Engineering topics

Dempsey, Paula J.

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

At least 19 records

NASA NTRS2019

NASA Electrified Aircraft Propulsion Efforts

NASA's broad investments in Electrified Aircraft Propulsion (EAP) are reviewed in this paper. NASA investments are guided by an assessment of potential market impacts, technical key performance parameters, and technology readiness attained through a combination of studies, enabling fundamental research, and flight research. NASA has determined that the impact of EAP varies by market and NASA is considering three markets: national/international, on-demand mobility, and short haul regional air transport. Flight research is underway to demonstrate integrated solutions and inform standards and certification processes. This paper focuses on the vehicle related activities, however there are related NASA activities in air space management and vehicle autonomy activities as well as a breakthrough technology project called the Convergent Aeronautics Solutions Project. A key finding is that sufficient technical advances in key areas have been made which indicate EAP is a viable technology for aircraft. Significant progress has been made to reduce EAP adoption barriers and further work is needed to transition the technology to a commercial product and improve the technology so it is applicable to large transonic aircraft. This paper will review the activities of the Hybrid Gas Electric Subproject of the Advanced Air Transport Technology Project, the Revolutionary Vertical Lift Technology Project, and the X-57 Flight Demonstration Project, and discuss the potential EAP benefits for commercial and military applications.

Jansen, Ralph H.↗

NASA NTRS2017

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗

NASA NTRS2016

Health Monitoring Survey of Bell 412EP Transmissions

Health and usage monitoring systems (HUMS) use vibration-based Condition Indicators (CI) to assess the health of helicopter powertrain components. A fault is detected when a CI exceeds its threshold value. The effectiveness of fault detection can be judged on the basis of assessing the condition of actual components from fleet aircraft. The Bell 412 HUMS-equipped helicopter is chosen for such an evaluation. A sample of 20 aircraft included 12 aircraft with confirmed transmission and gearbox faults (detected by CIs) and eight aircraft with no known faults. The associated CI data is classified into "healthy" and "faulted" populations based on actual condition and these populations are compared against their CI thresholds to quantify the probability of false alarm and the probability of missed detection. Receiver Operator Characteristic analysis is used to optimize thresholds. Based on the results of the analysis, shortcomings in the classification method are identified for slow-moving CI trends. Recommendations for improving classification using time-dependent receiver-operator characteristic methods are put forth. Finally, lessons learned regarding OEM-operator communication are presented.

mechanical components↗

NASA NTRS2015

Investigation of Spiral Bevel Gear Condition Indicator Validation via AC-29-2C Combining Test Rig Damage Progression Data with Fielded Rotorcraft Data

This is the final of three reports published on the results of this project. In the first report, results were presented on nineteen tests performed in the NASA Glenn Spiral Bevel Gear Fatigue Test Rig on spiral bevel gear sets designed to simulate helicopter fielded failures. In the second report, fielded helicopter HUMS data from forty helicopters were processed with the same techniques that were applied to spiral bevel rig test data. Twenty of the forty helicopters experienced damage to the spiral bevel gears, while the other twenty helicopters had no known anomalies within the time frame of the datasets. In this report, results from the rig and helicopter data analysis will be compared for differences and similarities in condition indicator (CI) response. Observations and findings using sub-scale rig failure progression tests to validate helicopter gear condition indicators will be presented. In the helicopter, gear health monitoring data was measured when damage occurred and after the gear sets were replaced at two helicopter regimes. For the helicopters or tails, data was taken in the flat pitch ground 101 rotor speed (FPG101) regime. For nine tails, data was also taken at 120 knots true airspeed (120KTA) regime. In the test rig, gear sets were tested until damage initiated and progressed while gear health monitoring data and operational parameters were measured and tooth damage progression documented. For the rig tests, the gear speed was maintained at 3500RPM, a one hour run-in was performed at 4000 in-lb gear torque, than the torque was increased to 8000 in-lbs. The HUMS gear condition indicator data evaluated included Figure of Merit 4 (FM4), Root Mean Square (RMS) or Diagnostic Algorithm 1(DA1), + 3 Sideband Index (SI3) and + 1 Sideband Index (SI1). These were selected based on their sensitivity in detecting contact fatigue damage modes from analytical, experimental and historical helicopter data. For this report, the helicopter dataset was reduced to fourteen tails and the test rig data set was reduced to eight tested gear sets. The damage modes compared were separated into three cases. For case one, both the gear and pinion showed signs of contact fatigue or scuffing damage. For case two, only the pinion showed signs of contact fatigue damage or scuffing. Case three was limited to the gear tests when scuffing occurred immediately after the gear run-in. Results of this investigation highlighted the importance of understanding the complete monitored systems, for both the helicopter and test rig, before interpreting health monitoring data. Further work is required to better define these two systems that include better state awareness of the fielded systems, new sensing technologies, new experimental methods or models that quantify the effect of system design on CI response and new methods for setting thresholds that take into consideration the variance of each system.

spiral bevel gears↗

NASA NTRS2014

Investigation of Spiral Bevel Gear Condition Indicator Validation via AC-29-2C Using Fielded Rotorcraft HUMS Data

This report presents the analysis of gear condition indicator data collected on a helicopter when damage occurred in spiral bevel gears. The purpose of the data analysis was to use existing in-service helicopter HUMS flight data from faulted spiral bevel gears as a Case Study, to better understand the differences between HUMS data response in a helicopter and a component test rig, the NASA Glenn Spiral Bevel Gear Fatigue Rig. The reason spiral bevel gear sets were chosen to demonstrate differences in response between both systems was the availability of the helicopter data and the availability of a test rig that was capable of testing spiral bevel gear sets to failure. The objective of the analysis presented in this paper was to re-process helicopter HUMS data with the same analysis techniques applied to the spiral bevel rig test data. The damage modes experienced in the field were mapped to the failure modes created in the test rig. A total of forty helicopters were evaluated. Twenty helicopters, or tails, experienced damage to the spiral bevel gears in the nose gearbox. Vibration based gear condition indicators data was available before and after replacement. The other twenty tails had no known anomalies in the nose gearbox within the time frame of the datasets. These twenty tails were considered the baseline dataset. The HUMS gear condition indicators evaluated included gear condition indicators (CI) Figure of Merit 4 (FM4), Root Mean Square (RMS) or Diagnostic Algorithm 1 (DA1) and +/- 3 Sideband Index (SI3). Three additional condition indicators, not currently calculated on-board, were calculated from the archived data. These three indicators were +/- 1 Sideband Index (SI1), the DA1 of the difference signal (DiffDA1) and the peak-to-peak of the difference signal (DP2P). Results found the CI DP2P, not currently available in the on-board HUMS, performed the best, responding to varying levels of damage on thirteen of the fourteen helicopters evaluated. Two additional CIs also not in the on-board system, DiffDA1and SI1, also performed well responding to twelve and ten of the fourteen helicopters evaluated respectively. Of the three CIs currently available in the MSPU, DA1, FM4 and SI3, SI3, responded to eight, DA1 responded to six and FM4 responded to four of the fourteen helicopters evaluated. FM4, the poorest performing CI, was not as responsive to damage as the other five CIs. Conversely, when compared to the other two, it was the only CI that responded to damage on two helicopters. CI response could not be correlated to specific failure modes due to limited pictures and subjective descriptions found within the TDA. Flight regime did affect CI response to some gear faults. Due to the range of operating conditions for each regime, more studies are required to determine their sensitivity to regimes.

rotorcraft↗

NASA NTRS2014

Investigation of Spiral Bevel Gear Condition Indicator Validation Via AC-29-2C Using Damage Progression Tests

This report documents the results of spiral bevel gear rig tests performed under a NASA Space Act Agreement with the Federal Aviation Administration (FAA) to support validation and demonstration of rotorcraft Health and Usage Monitoring Systems (HUMS) for maintenance credits via FAA Advisory Circular (AC) 29-2C, Section MG-15, Airworthiness Approval of Rotorcraft (HUMS) (Ref. 1). The overarching goal of this work was to determine a method to validate condition indicators in the lab that better represent their response to faults in the field. Using existing in-service helicopter HUMS flight data from faulted spiral bevel gears as a "Case Study," to better understand the differences between both systems, and the availability of the NASA Glenn Spiral Bevel Gear Fatigue Rig, a plan was put in place to design, fabricate and test comparable gear sets with comparable failure modes within the constraints of the test rig. The research objectives of the rig tests were to evaluate the capability of detecting gear surface pitting fatigue and other generated failure modes on spiral bevel gear teeth using gear condition indicators currently used in fielded HUMS. Nineteen final design gear sets were tested. Tables were generated for each test, summarizing the failure modes observed on the gear teeth for each test during each inspection interval and color coded based on damage mode per inspection photos. Gear condition indicators (CI) Figure of Merit 4 (FM4), Root Mean Square (RMS), +/- 1 Sideband Index (SI1) and +/- 3 Sideband Index (SI3) were plotted along with rig operational parameters. Statistical tables of the means and standard deviations were calculated within inspection intervals for each CI. As testing progressed, it became clear that certain condition indicators were more sensitive to a specific component and failure mode. These tests were clustered together for further analysis. Maintenance actions during testing were also documented. Correlation coefficients were calculated between each CI, component, damage state and torque. Results found test rig and gear design, type of fault and data acquisition can affect CI performance. Results found FM4, SI1 and SI3 can be used to detect macro pitting on two more gear or pinion teeth as long as it is detected prior to progressing to other components or transitioning to another failure mode. The sensitivity of RMS to system and operational conditions limit its reliability for systems that are not maintained at steady state. Failure modes that occurred due to scuffing or fretting were challenging to detect with current gear diagnostic tools, since the damage is distributed across all the gear and pinion teeth, smearing the impacting signatures typically used to differentiate between a healthy and damaged tooth contact. This is one of three final reports published on the results of this project. In the second report, damage modes experienced in the field will be mapped to the failure modes created in the test rig. The helicopter CI data will then be re-processed with the same analysis techniques applied to spiral bevel rig test data. In the third report, results from the rig and helicopter data analysis will be correlated. Observations, findings and lessons learned using sub-scale rig failure progression tests to validate helicopter gear condition indicators will be presented.

gears↗

NASA NTRS2014

Characterization and Comparison of Vibration Transfer Paths in a Helicopter Gearbox and a Fixture Mounted Gearbox

Health monitoring of rotorcraft components, currently being performed by Health and Usage Monitoring Systems through analyses of vibration signatures of dynamic mechanical components, is very important for their safe and economic operation. HUMS analyze vibration signatures associated with faults and quantify them as condition indicators to predict component behavior. Vibration transfer paths are characterized by frequency response functions derived from the input/output relationship between applied force and dynamic response through a structure as a function of frequency. With an objective to investigate the differences in transfer paths, transfer path measurements were recorded under similar conditions in the left and right nose gearboxes of an AH-64 helicopter and in an isolated left nose gearbox in a test fixture at NASA Glenn Research Center. The test fixture enabled the application of measured torques-common during an actual operation. An impact hammer as well as commercial and lab piezo shakers, were used in conjunction with two types of commercially available accelerometers to collect the vibration response under various test conditions. The frequency response functions measured under comparable conditions of both systems were found to be consistent. Measurements made on the fixture indicated certain real-world installation and maintenance issues, such as sensor alignments, accelerometer locations and installation torques, had minimal effect. However, gear vibration transfer path dynamics appeared to be somewhat dependent on the presence of oil, and the transfer path dynamics were notably different if the force input was on the internal ring gear rather than on the external gearbox case.

structures↗

NASA NTRS2014

Data Fusion Tool for Spiral Bevel Gear Condition Indicator Data

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.

health monitoring↗

NASA NTRS2013

Investigation of Gearbox Vibration Transmission Paths on Gear Condition Indicator Performance

Helicopter health monitoring systems use vibration signatures generated from damaged components to identify transmission faults. For damaged gears, these signatures relate to changes in dynamics due to the meshing of the damaged tooth. These signatures, referred to as condition indicators (CI), can perform differently when measured on different systems, such as a component test rig, or a full-scale transmission test stand, or an aircraft. These differences can result from dissimilarities in systems design and environment under dynamic operating conditions. The static structure can also filter the response between the vibration source and the accelerometer, when the accelerometer is installed on the housing. To assess the utility of static vibration transfer paths for predicting gear CI performance, measurements were taken on the NASA Glenn Spiral Bevel Gear Fatigue Test Rig. The vibration measurements were taken to determine the effect of torque, accelerometer location and gearbox design on accelerometer response. Measurements were taken at the housing and compared while impacting the gear set near mesh. These impacts were made at gear mesh to simulate gear meshing dynamics. Data measured on a helicopter gearbox installed in a static fixture were also compared to the test rig. The behavior of the structure under static conditions was also compared to CI values calculated under dynamic conditions. Results indicate that static vibration transfer path measurements can provide some insight into spiral bevel gear CI performance by identifying structural characteristics unique to each system that can affect specific CI response.

Dempsey, Paula J.↗

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↗

NASA NTRS2013

Continued Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet

This paper details analyses of condition indicator performance for the helicopter nose gearbox within the U.S. Army's Condition-Based Maintenance Program. Ten nose gearbox data sets underwent two specific analyses. A mean condition indicator level analysis was performed where condition indicator performance was based on a 'batting average' measured before and after part replacement. Two specific condition indicators, Diagnostic Algorithm 1 and Sideband Index, were found to perform well for the data sets studied. A condition indicator versus gear wear analysis was also performed, where gear wear photographs and descriptions from Army tear-down analyses were categorized based on ANSI/AGMA 1010-E95 standards. Seven nose gearbox data sets were analyzed and correlated with condition indicators Diagnostic Algorithm 1 and Sideband Index. Both were found to be most responsive to gear wear cases of micropitting and spalling. Input pinion nose gear box condition indicators were found to be more responsive to part replacement during overhaul than their corresponding output gear nose gear box condition indicators.

Delgado, Irebert R.↗

NASA NTRS2013

Integrating Condition Indicators and Usage Parameters for Improved Spiral Bevel Gear Health Monitoring

The objective of this study was to illustrate the importance of combining Health Usage Monitoring Systems (HUMS) data with usage monitoring system data when detecting rotorcraft transmission health. Three gear sets were tested in the NASA Glenn Spiral Bevel Gear Fatigue Rig. Damage was initiated and progressed on the gear and pinion teeth. Damage progression was measured by debris generation and documented with inspection photos at varying torque values. A contact fatigue analysis was applied to the gear design indicating the effect temperature, load and reliability had on gear life. Results of this study illustrated the benefits of combining HUMS data and actual usage data to indicate progression of damage for spiral bevel gears.

Dempsey, Paula J.↗

NASA NTRS2013

Integrating Condition Indicators and Usage Parameters for Improved Spiral Bevel Gear Health Monitoring

The objective of this study was to illustrate the importance of combining Health Usage Monitoring Systems (HUMS) data with usage monitoring system data when detecting rotorcraft transmission health. Six gear sets were tested in the NASA Glenn Spiral Bevel Gear Fatigue Rig. Damage was initiated and progressed on the gear and pinion teeth. Damage progression was measured by debris generation and documented with inspection photos at varying torque values. A contact fatigue analysis was applied to the gear design indicating the effect temperature, load and reliability had on gear life. Results of this study illustrated the benefits of combining HUMS data and actual usage data to indicate progression of damage for spiral bevel gears.

Dempsey, Paula J.↗

NASA NTRS2012

Comparison of an Inductance In-Line Oil Debris Sensor and Magnetic Plug Oil Debris Sensor

The objective of this research was to compare the performance of an inductance in-line oil debris sensor and magnetic plug oil debris sensor when detecting transmission component health in the same system under the same operating conditions. Both sensors were installed in series in the NASA Glenn Spiral Bevel Gear Fatigue Rig during tests performed on 5 gear sets (pinion/gear) when different levels of damage occurred on the gear teeth. Results of this analysis found both the inductance in-line oil debris sensor and magnetic plug oil debris sensor have benefits and limitations when detecting gearbox component damage.

Dempsey, Paula J.↗

NASA NTRS2012

A Survey of Current Rotorcraft Propulsion Health Monitoring Technologies

A brief review is presented on the state-of-the-art in rotorcraft engine health monitoring technologies including summaries on current practices in the area of sensors, data acquisition, monitoring and analysis. Also, presented are guidelines for verification and validation of Health Usage Monitoring System (HUMS) and specifically for maintenance credits to extend part life. Finally, a number of new efforts in HUMS are summarized as well as lessons learned and future challenges. In particular, gaps are identified to supporting maintenance credits to extend rotorcraft engine part life. A number of data sources were consulted and include results from a survey from the HUMS community, Society of Automotive Engineers (SAE) documents, American Helicopter Society (AHS) papers, as well as references from Defence Science & Technology Organization (DSTO), Civil Aviation Authority (CAA), and Federal Aviation Administration (FAA).

Delgado, Irebert R.↗

NASA NTRS2011

Investigation of Bearing Fatigue Damage Life Prediction Using Oil Debris Monitoring

Research was performed to determine if a diagnostic tool for detecting fatigue damage of helicopter tapered roller bearings can be used to determine remaining useful life (RUL). The taper roller bearings under study were installed on the tail gearbox (TGB) output shaft of UH- 60M helicopters, removed from the helicopters and subsequently installed in a bearing spall propagation test rig. The diagnostic tool was developed and evaluated experimentally by collecting oil debris data during spall progression tests on four bearings. During each test, data from an on-line, in-line, inductance type oil debris sensor was monitored and recorded for the occurrence of pitting damage. Results from the four bearings tested indicate that measuring the debris generated when a bearing outer race begins to spall can be used to indicate bearing damage progression and remaining bearing life.

Dempsey, Paula J.↗

NASA NTRS2011

Investigation of Data Fusion Applied to Health Monitoring of Wind Turbine Drive train Components

The research described was performed on diagnostic tools used to detect damage to dynamic mechanical components in a wind turbine gearbox. Different monitoring technologies were evaluated by collecting vibration and oil debris data from tests performed on a "healthy" gearbox and a damaged gearbox in a dynamometer test stand located at the National Renewable Energy Laboratory. The damaged gearbox tested was removed from the field after experiencing component damage due to two losses of oil events and was retested under controlled conditions in the dynamometer test stand. Preliminary results indicate oil debris and vibration can be integrated to assess the health of the wind turbine gearbox.

Dempsey, Paula J.↗

NASA NTRS2011

CBM (Condition Based Maintenance)

The presentation provides an overview of Condition Based Maintenance research performed in the NASA Glenn Tribology and Mechanical Components Branch in support of the Subsonic Rotary Wing Project.

Dempsey, Paula J.↗