Engineering topics

Dempsey, Paula J.

Publications and source records attributed to Dempsey, Paula J..

48 records · Page 3

NASA NTRS2003

Integrating Oil Debris and Vibration Measurements for Intelligent Machine Health Monitoring

A diagnostic tool for detecting damage to gears was developed. Two different measurement technologies, oil debris analysis and vibration were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig. An oil debris sensor and the two vibration algorithms were adapted as the diagnostic tools. An inductance type oil debris sensor was selected for the oil analysis measurement technology. Gear damage data for this type of sensor was limited to data collected in the NASA Glenn test rigs. For this reason, this analysis included development of a parameter for detecting gear pitting damage using this type of sensor. The vibration data was used to calculate two previously available gear vibration diagnostic algorithms. The two vibration algorithms were selected based on their maturity and published success in detecting damage to gears. Oil debris and vibration features were then developed using fuzzy logic analysis techniques, then input into a multi sensor data fusion process. Results show combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spur gears. As a result of this research, this new diagnostic tool has significantly improved detection of gear damage in the NASA Glenn Spur Gear Fatigue Rigs. This research also resulted in several other findings that will improve the development of future health monitoring systems. Oil debris analysis was found to be more reliable than vibration analysis for detecting pitting fatigue failure of gears and is capable of indicating damage progression. Also, some vibration algorithms are as sensitive to operational effects as they are to damage. Another finding was that clear threshold limits must be established for diagnostic tools. Based on additional experimental data obtained from the NASA Glenn Spiral Bevel Gear Fatigue Rig, the methodology developed in this study can be successfully implemented on other geared systems.

Dempsey, Paula J.↗

NASA NTRS2003

Spiral-Bevel-Gear Damage Detected Using Decision Fusion Analysis

Helicopter transmission integrity is critical to helicopter safety because helicopters depend on the power train for propulsion, lift, and flight maneuvering. To detect impending transmission failures, the ideal diagnostic tools used in the health-monitoring system would provide real-time health monitoring of the transmission, demonstrate a high level of reliable detection to minimize false alarms, and provide end users with clear information on the health of the system without requiring them to interpret large amounts of sensor data. A diagnostic tool for detecting damage to spiral bevel gears was developed. (Spiral bevel gears are used in helicopter transmissions to transfer power between nonparallel intersecting shafts.) Data fusion was used to integrate two different monitoring technologies, oil debris analysis and vibration, into a health-monitoring system for detecting surface fatigue pitting damage on the gears.

Dempsey, Paula J.↗

NASA NTRS2002

Spiral Bevel Gear Damage Detection Using Decision Fusion Analysis

A diagnostic tool for detecting damage to spiral bevel gears was developed. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual monitoring technologies. This diagnostic tool was evaluated by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spiral Bevel Gear Fatigue Rigs. Data was collected during experiments performed in this test rig when pitting damage occurred. Results show that combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spiral bevel gears.

Dempsey, Paula J.↗

NASA NTRS2002

Integrating Oil Debris and Vibration Gear Damage Detection Technologies Using Fuzzy Logic

A diagnostic tool for detecting damage to spur gears was developed. Two different measurement technologies, wear debris analysis and vibration, were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Test Rig. Experimental data were collected during experiments performed in this test rig with and without pitting. Results show combining the two measurement technologies improves the detection of pitting damage on spur gears.

Dempsey, Paula J.↗

NASA NTRS2001

Gear Damage Detection Using Oil Debris Analysis

The purpose of this paper was to verify, when using an oil debris sensor, that accumulated mass predicts gear pitting damage and to identify a method to set threshold limits for damaged gears. Oil debris data was collected from 8 experiments with no damage and 8 with pitting damage in the NASA Glenn Spur Gear Fatigue Rig. Oil debris feature analysis was performed on this data. Video images of damage progression were also collected from 6 of the experiments with pitting damage. During each test, data from an oil debris sensor was monitored and recorded for the occurrence of pitting damage. The data measured from the oil debris sensor during experiments with damage and with no damage was used to identify membership functions to build a simple fuzzy logic model. Using fuzzy logic techniques and the oil debris data, threshold limits were defined that discriminate between stages of pitting wear. Results indicate accumulated mass combined with fuzzy logic analysis techniques is a good predictor of pitting damage on spur gears.

Dempsey, Paula J.↗

NASA NTRS2001

Minimizing Load Effects on NA4 Gear Vibration Diagnostic Parameter

NA4 is a vibration diagnostic parameter, developed by researchers at NASA Glenn Research Center, for health monitoring of gears in helicopter transmissions. The NA4 reacts to the onset of gear pitting damage and continues to react to the damage as it spreads. This research also indicates NA4 reacts similarly to load variations. The sensitivity of NA4 to load changes will substantially affect its performance on a helicopter gearbox that experiences continuously changing load throughout its flight regimes. The parameter NA4 has been used to monitor gear fatigue tests at constant load. At constant load, NA4 effectively detects the onset of pitting damage and tracks damage severity. Previous research also shows that NA4 reacts to changes in load applied to the gears in the same way it reacts to the onset of pitting damage. The method used to calculate NA4 was modified to minimize these load effects. The modified NA4 parameter was applied to four sets of experimental data. Results indicate the modified NA4 is no longer sensitive to load changes, but remains sensitive to pitting damage.

Dempsey, Paula J.↗

NASA NTRS2000

A Comparison of Vibration and Oil Debris Gear Damage Detection Methods Applied to Pitting Damage

Helicopter Health Usage Monitoring Systems (HUMS) must provide reliable, real-time performance monitoring of helicopter operating parameters to prevent damage of flight critical components. Helicopter transmission diagnostics are an important part of a helicopter HUMS. In order to improve the reliability of transmission diagnostics, many researchers propose combining two technologies, vibration and oil monitoring, using data fusion and intelligent systems. Some benefits of combining multiple sensors to make decisions include improved detection capabilities and increased probability the event is detected. However, if the sensors are inaccurate, or the features extracted from the sensors are poor predictors of transmission health, integration of these sensors will decrease the accuracy of damage prediction. For this reason, one must verify the individual integrity of vibration and oil analysis methods prior to integrating the two technologies. This research focuses on comparing the capability of two vibration algorithms, FM4 and NA4, and a commercially available on-line oil debris monitor to detect pitting damage on spur gears in the NASA Glenn Research Center Spur Gear Fatigue Test Rig. Results from this research indicate that the rate of change of debris mass measured by the oil debris monitor is comparable to the vibration algorithms in detecting gear pitting damage.

Dempsey, Paula J.↗

NASA NTRS1995

Increasing Precision Of Temperature Sensors Of Liquid H2

Commercial silicon-diode temperature sensors intended for use in boiling or nearly boiling liquid hydrogen at temperatures near 37 degrees R recalibrated to greater precision by method involving careful attention to details of design, operation, and computation. Method based on fundamental electrical and thermodynamic principles and good engineering practice, also applicable to recalibration of other temperature sensors intended for use in other boiling or nearly boiling liquids.

Dempsey, Paula J.↗

NASA NTRS1993

Techniques for improving the accuracy of cyrogenic temperature measurement in ground test programs

The performance of a sensor is often evaluated by determining to what degree of accuracy a measurement can be made using this sensor. The absolute accuracy of a sensor is an important parameter considered when choosing the type of sensor to use in research experiments. Tests were performed to improve the accuracy of cryogenic temperature measurements by calibration of the temperature sensors when installed in their experimental operating environment. The calibration information was then used to correct for temperature sensor measurement errors by adjusting the data acquisition system software. This paper describes a method to improve the accuracy of cryogenic temperature measurements using corrections in the data acquisition system software such that the uncertainty of an individual temperature sensor is improved from plus or minus 0.90 deg R to plus or minus 0.20 deg R over a specified range.

Dempsey, Paula J.↗

NASA NTRS1993

Techniques for improving the accuracy of cryogenic temperature measurement in ground test programs

Dempsey, Paula J.↗

NASA NTRS1992

Using silicon diodes for detecting the liquid-vapor interface in hydrogen

Tests were performed using commercially available silicon diode temperature sensors to detect the location of the liquid-vapor interface in hydrogen during ground test programs. Results show that by increasing the current into the sensor, silicon diodes can be used as liquid level point sensors. After cycling the sensors from liquid to vapor several times, it was found that with a 30 mA (milliamps) input current, the sensors respond within 2 seconds by measuring a large voltage difference when transitioning from liquid to vapor across the interface. Nearly instantaneous response resulted during a transition form vapor to liquid. Detailed here are test procedures, experimental results, and guidelines for applying this information to other test facilities.

Dempsey, Paula J.↗

NASA NTRS1992

Using silicon diodes for detecting the liquid-vapor interface in hydrogen

Tests were performed using commercially available silicon diode temperature sensors to detect the location of the liquid-vapor interface in hydrogen during ground test programs. Results show that by increasing the current into the sensor, silicon diodes can be used as liquid level point sensors. After cycling the sensors from liquid to vapor several times, it was found that with a 30 mA (milliamps) input current, the sensors respond within 2 seconds by measuring a large voltage difference when transitioning from liquid to vapor across the interface. Nearly instantaneous response resulted during a transition from vapor to liquid. Detailed here are test procedures, experimental results, and guidelines for applying this information to other test facilities.

Dempsey, Paula J.↗