Search NASA⌕ Search
Home/Archive/NASA NTRS

NASA NTRS · 20140008623

Data Fusion Tool for Spiral Bevel Gear Condition Indicator Data

Dempsey, Paula J.·Antolick, Lance J.·Branning, Jeremy S.·Thomas, Josiah
View original source ↗Export metadata ↓

Abstract

Tests were performed on two spiral bevel gear sets in the NASA Glenn Spiral Bevel Gear Fatigue Test Rig to simulate the fielded failures of spiral bevel gears installed in a helicopter. Gear sets were tested until damage initiated and progressed on two or more gear or pinion teeth. During testing, gear health monitoring data was collected with two different health monitoring systems. Operational parameters were measured with a third data acquisition system. Tooth damage progression was documented with photographs taken at inspection intervals throughout the test. A software tool was developed for fusing the operational data and the vibration based gear condition indicator (CI) data collected from the two health monitoring systems. Results of this study illustrate the benefits of combining the data from all three systems to indicate progression of damage for spiral bevel gears. The tool also enabled evaluation of the effectiveness of each CI with respect to operational conditions and fault mode.

Explore related subjects

health monitoringgearsdata fusion

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dempsey, Paula J., Antolick, Lance J., Branning, Jeremy S., Thomas, Josiah. 2014-02-03. Data Fusion Tool for Spiral Bevel Gear Condition Indicator Data. https://ntrs.nasa.gov/citations/20140008623

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

NASA NTRS2016

Health Monitoring of a Planetary Rover Using Hybrid Particle Petri Nets

This paper focuses on the application of a Petri Net-based diagnosis method on a planetary rover prototype.The diagnosis is performed by using a model-based method in the context of health management of hybrid systems.In system health management, the diagnosis task aims at determining the current health state of a system and the fault occurrences that lead to this state. The Hybrid Particle Petri Nets (HPPN) formalism is used to model hybrid systems behavior and degradation, and to define the generation of diagnosers to monitor the health states of such systems under uncertainty. At any time, the HPPN-based diagnoser provides the current diagnosis represented by a distribution of beliefs over the health states. The health monitoring methodology is demonstrated on the K11 rover. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

health monitoring↗
NASA NTRS2015

Signal Processing for Determining Water Height in Steam Pipes with Dynamic Surface Conditions

An enhanced signal processing method based on the filtered Hilbert envelope of the auto-correlation function of the wave signal has been developed to monitor the height of condensed water through the steel wall of steam pipes with dynamic surface conditions. The developed signal processing algorithm can also be used to estimate the thickness of the pipe to determine the cut-off frequency for the low pass filter frequency of the Hilbert Envelope. Testing and analysis results by using the developed technique for dynamic surface conditions are presented. A multiple array of transducers setup and methodology are proposed for both the pulse-echo and pitch-catch signals to monitor the fluctuation of the water height due to disturbance, water flow, and other anomaly conditions.

health monitoring↗
NASA NTRS2013

Investigation of Sideband Index Response to Prototype Gear Tooth Damage

The objective of this analysis was to evaluate the ability of gear condition indicators (CI) to detect contact fatigue damage on spiral bevel gear teeth. Tests were performed in the NASA Glenn Spiral Bevel Gear Fatigue Rig on eight prototype gear sets (pinion/gear). Damage was initiated and progressed on the gear and pinion teeth. Vibration data was measured during damage progression at varying torque values while varying damage modes to the gear teeth were observed and documented with inspection photos. Sideband indexes (SI) and root mean square (RMS) CIs were calculated from the time synchronous averaged vibration data. Results found that both CIs respond differently to varying torque levels, damage levels and damage modes

health monitoring↗