and the maintenance reward is determined for the vehicle using the reward model.

Usage-based maintenance service system

A method includes identifying costs associated with different outcomes of a failure prediction algorithm. The algorithm is configured to predict one or more faults with at least one piece of industrial equipment. The different outcomes include both successful and unsuccessful predictions by the algorithm. The method also includes identifying a threshold value for the algorithm using the costs, where the threshold value is used by the failure prediction algorithm to identify whether maintenance of the at least one piece of industrial equipment is needed. The method further includes providing the threshold value to the algorithm. The threshold value is selected such that a net positive economic benefit is obtained from use of the threshold value with the failure prediction algorithm. In addition, the method can include generating a signal indicating whether maintenance is needed based on a comparison of an indicator value calculated using the algorithm and the threshold value.

Cost-driven system and method for predictive equipment failure detection

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Atlanta, GA, United States of America

Jan Zirnstein