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A diagnostic tool was developed for detecting fatigue damage to rolling element bearings in an OH-58 main rotor transmission. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting bearing surface fatigue pitting damage. 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 tests performed in the NASA Glenn 500 hp Helicopter Transmission Test Stand. Data was collected during experiments performed in this test rig when two unanticipated bearing failures occurred. Results show that combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spiral bevel gears duplex ball bearings and spiral bevel pinion triplex ball bearings in a main rotor transmission. Dempsey, Paula J. and Lewicki, David G. and Decker, Harry J. Glenn Research Center NASA/TM-2004-213382, ARL-TR-3328, E-14890
A diagnostic tool was developed for detecting fatigue damage to rolling element bearings in an OH-58 main rotor transmission. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting bearing surface fatigue pitting damage. 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 tests performed in the NASA Glenn 500 hp Helicopter Transmission Test Stand. Data was collected during experiments performed in this test rig when two unanticipated bearing failures occurred. Results show that combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spiral bevel gears duplex ball bearings and spiral bevel pinion triplex ball bearings in a main rotor transmission. Dempsey, Paula J. and Lewicki, David G. and Decker, Harry J. Glenn Research Center NASA/TM-2004-213382, ARL-TR-3328, E-14890...
A diagnostic tool was developed for detecting fatigue damage to rolling element bearings in an OH-58 main rotor transmission. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting bearing surface fatigue pitting damage. 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 tests performed in the NASA Glenn 500 hp Helicopter Transmission Test Stand. Data was collected during experiments performed in this test rig when two unanticipated bearing failures occurred. Results show that combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spiral bevel gears duplex ball bearings and spiral bevel pinion triplex ball bearings in a main rotor transmission.
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.
The first book on Prognostics and Health Management of Electronics Recently, the field of prognostics for electronic products has received increased attention due to the potential to provide early warning of system failures, forecast maintenance as needed, and reduce life cycle costs. In response to the subject's growing interest among industry, government, and academic professionals, this book provides a road map to the current challenges and opportunities for research and development in Prognostics and Health Management (PHM). The book begins with a review of PHM and the techniques being developed to enable a prognostics approach for electronic products and systems. building on this foundation, the book then presents the state of the art in sensor systems for in-situ health and usage monitoring. Next, it discusses the various models and algorithms that can be utilized in PHM. Finally, it concludes with a discussion of the opportunities in future research. Readers can use the information in this book to: Detect and isolate faults Reduce the occurrence of No Fault Found (NFF) Provide advanced warning of system failures Enable condition-based (predictive) maintenance Obtain knowledge of load history for future design, qualification, and root cause analysis Increase system availability through an extension of maintenance cycles and/or timely repair actions Subtract life cycle costs of equipment from reduction in inspection costs, down time, and inventory Prognostics and Health Management of Electronics is an indispensable reference for electrical engineers in manufacturing, systems maintenance, and management, as well as design engineers in all areas of electronics.
System Health Management: with Aerospace Applications provides the first complete reference text for System Health Management (SHM), the set of technologies and processes used to improve system dependability. Edited by a team of engineers and consultants with SHM design, development, and research experience from NASA, industry, and academia, each heading up sections in their own areas of expertise and co-coordinating contributions from leading experts, the book collates together in one text the state-of-the-art in SHM research, technology, and applications. It has been written primarily as a reference text for practitioners, for those in related disciplines, and for graduate students in aerospace or systems engineering. There are many technologies involved in SHM and no single person can be an expert in all aspects of the discipline.System Health Management: with Aerospace Applications provides an introduction to the major technologies, issues, and references in these disparate but related SHM areas. Since SHM has evolved most rapidly in aerospace, the various applications described in this book are taken primarily from the aerospace industry. However, the theories, techniques, and technologies discussed are applicable to many engineering disciplines and application areas. Readers will find sections on the basic theories and concepts of SHM, how it is applied in the system life cycle (architecture, design, verification and validation, etc.), the most important methods used (reliability, quality assurance, diagnostics, prognostics, etc.), and how SHM is applied in operations (commercial aircraft, launch operations, logistics, etc.), to subsystems (electrical power, structures, flight controls, etc.) and to system applications (robotic spacecraft, tactical missiles, rotorcraft, etc.).
The vibration problems associated with bearing, shaft, and gear have drawn attention of much engineering work. The advancements in preventive maintenance of rotor gear transmission systems are currently being sought for the improvement to study a fault diagnosis in the machine health monitoring system. Previously, the analytical procedure is widely employed by researchers and designers of large rotating machinery to study the dynamic behavior of rotating systems. Presently, it has been developed to include different types of fault and used together with some online vibration monitoring methods. These online monitoring methods do not require to shutdown machinery and can be used as an in-flight diagnostic. However, very little work has been carried out on the fault detection under the combination effects of bearing, shaft, and gear in a transmission system.In this dissertation, both numerical simulations and experimental investigations were performed to identify different damage scenarios involving different combinations of bearing damage, residual shaft bow, and gear tooth damage. The comprehensive numerical models were developed to study the transient response of rotating machinery systems including the effects of individually defected components. In order to increase the calculating efficiency and reduce the computational effort, a modal analysis method was applied to the equations of motion and solved the overall dynamics of the system. The nonlinear bearing force is considered as the result of the contacts between the ball and the raceways using Hertzian contact deformation theory and this model was extended considering the effect of localized defect on inner race. In the case of residual shaft bow, the bow effect was introduced as the forcing function in the equations of motion. In the gear transmission system model, the gear-mesh forces result from a nonlinear periodic gear meshing stiffness. The change in gear surface profile due to wear is modelled as the shift in amplitude of the meshing stiffness. The experimental results used in this study were obtained from the high-speed ball bearing test rig. During these tests, vibration signatures due to each damaged effect: namely bearing defect, residual shaft bow, and gear damage/wear were acquired for identification and validation.The vibration signatures obtained from both numerical simulations and experiments were examined in the time domain, the frequency domain, and the joint time-frequency domain. The diagnostic technique was suitably chosen to perform signal analysis for detecting and identifying each damaged characteristic.The analytical model of gear-rotor-bearing systems was finally extended to investigate the damaged effects under combination scenarios of defects in bearing, shaft, and gear. The numerical results were also validated by those acquired from the test rig experiment. The signature analysis techniques were carefully applied on both numerical and experimental results to examine and characterize its root cause.
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. and Afjeh, Abdollah A. Glenn Research Center NASA/TM-2002-211126, NAS 1.15:211126, E-12976
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. Glenn Research Center NASA/TM-2001-210936, E-12789, NAS 1.15:210936