Condition Monitoring and Trend Prediction Based on Flight Data

2016 ◽  
pp. 153-213
Author(s):  
Jianye Zhang ◽  
Peng Zhang
Keyword(s):  
Condition Monitoring ◽  
Trend Prediction ◽  
Flight Data

scholarly journals A Data Filter for Identifying Steady-State Operating Points in Engine Flight Data for Condition Monitoring Applications

2010 ◽  
Author(s):  
Donald L. Simon ◽  
Jonathan S. Litt
Keyword(s):  
Steady State ◽  
Standard Deviation ◽  
Condition Monitoring ◽  
Measurement Data ◽  
Streaming Data ◽  
State Data ◽  
Flight Data ◽  
Monitoring Applications ◽  
The Mean ◽  
Operating Points

This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point, and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications.

Non-destructive online condition monitoring and trend prediction of lubricating oil in a steam turbine

2016 ◽  
Vol 58 (12) ◽  
pp. 649-655
Author(s):  
Xiaojian Xu ◽  
Xinping Yan ◽  
Chenxing Sheng ◽  
Chengqing Yuan ◽  
Baobao Song
Keyword(s):  
Steam Turbine ◽  
Condition Monitoring ◽  
Lubricating Oil ◽  
Trend Prediction ◽  
Non Destructive

scholarly journals A Data Filter for Identifying Steady-State Operating Points in Engine Flight Data for Condition Monitoring Applications

2011 ◽  
Vol 133 (7) ◽  
Author(s):  
Donald L. Simon ◽  
Jonathan S. Litt
Keyword(s):  
Steady State ◽  
Standard Deviation ◽  
Condition Monitoring ◽  
Measurement Data ◽  
Streaming Data ◽  
State Data ◽  
Flight Data ◽  
Monitoring Applications ◽  
The Mean ◽  
Operating Points

This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications.

A Field Demonstration of the Air Traffic Control Tower Flight Data Manager Prototype

2011 ◽  
Author(s):  
Hayley J. Davison Reynolds ◽  
Maria Picardi Kuffner ◽  
Sarah K. Yenson
Keyword(s):  
Traffic Control ◽  
Air Traffic Control ◽  
Air Traffic ◽  
Flight Data ◽  
Data Manager

Dose Tracker Application for Collecting Medication Use Data from International Space Station Crew

2020 ◽  
Vol 91 (1) ◽  
pp. 41-45 ◽  
Author(s):  
Virginia. E. Wotring ◽  
LaRona K. Smith
Keyword(s):  
Data Collection ◽  
International Space Station ◽  
Sleep Disturbances ◽  
Space Station ◽  
Medication Use ◽  
International Space ◽  
Flight Data ◽  
Open Questions ◽  
Share Data ◽  
Usability Feedback

INTRODUCTION: There are knowledge gaps in spaceflight pharmacology with insufficient in-flight data to inform future planning. This effort directly addressed in-mission medication use and also informed open questions regarding spaceflight-associated changes in pharmacokinetics (PK) and/or pharmacodynamics (PD).METHODS: An iOS application was designed to collect medication use information relevant for research from volunteer astronaut crewmembers: medication name, dose, dosing frequency, indication, perceived efficacy, and side effects. Leveraging the limited medication choices aboard allowed a streamlined questionnaire. There were 24 subjects approved for participation.RESULTS: Six crewmembers completed flight data collection and five completed ground data collection before NASA’s early study discontinuation. There were 5766 medication use entries, averaging 20.6 ± 8.4 entries per subject per flight week. Types of medications and their indications were similar to previous reports, with sleep disturbances and muscle/joint pain as primary drivers. Two subjects treated prolonged skin problems. Subjects also used the application in unanticipated ways: to note drug tolerance testing or medication holiday per research protocols, and to share data with flight surgeons. Subjects also provided usability feedback on application design and implementation.DISCUSSION: The volume of data collected (20.6 ± 8.4 entries per subject per flight week) is much greater than was collected previously (<12 per person per entire mission), despite user criticisms regarding app usability. It seems likely that improvements in a software-based questionnaire application could result in a robust data collection tool that astronauts find more acceptable, while simultaneously providing researchers and clinicians with useful data.Wotring VE, Smith LK. Dose tracker application for collecting medication use data from International Space Station crew. Aerosp Med Hum Perform. 2020; 91(1):41–45.

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